Patent Publication Number: US-2023160807-A1

Title: Integrated Flow Cytometry Data Quality Control

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     Pursuant to 35 U.S.C. § 119 (e), this application claims priority to the filing dates of U.S. Provisional Patent Application Ser. No. 63/282,904 filed Nov. 24, 2021 and U.S. Provisional Patent Application Ser. No. 63/320,487 filed Mar. 16, 2022, the disclosures of which applications are incorporated herein by reference in their entirety. 
    
    
     INTRODUCTION 
     Light detection can be used to characterize components of a sample (e.g., biological samples), for example when the sample is used in the diagnosis of a disease or medical condition. When a sample is irradiated, light can be scattered by the sample, transmitted through the sample as well as emitted by the sample (e.g., by fluorescence). Variations in the sample components, such as morphologies, absorptivity and the presence of fluorescent labels may cause variations in the light that is scattered, transmitted or emitted by the sample. These variations can be used for characterizing and identifying the presence of components in the sample. To quantify these variations, the light is collected and directed to the surface of a detector. 
     One technique that utilizes light detection to characterize the components in a sample is flow cytometry. Using data generated from the detected light, distributions of the components can be recorded and where desired material may be sorted. Flow cytometers work in part by measuring the quantity of different molecular analytes on or in biological cells by recording the intensity of analyte-specific fluorescent labels attached to those cells. These fluorescent labels are targeted to specific molecular analytes through the use of antibodies which bind with high specificity to their molecular antigens, which may be surface markers on a cell, intracellular proteins and organelles within the cell, or molecules excreted from the cell. The fluorescent labels used in immunofluorescence flow cytometry usually consist of two molecular components that are conjugated together: a fluorescently active molecule (typically called the “dye,” “tag,” “fluorochrome,” or “fluorophore”) and an antibody which binds specifically to certain target molecules (often called “antigens” or “markers”) on, in, or around the cell. The molecularly linked construct consisting of the fluorophore and the antibody is often called a “conjugate,” “reagent,” or “label.” By labeling cells with a mixture of conjugates (called a “panel”), where each conjugate type has both an antibody targeting a different antigen and a fluorophore having unique spectral properties (emission and excitation) when measured on the flow cytometer, many different molecular analytes can be measured on a single cell simultaneously. This is called “multicolor,” “multiparameter,” or “polychromatic” flow cytometry. The biological identity of different cell types in a sample may then be determined by examining the expression patterns of molecular analytes on each cell, as revealed by measurements of the antibody-fluorophore conjugates on the cell. 
     Flow cytometry data quality may be negatively affected by the quality of the sample or issues in data acquisition, such as clogs or bubbles in the sample core stream. This can lead to wrong conclusions derived from the data analysis. 
     SUMMARY 
     Aspects of the present disclosure include methods for integrating data quality assessment with data acquisition with a flow cytometer. Methods according to certain embodiments include detecting light with a light detection system from a sample irradiated by a light source in a flow stream, generating event data signals in response to events detected with the light detection system, binning the generated event data signals in a plurality of binning windows having overlapping data bins and assessing the plurality of binning windows for outlier events in the generated data signals. Systems having a processor with memory having instructions for practicing the subject methods are also described. Non-transitory computer readable storage medium is also provided. 
     In some embodiments, binning the generated event data signals includes binning a predetermined number of event data signals in each binning window, such as, for example, binning 500 event data signals in each binning window. In some instances, the event data signals are sequentially binned based on time of event detection by the light detection system. In certain instances, one or more event data signals are binned in two or more binning windows, such as where the two or more binning windows overlap by a predetermined time frame. In some instances, the binning windows overlap by a time frame of from 0.001 μs to 100 μs. In some embodiments, the event data signals include the fluorescence intensity of the events. In certain embodiments, the fluorescence intensity corresponds to an expression level for each event. 
     In some embodiments, a transformation is applied to each event data signal. In some instances, the transformation is one or more of a centered log-ratio transformation, an additive log-ratio transformation, an isometric log-ratio transformation. In some instances, methods include calculating a median parameter for each data bin of the overlapping data bins. In some instances, a median or mean fluorescence intensity of events for each data bin is calculated. In some instances, a median or mean expression level for events for each data bin is calculated. In some embodiments, assessing the plurality of binning windows for outlier events includes comparing the median parameter for each data bin with an outlier threshold. In some instances, the outlier threshold is a median absolute deviation (MAD) outlier threshold. In some instances, the outlier threshold is a median tolerance percentage outlier threshold. In certain instances, methods include dynamically calculating the outlier threshold, such as in real-time. For example, the outlier threshold may be dynamically adjusted in real-time in response to binning of each of the generated event data signals. 
     In some embodiments, methods include classifying one or more data bins as an outlier-containing data bin. In some instances, a data bin is classified as being an outlier-containing data bin when the median parameter is determined to be greater than the outlier threshold. In some instances, the methods further include identifying the event data signals of an outlier-containing data bin as being an outlier. In some embodiments, an alert is generated that an outlier is detected when assessing the plurality of data bins for outlier events. In some instances, methods include generating an alert that there is a change in flow rate in the flow stream of the particle analyzer. In some instances, an alert is generated that there is a clog in the particle analyzer. In some instances, the flow rate of the flow stream is measured with a flow rate sensor. In certain instances, methods include comparing the measured flow rate of the flow stream with an expected flow rate of the flow stream and reassessing the data bins classified as being an outlier-containing data bin based on the compared flow rate of the flow stream. In some instances, one or more data bins are reclassified based on the compared flow rate of the flow stream. In some instances, an alert is not generated when the measured flow rate is within a predetermined threshold of the expected flow rate, for example where the predetermined threshold is within 5% or less of the expected flow rate. In some embodiments, methods include comparing the median fluorescence intensity of the plurality of data bins with one or more parameters of the light detection system and reassessing the data bins classified as being an outlier-containing data bin based on the parameter of the light detection system, such as photodetector voltage of one or more photodetectors of the light detection system. In certain embodiments, a calibration of the particle analyzer is performed in response to a data bin being classified as being an outlier-containing data bin. In some instances, calibration of the particle analyzer includes detecting light with the light detection system from a standard composition irradiated by the light source in the flow stream, generating event data signals in response to events detected with the light detection system, binning the generated event data signals into a plurality of binning windows having overlapping data bins, assessing the generated data signals in the plurality of binning windows and determining that no outlier-containing data bins are present. 
     Aspects of the present disclosure also include systems (e.g., a particle analyzer) having a light detection system. In embodiments, light detection systems include a detector component having a photodetector configured to detect light from a sample irradiated in a flow stream and to generate event data signals in response to events detected with the light detection system and a modulator component configured to bin the generated event data signals in a plurality of binning windows comprising overlapping data bins. In embodiments, the detector component includes one or more of light scatter photodetectors, fluorescence light photodetectors and light loss photodetectors. In some instances, the detector component includes a forward scatter photodetector. In some instances, the detector component includes a side scatter photodetector. In some instances, the detector component includes a back scatter photodetector. In some instances, the detector component includes one or more fluorescence photodetectors. In some instances, the detector component includes a light loss photodetector. In some instances, the detector component includes a darkfield photodetector. In some instances, the detector component includes a brightfield photodetector. In some embodiments, the light detection system includes two or more photodetectors, such as 3 or more, such as 4 or more, such as 5 or more and including 10 or more photodetectors. In some instances, the photodetectors form a photodetector array, such as a photodetector array having 4 or more photodetectors, such as 8 or more, such as 12 or more, such as 16 or more, such as 24 or more and including a photodetector array with 48 or more photodetectors. In some embodiments, the detector component is configured to generate event data signals in 4 or more photodetector channels, such as 16 or more, such as 32 or more and including in 64 or more photodetector channels. 
     In some embodiments, the modulator component includes an integrated circuit (e.g., a field programmable gate array, FPGA) having programming for binning event data signals from two or more different photodetector channels. In some instances, the modulator component is configured to bin a predetermined number of event data signals into each binning window, such as for example binning 500 event data signals in each binning window. In some instances, the modulator component is configured to sequentially bin the generated event data signals based on time of event detection by the light detection system. In some instances, the modulator component is configured to bin one or more of the event data signals into two or more binning windows, such as where the two or more binning windows overlap by a predetermined time frame. In some instances, the binning windows overlap by a time frame of from 0.001 μs to 100 μs. In some embodiments, the event data signals include the fluorescence intensity of the events. In certain embodiments, the fluorescence intensity corresponds to an expression level for each event. In some instances, the modulator component is configured to apply a transformation to each event data signal. In some instances, the transformation is one or more of a centered log-ratio transformation, an additive log-ratio transformation, an isometric log-ratio transformation. 
     In embodiments, systems include a processor having memory operably coupled to the processor where the memory includes instructions stored thereon, which when executed by the processor, cause the processor to assess the plurality of binning windows for outlier events in the generated data signals. In some embodiment, the memory includes instructions to a median or mean fluorescence intensity of events for each data bin. In some embodiments, the memory includes instructions to calculate a median or mean expression level of events for each data bin. In some embodiments, the memory includes instructions to assess the plurality of binning windows for outlier events by comparing the median parameter for each data bin with an outlier threshold. In some instances, the outlier threshold is a median absolute deviation (MAD) outlier threshold. In some instances, the outlier threshold is a median tolerance percentage outlier threshold. In certain instances, the memory includes instructions for dynamically calculating the outlier threshold in real-time, such as to dynamically adjust the outlier threshold in real-time in response to binning of the generated event data signals. In some embodiments, the memory includes instructions for classifying a data bin as an outlier-containing data bin when the median parameter is determined to be greater than the predetermined outlier threshold. In some instances, the memory includes instructions to identify the event data signals of each outlier-containing data bin as being an outlier. In certain instances, the memory includes instructions to generate an alert that an outlier is detected when assessing the plurality of data bins for outlier events. In some embodiments, the memory includes instructions to generate an alert that there is a change in flow rate in the flow stream of the particle analyzer. In some embodiments, the memory includes instructions to generate an alert that there is a clog in the particle analyzer. 
     In certain embodiments, systems of interest include a flow rate sensor to measure the flow rate of the flow stream. In some embodiments, the memory includes instructions stored thereon, which when executed by the processor, cause the processor to compare the measured flow rate of the flow stream with an expected flow rate of the flow stream and reassess the data bins classified as being an outlier-containing data bin based on the compared flow rate of the flow stream. In some instances, the memory includes instructions to reclassify the data bin based on the compared flow rate of the flow stream. In some embodiments, the memory includes instructions to not generate an alert when the measured flow rate is within a predetermined threshold of the expected flow rate, such as where the predetermined threshold is within 5% or less of the expected flow rate. 
     In some embodiments, the memory includes instructions to compare the median fluorescence intensity of the plurality of data bins with one or more parameters of the light detection system and reassess the data bins classified as being an outlier-containing data bin based on the parameter of the light detection system. In some embodiments, the system includes memory with instructions to perform a calibration of the particle analyzer in response to one or more data bins being classified as outlier-containing data bins. In some instances, instructions for calibration of the particle analyzer include instructions for detect light with the light detection system from a standard composition irradiated by the light source in the flow stream, instructions to generate event data signals in response to events detected with the light detection system, instructions to bin the generated event data signals into a plurality of binning windows having overlapping data bins, instructions to assess the generated data signals in the plurality of binning windows and instructions to determine that no outlier-containing data bins are present. 
     Aspects of the present disclosure also include non-transitory computer readable storage medium for practicing the subject methods. Non-transitory computer readable storage medium according to certain embodiments includes instructions having algorithm for detecting light with a light detection system from a sample irradiated by a light source in a flow stream, algorithm for generating event data signals in response to events detected with the light detection system, algorithm for binning the generated event data signals in a plurality of binning windows comprising overlapping data bins and 
     algorithm for assessing the plurality of binning windows for outlier events in the generated data signals. In some embodiments, the non-transitory computer readable storage medium includes algorithm for binning a predetermined number of event data signals in each window. In some embodiments, non-transitory computer readable storage medium includes algorithm for sequentially binning the event data signals based on time of event detection by the light detection system. In some embodiments, the non-transitory computer readable storage medium includes algorithm for binning one or more event data signals into two or more binning windows, such as where the two or more binning windows overlap by a predetermined time frame. For example, the binning windows may overlap by a time frame of from 0.001 μs to 100 μs. 
     In some embodiments, the non-transitory computer readable storage medium includes algorithm for applying a transformation to each event data signal. In certain embodiments, the transformation is one or more of a centered log-ratio transformation, an additive log-ratio transformation, an isometric log-ratio transformation or a combination thereof. In some embodiments, the non-transitory computer readable storage medium includes algorithm for calculating a median parameter for each data bin of the overlapping data bins. In some embodiments, the non-transitory computer readable storage medium includes algorithm for calculating a median or mean fluorescence intensity of events for each data bin. In some embodiments, the non-transitory computer readable storage medium includes algorithm for calculating a median or mean expression level of events for each data bin. 
     In some embodiments, the non-transitory computer readable storage medium includes algorithm for assessing the plurality of binning windows for outlier events by comparing the median parameter for each data bin with an outlier threshold. In some instances, the outlier threshold is a median absolute deviation (MAD) outlier threshold. In some instances, the outlier threshold is a median tolerance percentage outlier threshold. In some embodiments, the non-transitory computer readable storage medium includes algorithm for dynamically calculating the outlier threshold in real-time, such as for dynamically adjusting the outlier threshold in real-time in response to binning of the generated event data signals. In some embodiments, the non-transitory computer readable storage medium includes algorithm for classifying a data bin as an outlier-containing data bin when the median parameter is determined to be greater than the predetermined outlier threshold. In some instances, the non-transitory computer readable storage medium includes algorithm for identifying the event data signals of each outlier-containing data bin as being an outlier. 
     In some embodiments, the non-transitory computer readable storage medium comprises algorithm for generating an alert that an outlier is detected when assessing the plurality of data bins for outlier events. In some instances, the non-transitory computer readable storage medium includes algorithm for generating an alert that there is a change in flow rate in the flow stream of the particle analyzer. In some instances, the non-transitory computer readable storage medium includes algorithm for generating an alert that there is a clog in the particle analyzer. 
     In certain instances, the non-transitory computer readable storage medium includes algorithm for measuring flow rate of the flow stream with a flow rate sensor. In some instances, the non-transitory computer readable storage medium includes algorithm for comparing the measured flow rate of the flow stream with an expected flow rate of the flow stream and algorithm for reassessing the data bins classified as being an outlier-containing data bin based on the compared flow rate of the flow stream. In some embodiments, the non-transitory computer readable storage medium includes algorithm for reclassifying the data bin based on the compared flow rate of the flow stream. In some embodiments, the non-transitory computer readable storage medium includes algorithm for not generating an alert when the measured flow rate is within a predetermined threshold of the expected flow rate, such as where the predetermined threshold is within 5% or less of the expected flow rate. In some embodiments, the non-transitory computer readable storage medium includes algorithm for comparing the median fluorescence intensity of the plurality of data bins with one or more parameters of the light detection system and algorithm for reassessing the data bins classified as being an outlier-containing data bin based on the parameter of the light detection system (e.g., photodetector voltage of one or more of the photodetectors of the light detection system). In certain embodiments, the non-transitory computer readable storage medium includes algorithm for performing a calibration of the particle analyzer in response to one or more data bins being classified as outlier-containing data bins. In certain instances, algorithm for calibration of the particle analyzer include algorithm for detecting light with the light detection system from a standard composition irradiated by the light source in the flow stream, algorithm for generating event data signals in response to events detected with the light detection system algorithm for binning the generated event data signals into a plurality of binning windows having overlapping data bins, algorithm for assessing the generated data signals in the plurality of binning windows and algorithm for determining that no outlier-containing data bins are present. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The invention may be best understood from the following detailed description when read in conjunction with the accompanying drawings. Included in the drawings are the following figures: 
         FIG.  1    depicts binning generated event data signals in a plurality of binning windows having overlapping data bins according to certain embodiments. 
         FIG.  2    depicts assessment of data signals from a particle analyzer according to certain embodiments. 
         FIG.  3    depicts a flow chart for dynamic real-time assessment of data signals from a particle analyzer according to certain embodiments. 
         FIG.  4 A  depicts a functional block diagram of a particle analysis system according to certain embodiments.  FIG.  4 B  depicts a flow cytometer according to certain embodiments. 
         FIG.  5    depicts a functional block diagram for one example of a particle analyzer control system according to certain embodiments. 
         FIG.  6 A  depicts a schematic drawing of a particle sorter system according to certain embodiments. 
         FIG.  6 B  depicts a schematic drawing of a particle sorter system according to certain embodiments. 
         FIG.  7    depicts a block diagram of a computing system according to certain embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Aspects of the present disclosure include methods for integrating data quality assessment with data acquisition with a flow cytometer. Methods according to certain embodiments include detecting light with a light detection system from a sample irradiated by a light source in a flow stream, generating event data signals in response to events detected with the light detection system, binning the generated event data signals in a plurality of binning windows having overlapping data bins and assessing the plurality of binning windows for outlier events in the generated data signals. Systems having a processor with memory having instructions for practicing the subject methods are also described. Non-transitory computer readable storage medium is also provided. 
     Before the present invention is described in greater detail, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims. 
     Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention. 
     Certain ranges are presented herein with numerical values being preceded by the term “about.” The term “about” is used herein to provide literal support for the exact number that it precedes, as well as a number that is near to or approximately the number that the term precedes. In determining whether a number is near to or approximately a specifically recited number, the near or approximating unrecited number may be a number which, in the context in which it is presented, provides the substantial equivalent of the specifically recited number. 
     Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative illustrative methods and materials are now described. 
     All publications and patents cited in this specification are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed. 
     It is noted that, as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely,” “only” and the like in connection with the recitation of claim elements, or use of a “negative” limitation. 
     As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible. 
     While the apparatus and method has or will be described for the sake of grammatical fluidity with functional explanations, it is to be expressly understood that the claims, unless expressly formulated under 35 U.S.C. § 112, are not to be construed as necessarily limited in any way by the construction of “means” or “steps” limitations, but are to be accorded the full scope of the meaning and equivalents of the definition provided by the claims under the judicial doctrine of equivalents, and in the case where the claims are expressly formulated under 35 U.S.C. § 112 are to be accorded full statutory equivalents under 35 U.S.C. § 112. 
     As summarized above, the present disclosure provides methods for integrating data quality assessment with data acquisition with a flow cytometer, such as where data acquisition can be dynamically adjusted in response to the data quality assessment. In further describing embodiments of the disclosure, methods for detecting light with a light detection system, generating event data signals in response to events detected with the light detection system, binning the generated event data signals in a plurality of binning windows having overlapping data bins and assessing the plurality of binning windows for outlier events is first described in greater detail. Next, systems that include a light detection system having a detector component and a modulator component configured to bin event data signals in a plurality of binning windows are provided. Non-transitory computer readable storage medium are also described. 
     Methods for Integrating Data Quality Assessment with Data Acquisition in A Particle Analyzer 
     Aspects of the present disclosure include methods for dynamic data quality assessment, such as in conjunction with data acquisition in a particle analyzer (e.g., a flow cytometer as described in greater detail below). In some embodiments, event data signal assessment as described herein provides for increased precision in data collection by a light detection system, such as where spurious data signals, event data signal irregularities (e.g., based on flow rate or voltage parameters of the light detection system) or photodetector noise is more efficiently detected and in certain cases excluded. In some instances, the methods described herein provide for new options for using the particle analyzer, such as to allow for pausing data acquisition, alerting a user during acquisition, performing a cleaning or calibration process and resuming data acquisition once it is determined that outliers (as described in greater detail below) are removed from the acquired event data signals. In certain embodiments, the methods described herein provide for generating alerts to a user due to a malfunction or an unexpected change in parameter of the particle analyzer, such as the flow rate of the flow stream due to a clog or a change in the voltage or detector gain of one or more photodetectors of the light detection system. In certain embodiments, integrating event data signal assessment with data acquisition provides for predictive detection of parameter changes in one or more components of the particle analyzer, such as in the flow stream, light irradiation from the light source or light detection by one or more photodetectors. As described below, in certain instances a calibration protocol may be implemented either before or in response to acquisition of one or more event data signals. 
     In embodiments, light from particles in a sample irradiated by a light source in a flow stream is conveyed to a light detection system (e.g., a light detection system having a detector component that includes a photodetector configured to detect light and generate data signals in a plurality of photodetector channels and a modulator component configured to bin the generated event data signals in a plurality of binning windows, as described in greater detail below). Light may be measured by the photodetector at one or more wavelengths, such as at 2 or more wavelengths, such as at 5 or more different wavelengths, such as at 10 or more different wavelengths, such as at 25 or more different wavelengths, such as at 50 or more different wavelengths, such as at 100 or more different wavelengths, such as at 200 or more different wavelengths, such as at 300 or more different wavelengths and including measuring light from particles in the flow stream at 400 or more different wavelengths. In certain instances, light from the sample is spectrally separated into 2 or more spectral ranges and each spectral range is detected in one or more photodetector channels, such as 3 or more spectral ranges, such as 4 or more, such as 8 or more, such as 16 or more, such as 32 or more, such as 64 or more, such as 128 or more, such as 256 or more and including 512 or more different spectral ranges. Light may be measured continuously or in discrete intervals. In some instances, detectors of interest are configured to take measurements of the light continuously. In other instances, detectors of interest are configured to take measurements in discrete intervals, such as measuring light every 0.001 millisecond, every 0.01 millisecond, every 0.1 millisecond, every 1 millisecond, every 10 milliseconds, every 100 milliseconds and including every 1000 milliseconds, or some other interval. 
     Measurements of the light from the particles in the sample may be taken one or more times during each discrete time interval, such as 2 or more times, such as 3 or more times, such as 5 or more times and including 10 or more times. In certain embodiments, the light from the light source is measured by the photodetector 2 or more times, with the data in certain instances being averaged. 
     In some instances, light detected from particles in the sample is scattered light. In some cases, the scattered light is forward scattered light. In some cases, the scattered light is backscattered light. In some cases, the scattered light is side scattered light. In some instances, light conveyed from the irradiated particle is transmitted light. In certain embodiments, light detected from each particle is emitted light, such as particle luminescence (i.e., fluorescence or phosphorescence). In these embodiments, each particle may include one or more fluorophores which emits fluorescence in response to irradiation by the two or more light sources. For example, each particle may include 2 or more fluorophores, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more and including 10 or more fluorophores. In some instances, each particle includes a fluorophore which emits fluorescence in response to irradiation by the light source. In some embodiments, the event data signals include the fluorescence intensity of the events. In certain embodiments, the fluorescence intensity corresponds to an expression level for each event. 
     In some embodiments, fluorophores of interest may include, but are not limited, to dyes suitable for use in analytical applications (e.g., flow cytometry, imaging, etc.), such as an acridine dye, anthraquinone dyes, arylmethane dyes, diarylmethane dyes (e.g., diphenyl methane dyes), chlorophyll containing dyes, triarylmethane dyes (e.g., triphenylmethane dyes), azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinon-imine dyes, azine dyes, eurhodin dyes, safranin dyes, indamins, indophenol dyes, fluorine dyes, oxazine dye, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronin dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, as well as dyes combining two or more of the aforementioned dyes (e.g., in tandem), polymeric dyes having one or more monomeric dye units and mixtures of two or more of the aforementioned dyes thereof. A large number of dyes are commercially available from a variety of sources, such as, for example, Molecular Probes (Eugene, Oreg.), Dyomics GmbH (Jena, Germany), Sigma-Aldrich (St. Louis, Mo.), Sirigen, Inc. (Santa Barbara, Calif.), Becton Dickinson (BD) and Company (Franklin Lakes, N.J.) and Exciton (Dayton, Ohio). For example, the fluorophore may include 4-acetamido-4′-isothiocyanatostilbene-2,2′disulfonic acid; acridine and derivatives such as acridine, acridine orange, acridine yellow, acridine red, and acridine isothiocyanate; allophycocyanin (APC), phycoerythrin (PE), peridinin-chlorophyll protein, 5-(2′-aminoethyl)aminonaphthalene-1-sulfonic acid (EDANS); 4-amino-N-[3-vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate (Lucifer Yellow VS); N-(4-anilino-1-naphthyl)maleimide; anthranilamide; Brilliant Yellow; coumarin and derivatives such as coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4-trifluoromethylcouluarin (Coumaran 151); cyanine and derivatives such as cyanosine, Cy3, Cy3.5, Cy5, Cy5.5, and Cy7; 4′,6-diaminidino-2-phenylindole (DAPI); 5′, 5″-dibromopyrogallol-sulfonephthalein (Bromopyrogallol Red); 7-diethylamino-3-(4′-isothiocyanatophenyl)-4-methylcoumarin; diethylaminocoumarin; diethylenetriamine pentaacetate; 4,4′-diisothiocyanatodihydro-stilbene-2,2′-disulfonic acid; 4,4′-diisothiocyanatostilbene-2,2′-disulfonic acid; 5-[dimethylamino]naphthalene sulfonyl chloride (DNS, dansyl chloride); 4-(4′-dimethylaminophenylazo)benzoic acid (DABCYL); 4-dimethylaminophenylazophenyl-4′-isothiocyanate (DABITC); eosin and derivatives such as eosin and eosin isothiocyanate; erythrosin and derivatives such as erythrosin B and erythrosin isothiocyanate; ethidium; fluorescein and derivatives such as 5-carboxyfluorescein (FAM), 5-(4,6-dichlorotriazin-2-yl)aminofluorescein (DTAF), 2′7′-dimethoxy-4′5′-dichloro-6-carboxyfluorescein (JOE), fluorescein isothiocyanate (FITC), fluorescein chlorotriazinyl, naphthofluorescein, and QFITC (XRITC); fluorescamine; IR144; IR1446; Green Fluorescent Protein (GFP); Reef Coral Fluorescent Protein (RCFP); Lissamine™; Lissamine rhodamine, Lucifer yellow; Malachite Green isothiocyanate; 4-methylumbelliferone; ortho cresolphthalein; nitrotyrosine; pararosaniline; Nile Red; Oregon Green; Phenol Red; B-phycoerythrin; o-phthaldialdehyde; pyrene and derivatives such as pyrene, pyrene butyrate and succinimidyl 1-pyrene butyrate; Reactive Red 4 (Cibacron™ Brilliant Red 3B-A); rhodamine and derivatives such as 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), 4,7-dichlororhodamine lissamine, rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulforhodamine B, sulforhodamine 101, sulfonyl chloride derivative of sulforhodamine 101 (Texas Red), N,N,N′,N′-tetramethyl-6-carboxyrhodamine (TAMRA), tetramethyl rhodamine, and tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid and terbium chelate derivatives; xanthene; dye-conjugated polymers (i.e., polymer-attached dyes) such as fluorescein isothiocyanate-dextran as well as dyes combining two or more dyes (e.g., tandem dyes or protein complex tandem dyes) such as phycoerythrin (PE) tandem dyes or allophycocyanin (APC) tandem dyes, for example phycoerythrin-CF594 (PE-CF594) tandem, phycoerythrin-cyanine 5 tandem (PE-Cy5), phycoerythrin-cyanine 5.5 tandem (PE-Cy5.5), phycoerythrin-cyanine 7 tandem (PE-Cy7), allophycocyanin-R700 tandem (APC-R700), allophycocyanin-cyanine 7 (APC-Cy7), polymeric dyes having one or more monomeric dye units and mixtures of two or more of the aforementioned dyes or combinations thereof. 
     In some instances, the fluorophore is polymeric dye. In some instances of the method, the polymeric dye includes a conjugated polymer. Conjugated polymers (CPs) are characterized by a delocalized electronic structure which includes a backbone of alternating unsaturated bonds (e.g., double and/or triple bonds) and saturated (e.g., single bonds) bonds, where π-electrons can move from one bond to the other. As such, the conjugated backbone may impart an extended linear structure on the polymeric dye, with limited bond angles between repeat units of the polymer. For example, proteins and nucleic acids, although also polymeric, in some cases do not form extended-rod structures but rather fold into higher-order three-dimensional shapes. In addition, CPs may form “rigid-rod” polymer backbones and experience a limited twist (e.g., torsion) angle between monomer repeat units along the polymer backbone chain. In some instances, the polymeric dye includes a CP that has a rigid rod structure. The structural characteristics of the polymeric dyes can have an effect on the fluorescence properties of the molecules. 
     Polymeric dyes of interest include, but are not limited to, those dyes described by Gaylord et al. in U.S. Publication Nos. 20040142344, 20080293164, 20080064042, 20100136702, 20110256549, 20110257374, 20120028828, 20120252986, 20130190193, 20160264737, 20160266131, 20180231530, 20180009990, 20180009989, and 20180163054, the disclosures of which are herein incorporated by reference in their entirety; and Gaylord et al., J. Am. Chem. Soc., 2001, 123 (26), pp 6417-6418; Feng et al., Chem. Soc. Rev., 2010, 39, 2411-2419; and Traina et al., J. Am. Chem. Soc., 2011, 133 (32), pp 12600-12607, the disclosures of which are herein incorporated by reference in their entirety. 
     The polymeric dye may have one or more desirable spectroscopic properties, such as a particular absorption maximum wavelength, a particular emission maximum wavelength, extinction coefficient, quantum yield, and the like (see e.g., Chattopadhyay et al., “Brilliant violet fluorophores: A new class of ultrabright fluorescent compounds for immunofluorescence experiments.” Cytometry Part A, 81A(6), 456-466, 2012). In some embodiments, the polymeric dye has an absorption curve between 280 nm and 475 nm. In certain embodiments, the polymeric dye has an absorption maximum (excitation maximum) in the range 280 nm and 475 nm. In some embodiments, the polymeric dye absorbs incident light having a wavelength in the range between 280 nm and 475 nm. In some embodiments, the polymeric dye has an emission maximum wavelength ranging from 400 nm to 850 nm, such as 415 nm to 800 nm, where specific examples of emission maxima of interest include, but are not limited to: 421 nm, 510 nm, 570 nm, 602 nm, 650 nm, 711 nm and 786 nm. In some instances, the polymeric dye has an emission maximum wavelength in a range selected from the group consisting of 410 nm to 430 nm, 500 nm to 520 nm, 560 nm to 580 nm, 590 nm to 610 nm, 640 nm to 660 nm, 700 nm to 720 nm, and 775 nm to 795 nm. In certain embodiments, the polymeric dye has an emission maximum wavelength of 421 nm. In some instances, the polymeric dye has an emission maximum wavelength of 510 nm. In some cases, the polymeric dye has an emission maximum wavelength of 570 nm. In certain embodiments, the polymeric dye has an emission maximum wavelength of 602 nm. In some instances, the polymeric dye has an emission maximum wavelength of 650 nm. In certain cases, the polymeric dye has an emission maximum wavelength of 711 nm. In some embodiments, the polymeric dye has an emission maximum wavelength of 786 nm. In certain instances, the polymeric dye has an emission maximum wavelength of 421 nm±5 nm. In some embodiments, the polymeric dye has an emission maximum wavelength of 510 nm±5 nm. In certain instances, the polymeric dye has an emission maximum wavelength of 570 nm±5 nm. In some instances, the polymeric dye has an emission maximum wavelength of 602 nm±5 nm. In some embodiments, the polymeric dye has an emission maximum wavelength of 650 nm±5 nm. In certain instances, the polymeric dye has an emission maximum wavelength of 711 nm±5 nm. In some cases, the polymeric dye has an emission maximum wavelength of 786 nm±5 nm. In certain embodiments, the polymeric dye has an emission maximum selected from the group consisting of 421 nm, 510 nm, 570 nm, 602 nm, 650 nm, 711 nm and 786 nm. 
     Specific polymeric dyes that may be employed include, but are not limited to, BD Horizon Brilliant™ Dyes, such as BD Horizon Brilliant™ Violet Dyes (e.g., BV421, BV480, BV510, BV570, BV605, BV650, BV711, BV786, BV829); BD Horizon Brilliant™ Ultraviolet Dyes (e.g., BUV395, BUV496, BUV563, BUV615, BUV661, BUV737, BUV805); and BD Horizon Brilliant™ Blue Dyes (e.g., BB515, BB630, BB660, BB700, BB755, BB790) (BD Biosciences, San Jose, Calif.). 
     In some embodiments, light from particles in the sample is detected in two or more photodetector channels, such as 4 or more, such as 8 or more, such as 12 or more, such as 16 or more, such as 20 or more, such as 24 or more, such as 28 or more, such as 32 or more, such as 36 or more, such as 40 or more, such as 44 or more, such as 48 or more, such as 52 or more, such as 56 or more, such as 60 or more and including in 64 or more photodetector channels. Depending on the spectral width of each predetermined spectral range of light and the number of photodetector channels being employed, light from one or more spectral ranges is detected in each photodetector channel, such as 2 or more spectral ranges, such as 3 or more and including detecting 4 or more different spectral ranges in each photodetector channel. In embodiments, one or more of event data signals are generated in each of the photodetector channels in response to the detected light, such as 2 or more event data signals, such as 4 or more, such as 8 or more, such as 16 or more and including generating 32 or more event data signals in each photodetector channel in response to the detected light. 
     In practicing the subject methods, event data signals generated in response to events detected in the light detection system are binned in a plurality of binning windows having overlapping data bins. The term binning data signals is used herein in its conventional sense to refer to the discrete grouping or bucketing of raw event data signal into a predetermined interval (i.e., a bin) or combining event data signals from different photodetector channels as a form of quantization of the data signals. In some embodiments, the event data signals that are binned include fluorescence intensity of the light detected in response to events. In some embodiments, the event data signals that are binned include the fluorescence expression of the events detected by the light detection system. In certain embodiments, the event data signals that are binned include time values of the events detected by the light detection system. 
     In certain embodiments, event data signals from two or more different photodetector channels are binned. Methods include binning together data signals from two or more different photodetector channels, such as 3 or more, such as 4 or more and including from 8 more different photodetector channels. In some instances, methods include binning data signals from non-adjacent photodetectors. In other instances, methods include binning data signals from adjacent photodetectors. In certain instances, methods include horizontal binning of adjacent photodetectors. In certain embodiments, methods include dynamically binning data signals from two or more different photodetector channels in real time. 
     In some embodiments, each binning window includes a predetermined time frame of data acquisition (i.e., generated event data signals) by the particle analyzer. In some instances, each binning window includes a data acquisition time frame of 0.001 μs or more, such as 0.005 μs or more, such as 0.01 μs or more, such as 0.05 μs or more, such as 0.1 μs or more, such as 0.5 μs or more, such as 1 μs or more, such as 5 μs or more, such as 10 μs or more, such as 50 μs or more, such as 100 μs or more, such as 500 μs or more and including a data acquisition time frame of 1000 μs or more. In certain instances, each binning window includes a data acquisition time frame of from 0.001 μs to 5000 μs, such as from 0.005 μs to 4500 μs, such as 0.01 μs to 4000 μs, such as 0.05 μs to 3500 μs, such as from 0.1 μs to 3000 μs, such as from 0.5 μs to 2500 μs, such as from 1 μs to 2000 μs, such as from 5 μs to 1000 μs, such as from 10 μs to 500 μs and including a data acquisition time frame of from 50 μs to 100 μs. Each binning window may be the same duration or may be different. 
     In some embodiments, binning the generated event data signals includes binning a predetermined number of event data signals in each binning window, such as where 5 or more event data signals are binned in each binning window, such as 10 event data signals or more, such as 25 event data signals or more, such as 50 event data signals or more, such as 75 event data signals or more, such as 100 event data signals or more, such as 125 event data signals or more, such as 150 event data signals or more, such as 175 event data signals or more, such as 200 event data signals or more, such as 300 event data signals or more, such as 400 event data signals or more, such as 500 event data signals or more, such as 600 event data signals or more, such as 700 event data signals or more, such as 800 event data signals or more, such as 900 event data signals or more and including binning 1000 event data signals or more in each binning window. 
     In some embodiments, two or more of the binning windows at least partially overlap, such as where one or more event data signals are binned in more than one binning window. For example, 3 or more binning windows may at least partially overlap, such as 4 or more and including where 5 or more of the binning windows at least partially overlap. In certain instances, binning windows according to methods of the present disclosure form a sliding window configuration where each binning window at least partially overlaps with the previous binning window and the next binning window. For example, in a sliding window binning configuration having 3 different binning windows, a second binning window at least partially overlaps with the first binning window (at the beginning of the second binning window) and at least partially overlaps with the third binning window (at the end of the second binning window). In a sliding window binning configuration, each generated event data signal is binned in at least one of the binning windows such that no event data signal is missed from the data acquisition.  FIG.  1    depicts binning generated event data signals in a plurality of binning windows having overlapping data bins according to certain embodiments. In discrete binning, each predetermined set of generated event data signals (each section of data acquisition window  101 , e.g., 500 or more data signals) is binned. As shown in  FIG.  1   , the end of each binning window is the beginning of the subsequent binning window. Each binning window may be the same length or a different length. In a sliding-window binning configuration, each binning window at least partially overlaps with the previous binning window and the next binning window. As shown in  FIG.  1   , one or more sections of data acquisition window  102  is binned in each binning window. In embodiments of the present disclosure, one or more of the sections of data acquisition window  102  is binned in two or more different binning windows. 
     In certain instances, one or more of the generated event data signals are binned in two or more of the binning windows (i.e., event data signals generated where the binning windows overlap). In some instances, the binning windows overlap by a time frame of 0.001 μs or more, such as 0.005 μs or more, such as 0.01 μs or more, such as 0.05 μs or more, such as 0.1 μs or more, such as 0.5 μs or more, such as 1 μs or more, such as 5 μs or more, such as 10 μs or more, such as 50 μs or more, such as 100 μs or more, such as 500 μs or more and including where the binning windows overlap by a time frame of 1000 μs or more. In certain instances, the binning windows overlap by a time frame of from 0.001 μs to 5000 μs, such as from 0.005 μs to 4500 μs, such as 0.01 μs to 4000 μs, such as 0.05 μs to 3500 μs, such as from 0.1 μs to 3000 μs, such as from 0.5 μs to 2500 μs, such as from 1 μs to 2000 μs, such as from 5 μs to 1000 μs, such as from 10 μs to 500 μs and including from 50 μs to 100 μs. 
     In certain instances, each binning window is adjusted in response to a change in the rate of data acquisition by the particle analyzer, such as where the flow rate of the flow stream is changed. In one example, the size of one or more binning windows may be increased in response to a change in the flow rate of the flow stream (e.g., where the flow rate is increased), such as by 5% or more, such as by 10% or more, such as by 25% or more and including by 50% or more. In certain instances, the increase in the size of each binning window is equivalent to the change in the flow rate of the flow stream (i.e., the size of the binning window is increased by 10% when the flow rate of the flow stream is increased by 10%). In other instances, the increase in the size of each binning window is 5% or more of the change to the flow rate of the flow stream, such as 10% or more, such as 20% or more, such as 30% or more, such as 40% or more, such as 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more and including 95% or more. In a second example, the size of one or more binning windows may be decreased in response to a change in the flow rate of the flow stream (e.g., where the flow rate is decreased), such as by 5% or more, such as by 10% or more, such as by 25% or more and including by 50% or more. In certain instances, the decrease in the size of each binning window is equivalent to the change in the flow rate of the flow stream (i.e., the size of the binning window is decreased by 10% when the flow rate of the flow stream is decreased by 10%). In other instances, the decrease in the size of each binning window is 5% or more of the change to the flow rate of the flow stream, such as 10% or more, such as 20% or more, such as 30% or more, such as 40% or more, such as 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more and including 95% or more. In certain instances, one or more binning windows are automatically adjusted in response to a change in the rate of data acquisition by the particle analyzer. 
     In some embodiments, a transformation is applied to each event data signal. In some instances, the transformation is one or more of a centered log-ratio transformation, an additive log-ratio transformation, an isometric log-ratio transformation. In some instances, methods include calculating a median parameter for each data bin of the overlapping data bins. In some instances, methods include calculating a mean parameter for each data bin of the overlapping data bins. In some instances, a median or mean fluorescence intensity of events for each data bin is calculated. In some instances, methods include calculating a median or mean expression level for events for each data bin. 
     In some embodiments, assessing the plurality of binning windows for outlier events includes comparing the median parameter for each data bin with an outlier threshold. In some instances, the outlier threshold is a median absolute deviation (MAD) outlier threshold. In some instances, the outlier threshold is a median tolerance percentage outlier threshold. In certain instances, methods include dynamically calculating the outlier threshold, such as in real-time. For example, the outlier threshold may be dynamically adjusted in real-time in response to binning of each of the generated event data signals. In certain embodiments, methods include implementing a dynamic algorithm, such as a machine learning algorithm for calculating or adjusting a calculated outlier threshold. In some instances, the outlier threshold may be calculated by the machine learning algorithm. In certain instances, a change to the outlier threshold is determined by the machine learning algorithm. For example, in some embodiments the change to the outlier threshold may be sufficient to increase the number of event data signals which are above or below the median absolute deviation or the median tolerance percentage. In other embodiments the change to the outlier threshold is sufficient to decrease the number of event data signals which are above or below the median absolute deviation or the median tolerance percentage. The term “machine learning” is used herein in its conventional sense to refer to adjustments to the outlier threshold (e.g., a median absolute deviation outlier threshold or median tolerance percentage outlier threshold) by computational methods that ascertain and implement information directly from data without relying on a predetermined equation as a model. In certain embodiments, machine learning includes learning algorithms which find patterns in data signals (e.g., from a plurality of event data signals from the irradiated sample). In these embodiments, the learning algorithm is configured to generate better and more accurate decisions and predictions as a function of the number of data signals (i.e., the learning algorithm becomes more robust as the number of characterized event data signals from the sample increases). Machine-learning protocols of interest may include, but are not limited to artificial neural networks, decision tree learning, decision tree predictive modeling, support vector machines, Bayesian networks, dynamic Bayesian networks, genetic algorithms among other machine learning protocols. 
     In some embodiments, methods include classifying one or more data bins in the plurality of binning windows as an outlier-containing data bin, such as where 2 or more data bins are classified as being an outlier-containing data bin, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more and including 10 or more. In some instances, a data bin is classified as being an outlier-containing data bin when a median (or mean) parameter is determined to be greater than the outlier threshold. In certain instances, the parameter is fluorescence intensity of events for each data bin and the data bin is classified as being an outlier-containing data bin when the median or mean fluorescence intensity of event data signals is greater than the calculated outlier threshold (e.g., a median absolute deviation outlier threshold or median tolerance percentage outlier threshold). In other instances, the parameter is expression level for events for each data bin and the data bin is classified as being an outlier-containing data bin when the median or mean expression level for events is greater than the calculated outlier threshold. In some embodiments, a data bin is classified as being an outlier-containing data bin when a median (or mean) parameter is determined to be within (i.e., greater than or less than) a predetermined percentage of the outlier threshold, such as where the median parameter (e.g., fluorescence intensity of the events or expression level) is within 10% of the outlier threshold, such as within 9% of the outlier threshold, such as within 8% of the outlier threshold, such as within 7% of the outlier threshold, such as within 6% of the outlier threshold, such as within 5% of the outlier threshold, such as within 4% of the outlier threshold, such as within 3% of the outlier threshold, such as within 2% of the outlier threshold and including where the median parameter is within 1% of the outlier threshold. In some instances, the methods further include identifying the event data signals of an outlier-containing data bin as being an outlier. 
     In certain embodiments, one or more outliers in the event data signals is identified using a median absolute deviation algorithm having the steps of:
         1. Slice input data D into distinct or overlapping bins of equal or close to equal number of events, use preferred bin size B but allow minor adjustments to improve homogeneity of the bin size over D; if overlapping bins are used then those are all exactly the size of B   2. Calculate relevant characteristics for these bins, such as time or median expression of selected parameters, store in xr[ ]   3. Rescale result xr as x as appropriate (e.g., start of acquisition, last bin being shorter than other bins, user adjusting speed during acquisition)   4. Calculate xm as median(x)   5. Center x around xm and store as xc[ ]   6. Calculate absxc[ ]   7. Calculate mad as median(absxc)   8. Calculate m[ ] as ((0.6745*xc)/mad)   9. Mark bins i as outliers if |m[i]|&gt;T   10. Mark events in D as outliers if they are in at least one bin that is marked as an outlier (in the overlapping bins case, an event may belong to several bins)   Where: D: flow cytometry expression data with a time channel
           T: MAD outlier threshold   B: bin size   
               

     In certain embodiments, one or more outliers in the event data signals is identified using a median outlier threshold algorithm having the steps of:
         1. Slice input data D into distinct or overlapping bins of equal or close to equal number of events, use preferred bin size B but allow minor adjustments to improve homogeneity of the bin size over D; if overlapping bins are used then those are all exactly the size of B   2. Calculate relevant characteristics for these bins, such as time or median expression of selected parameters, store in xr[ ]   3. Rescale result xr as x as appropriate (e.g., start of acquisition, last bin being shorter than other bins, user adjusting speed during acquisition)   4. Calculate xm as median(x)   5. Mark bins i as outliers if |x[i]|&gt;xm*(1+T)   6. Mark events in D as outliers if they are in at least one bin that is marked as an outlier (in the overlapping bins case, an event may belong to several bins)   Where: D: flow cytometry expression data with a time channel
           T: median outlier threshold   B: bin size   
               

     In some embodiments, the algorithm is such that it does not require all of the collected event data signals before calculating the median parameter characteristics. In some instances, once the data flow stabilizes during data acquisition, median characteristics (i.e., xm) from each of the plurality of binning windows can be estimated from a subset of bins without all the remaining data available. The rest of the algorithm can remain the same with bins and event data signals being classified as data becomes available. 
     In some embodiments, an alert is generated that an outlier is detected when assessing the plurality of data bins for outlier events. In some instances, methods include generating an alert that there is a change in flow rate in the flow stream of the particle analyzer. In some instances, the flow rate of the flow stream is measured with a flow rate sensor. In one example, an alert may be generated that there is an increase in the flow rate of the flow stream, such as when there is an increase in the flow rate by 1% or more, such as by 2% or more, such as by 3% or more, such as by 4% or more, such as 5% or more, such as by 10% or more, such as by 25% or more, such as by 50% or more, such as by 75% or more, such as by 90% or more and including where there is an increase in the flow rate of the flow stream by 95% or more. In one example, an alert may be generated that there is a decrease in the flow rate of the flow stream, such as when there is a decrease in the flow rate by 1% or more, such as by 2% or more, such as by 3% or more, such as by 4% or more, such as 5% or more, such as by 10% or more, such as by 25% or more, such as by 50% or more, such as by 75% or more, such as by 90% or more and including where there is a decrease in the flow rate of the flow stream by 95% or more. In response to the generated alert, the change in the flow rate in the flow stream can be correlated with one or more of the subject methods described herein, such as where the change in flow rate can be correlated with the rate of generating event data signals, the rate of binning of the generated event data signals, the size of each binning window (as described above) or the assessment of the plurality of binning windows for outlier events in the generated data signals. In certain instances, the rate of generating the event data signals by the particle analyzer is adjusted (i.e., increased or decreased) in response to the generated alert that there is a change in the flow rate of the flow stream. In other instances, the rate of binning of the generated event data signals is adjusted in response to the generated alert that there is a change in the flow rate of the flow stream. In other instances, the size of each binning window is adjusted in response to the generated alert that there is a change in the flow rate of the flow stream. In other instances, the assessment of the plurality of binning windows for outlier events in the generated data signals is adjusted in response to the generated alert that there is a change in the flow rate of the flow stream. 
       FIG.  2    depicts assessment of data signals from a particle analyzer according to certain embodiments. Event data signals generated in response to light from a sample detected by a light detection system is binned in a plurality of binning windows ( 201 ). Outlier-containing data bins are identified ( 202 ) by comparing a median parameter of the data bin with an outlier threshold. The outlier-containing data bins may be reassessed, such as in response to a change or adjustment to a parameter of the particle analyzer system (e.g., flow rate or photodetector voltage as described below). In certain embodiments, event data signals from the outlier-containing data bins may be excluded ( 203 ) from the acquired data. 
     In some instances, an alert is generated that there is a clog, bubble or entrainment in the flow stream in the particle analyzer. In certain instances, methods include comparing the measured flow rate of the flow stream with an expected flow rate of the flow stream and reassessing the data bins classified as being an outlier-containing data bin based on the compared flow rate of the flow stream. In some instances, one or more data bins are reclassified based on the compared flow rate of the flow stream. In some instances, an alert is not generated when the measured flow rate is within a predetermined threshold of the expected flow rate, for example where the measured flow rate is within 20% or less of the expected flow rate, such as within 15% or less, such as within 10% or less, such as within 9% or less, such as within 8% or less, such as within 7% or less, such as within 6% or less, such as within 5% or less, such as within 4% or less, such as within 3% or less, such as within 2% or less, such as within 1% or less and including not generating an alert when the measured flow rate is within 0.1% or less of the expected flow rate. 
     In some embodiments, an alert is generated due to an irregular parameter in the light detection system or due to a change to a parameter of the light detection system. In some instances, an alert is generated when there is a change (or a detected irregularity) to the photodetector voltage to one or more of the photodetectors of the light detection system. In some instances, methods include comparing the median or mean fluorescence intensity of the plurality of data bins with one or more parameters of the light detection system and reassessing the data bins classified as being an outlier-containing data bin based on the change (or a detected irregularity) to the parameter of the light detection system. 
     In some embodiments, when a change, anomaly or irregularity is detected in the flow stream (e.g., flow rate) or a parameter of the light detection system (e.g., photodetector voltage), methods include removing one or more of the reassessed data bins that were classified as being an outlier-containing data bins. One or more of the outlier-containing data bins may be removed from the acquired data of the sample, such as where all of the outlier-containing data bins are removed when an alert is generated in connection with the flow stream or parameter of the light detection system. In other embodiments, when a change, anomaly or irregularity is detected in the flow stream or a parameter of the light detection system, methods include reloading the sample and re-running sample acquisition by the particle analyzer. 
     In certain embodiments, a calibration of the particle analyzer is performed in response to a data bin being classified as being an outlier-containing data bin. In some instances, calibration of the particle analyzer includes detecting light with the light detection system from a standard composition irradiated by the light source in the flow stream, generating event data signals in response to events detected with the light detection system, binning the generated event data signals into a plurality of binning windows having overlapping data bins, assessing the generated data signals in the plurality of binning windows and determining that no outlier-containing data bins are present. In some instances, the standard composition includes particles (e.g., beads) having one or more fluorophores. In certain embodiments, the standard composition includes multispectral beads, such as those described in U.S. Provisional Patent Application No. 63/221,227 filed Jul. 13, 2021, the disclosure of which is herein incorporated by reference. 
     Particles of interest according to certain embodiments may include a single-peak multi-fluorophore bead that provides for a bright photodetector signal across all light source wavelengths (e.g., across all LEDs or lasers of the system) and across detection wavelengths of the photodetectors. Each particle may have one or more different types of fluorophores, such as 2 or more, or 3 or more, or 4 or more, or 5 or more, or 6 or more, or 7 or more, or 8 or more, or 9 or more, or 10 or more, or 11 or more, or 12 or more, or 13 or more, or 14 or more, or 15 or more, 16 or more, or 17 or more, or 18 or more, or 19 or more, or 20 or more, or 25 or more, or 30 or more, or 35 or more, or 40 or more, or 45 or more, 50 or more different types of fluorophores. For example, each particle may include 2, or 3, or 4, or 5, or 6, or 7, or 8, or 9, or 10, or 11, or 12, or 13, or 14, or 15, or 16, or 17, or 18, or 19, or 20 different types of fluorophores. In embodiments, each fluorophore is stably associated with the particle. By stably associated is meant that the fluorophore does not readily dissociate from the particle to contact with a liquid medium, e.g., an aqueous medium. In some embodiments, one or more of the fluorophores are covalently conjugated to the particle. In other embodiments, one or more of the fluorophores are physically associated (i.e., non-covalently coupled) to the particle. In other embodiments, one or more fluorophores are covalently conjugated to the particle and one or more fluorophores are physically associated with the particle. 
     The particles may be any convenient shape for irradiating by the light source as described above. In some instances, the particle is a solid support that is shaped or configured as discs, spheres, ovates, cubes, blocks, cones, etc., as well as irregular shapes. The mass of the particles may vary, ranging in some instances from 0.01 mg to 20 mg, such as from 0.05 mg to 19.5 mg, such as from 0.1 mg to 19 mg, such as from 0.5 mg to 18.5 mg, such as from 1 mg to 18 mg, such as from 1.5 mg to 17.5 mg, such as from 2 mg to 15 mg and including from 3 mg to 10 mg. The particle may have a surface area of 0.01 mm 2  or more, such as 0.05 mm 2  or more, such as 0.1 mm 2  or more, such as 0.5 mm 2  or more, such as 1 mm 2  or more, such as 1.5 mm 2  or more, such as 2 mm 2  or more, such as 2.5 mm 2  or more, such as 3 mm 2  or more, such as 3.5 mm 2  or more, such as 4 mm 2  or more, such as 4.5 mm 2  or more and including 5 mm 2  or more, e.g., as determined using a Vertex system or equivalent. 
     The dimensions of the particles may vary, as desired, where in some instances, particles have a longest dimension ranging from 0.01 mm to 10 mm, such as from 0.05 mm to 9.5 mm, such as from 0.1 mm to 9 mm, such as from 0.5 mm to 8.5 mm, such as from 1 mm to 8 mm, such as from 1.5 mm to 7.5 mm, such as from 2 mm to 7 mm, such as from 2.5 mm to 6.5 mm and including from 3 mm to 6 mm. In certain instances, particles have a shortest dimension ranging from 0.01 mm to 5 mm, such as from 0.05 mm to 4.5 mm, such as from 0.1 mm to 4 mm, such as from 0.5 mm to 3.5 mm and including from 1 mm to 3 mm. 
     In certain instances, particles of interest are porous, such as where the particles have a porosity ranging from 5μ to 100μ, such as from 10μ to 90μ, such as from 15μ to 85μ, such as from 20μ to 80μ, such as from 25μ to 75μ and including from 30μ to 70μ, for instance 50μ as determined for example using a Capillary Flow Porometer or equivalent. 
     The particles may be form from any convenient material. Of interest in some embodiments are particles, e.g., beads, having low or no auto-fluorescence. Suitable materials include, but are not limited to, glass materials (e.g., silicates), ceramic materials (e.g., calcium phosphates), metallic materials, and polymeric materials, etc. such as for example, polyethylene, polypropylene, polytetrafluoroethylene, polyvinylidine fluoride, and the like. In some instances, the particles are formed from a solid support, such as the porous matrices as described in U.S. Pat. No. 9,797,899, the disclosure of which is herein incorporated by reference. As such, a surface area of the particle may be any suitable macroporous or microporous substrate, where suitable macroporous and microporous substrates include, but are not limited to, ceramic matrices, frits, such as fritted glass, polymeric matrices as well as metal-organic polymeric matrices. In some embodiments, the porous matrix is a frit. The term “frit” is used herein in its conventional sense to refer to the porous composition formed from a sintered granulated solid, such as glass. Frits may have a chemical constituent which vary, depending on the type of sintered granulate used to prepare the frit, where frits that may be employed include, but are not limited to, frits composed of aluminosilicate, boron trioxide, borophosphosilicate glass, borosilicate glass, ceramic glaze, cobalt glass, cranberry glass, fluorophosphate glass, fluorosilicate glass, fuzed quartz, germanium dioxide, metal and sulfide embedded borosilicate, leaded glass, phosphate glass, phosphorus pentoxide glass, phosphosilicate glass, potassium silicate, soda-lime glass, sodium hexametaphosphate glass, sodium silicate, tellurite glass, uranium glass, vitrite and combinations thereof. In some embodiments, the porous matrix is a glass frit, such as a borosilicate, aluminosilicate, fluorosilicate, potassium silicate or borophosphosilicate glass frit. In some embodiments, the particle is formed from a porous organic polymer. 
       FIG.  3    depicts a flow chart for dynamic real-time assessment of data signals from a particle analyzer according to certain embodiments. At step  301 , light from a sample irradiated by a light source in a flow stream is detected with a light detection system. Event data signals are generated at step  302  in response to events detected with the light detection system. At step  303 , generated event data signals are binned in a plurality of binning windows that have overlapping data bins. The binning windows are assessed at step  304  for outlier events in the generated event data signals, where outlier containing data bins are classified when compared to an outlier threshold. In certain embodiments, methods include at step  305  determining that there is a change in a parameter of the particle analyzer, such as a change in the flow rate of the flow stream ( 306   a ) or a change in a parameter of the light detection system such as a change in photodetector voltage ( 306   b ). Each outlier-containing data bin is reassessed at step  307 , such as in response to the change in particle analyzer parameter. In certain embodiments, the outlier-containing data bins are removed ( 308   a ). In other embodiments, one or more data acquisition parameters (e.g., rate of event data signal binning) is adjusted ( 308   b ) in response to the reassessed binning windows and the parameter changes to the particle analyzer. In other embodiments, methods include calibrating the particle analyzer ( 308   c ) in response to the assessed plurality of binning windows where one or more outlier-containing data bins are identified. 
     Methods according to certain embodiments include irradiating the sample (e.g., in a flow stream of a flow cytometer) with light from a light source. In some embodiments, the light source is a broadband light source, emitting light having a broad range of wavelengths, such as for example, spanning 50 nm or more, such as 100 nm or more, such as 150 nm or more, such as 200 nm or more, such as 250 nm or more, such as 300 nm or more, such as 350 nm or more, such as 400 nm or more and including spanning 500 nm or more. For example, one suitable broadband light source emits light having wavelengths from 200 nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having wavelengths from 400 nm to 1000 nm. Where methods include irradiating with a broadband light source, broadband light source protocols of interest may include, but are not limited to, a halogen lamp, deuterium arc lamp, xenon arc lamp, stabilized fiber-coupled broadband light source, a broadband LED with continuous spectrum, superluminescent emitting diode, semiconductor light emitting diode, wide spectrum LED white light source, an multi-LED integrated white light source, among other broadband light sources or any combination thereof. 
     Depending on the light source, the sample may be irradiated from a distance which varies such as 0.01 mm or more, such as 0.05 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more, such as 2.5 mm or more, such as 5 mm or more, such as 10 mm or more, such as 15 mm or more, such as 25 mm or more and including 50 mm or more. Also, the angle or irradiation may also vary, ranging from 10° to 90°, such as from 15° to 85°, such as from 20° to 80°, such as from 25° to 75° and including from 30° to 60°, for example at a 90° angle. 
     In some embodiments, the methods include irradiating the sample in the flow stream with a continuous wave light source, such as where the light source provides uninterrupted light flux and maintains irradiation of particles in the flow stream with little to no undesired changes in light intensity. In some embodiments, the continuous light source emits non-pulsed or non-stroboscopic irradiation. In certain embodiments, the continuous light source provides for substantially constant emitted light intensity. For instance, methods may include irradiating the sample in the flow stream with a continuous light source that provides for emitted light intensity during a time interval of irradiation that varies by 10% or less, such as by 9% or less, such as by 8% or less, such as by 7% or less, such as by 6% or less, such as by 5% or less, such as by 4% or less, such as by 3% or less, such as by 2% or less, such as by 1% or less, such as by 0.5% or less, such as by 0.1% or less, such as by 0.01% or less, such as by 0.001% or less, such as by 0.0001% or less, such as by 0.00001% or less and including where the emitted light intensity during a time interval of irradiation varies by 0.000001% or less. The intensity of light output can be measured with any convenient protocol, including but not limited to, a scanning slit profiler, a charge coupled device (CCD, such as an intensified charge coupled device, ICCD), a positioning sensor, power sensor (e.g., a thermopile power sensor), optical power sensor, energy meter, digital laser photometer, a laser diode detector, among other types of photodetectors. 
     In other embodiments, the methods include irradiating the sample propagating through the flow stream with a pulsed light source, such as where light is emitted at predetermined time intervals, each time interval having a predetermined irradiation duration (i.e., pulse width). In certain embodiments, methods include irradiating the particle with the pulsed light source in each interrogation region of the flow stream with periodic flashes of light. For example, the frequency of each light pulse may be 0.0001 kHz or greater, such as 0.0005 kHz or greater, such as 0.001 kHz or greater, such as 0.005 kHz or greater, such as 0.01 kHz or greater, such as 0.05 kHz or greater, such as 0.1 kHz or greater, such as 0.5 kHz or greater, such as 1 kHz or greater, such as 2.5 kHz or greater, such as 5 kHz or greater, such as 10 kHz or greater, such as 25 kHz or greater, such as 50 kHz or greater and including 100 kHz or greater. In certain instances, the frequency of pulsed irradiation by the light source ranges from 0.00001 kHz to 1000 kHz, such as from 0.00005 kHz to 900 kHz, such as from 0.0001 kHz to 800 kHz, such as from 0.0005 kHz to 700 kHz, such as from 0.001 kHz to 600 kHz, such as from 0.005 kHz to 500 kHz, such as from 0.01 kHz to 400 kHz, such as from 0.05 kHz to 300 kHz, such as from 0.1 kHz to 200 kHz and including from 1 kHz to 100 kHz. The duration of light irradiation for each light pulse (i.e., pulse width) may vary and may be 0.000001 ms or more, such as 0.000005 ms or more, such as 0.00001 ms or more, such as 0.00005 ms or more, such as 0.0001 ms or more, such as 0.0005 ms or more, such as 0.001 ms or more, such as 0.005 ms or more, such as 0.01 ms or more, such as 0.05 ms or more, such as 0.1 ms or more, such as 0.5 ms or more, such as 1 ms or more, such as 2 ms or more, such as 3 ms or more, such as 4 ms or more, such as 5 ms or more, such as 10 ms or more, such as 25 ms or more, such as 50 ms or more, such as 100 ms or more and including 500 ms or more. For example, the duration of light irradiation may range from 0.000001 ms to 1000 ms, such as from 0.000005 ms to 950 ms, such as from 0.00001 ms to 900 ms, such as from 0.00005 ms to 850 ms, such as from 0.0001 ms to 800 ms, such as from 0.0005 ms to 750 ms, such as from 0.001 ms to 700 ms, such as from 0.005 ms to 650 ms, such as from 0.01 ms to 600 ms, such as from 0.05 ms to 550 ms, such as from 0.1 ms to 500 ms, such as from 0.5 ms to 450 ms, such as from 1 ms to 400 ms, such as from 5 ms to 350 ms and including from 10 ms to 300 ms. 
     In other embodiments, methods includes irradiating with a narrow band light source emitting a particular wavelength or a narrow range of wavelengths, such as for example with a light source which emits light in a narrow range of wavelengths like a range of 50 nm or less, such as 40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20 nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nm or less, such as 2 nm or less and including light sources which emit a specific wavelength of light (i.e., monochromatic light). Where methods include irradiating with a narrow band light source, narrow band light source protocols of interest may include, but are not limited to, a narrow wavelength LED, laser diode or a broadband light source coupled to one or more optical bandpass filters, diffraction gratings, monochromators or any combination thereof. 
     In certain embodiments, methods include irradiating the sample with one or more lasers. As discussed above, the type and number of lasers will vary depending on the sample as well as desired light collected and may be a gas laser, such as a helium-neon laser, argon laser, krypton laser, xenon laser, nitrogen laser, CO 2  laser, CO laser, argon-fluorine (ArF) excimer laser, krypton-fluorine (KrF) excimer laser, xenon chlorine (XeCl) excimer laser or xenon-fluorine (XeF) excimer laser or a combination thereof. In other instances, the methods include irradiating the flow stream with a dye laser, such as a stilbene, coumarin or rhodamine laser. In yet other instances, methods include irradiating the flow stream with a metal-vapor laser, such as a helium-cadmium (HeCd) laser, helium-mercury (HeHg) laser, helium-selenium (HeSe) laser, helium-silver (HeAg) laser, strontium laser, neon-copper (NeCu) laser, copper laser or gold laser and combinations thereof. In still other instances, methods include irradiating the flow stream with a solid-state laser, such as a ruby laser, an Nd:YAG laser, NdCrYAG laser, Er:YAG laser, Nd:YLF laser, Nd:YVO 4  laser, Nd:YCa 4 O(BO 3 ) 3  laser, Nd:YCOB laser, titanium sapphire laser, thulim YAG laser, ytterbium YAG laser, ytterbium 2 O 3  laser or cerium doped lasers and combinations thereof. 
     In certain embodiments, methods include irradiating the sample with two or more beams of frequency shifted light. As described above, a light beam generator component may be employed having a laser and an acousto-optic device for frequency shifting the laser light. In these embodiments, methods include irradiating the acousto-optic device with the laser. Depending on the desired wavelengths of light produced in the output laser beam (e.g., for use in irradiating a sample in a flow stream), the laser may have a specific wavelength that varies from 200 nm to 1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000 nm, such as from 350 nm to 900 nm and including from 400 nm to 800 nm. The acousto-optic device may be irradiated with one or more lasers, such as 2 or more lasers, such as 3 or more lasers, such as 4 or more lasers, such as 5 or more lasers and including 10 or more lasers. The lasers may include any combination of types of lasers. For example, in some embodiments, the methods include irradiating the acousto-optic device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers and one or more solid-state lasers. 
     Where more than one laser is employed, the acousto-optic device may be irradiated with the lasers simultaneously or sequentially, or a combination thereof. For example, the acousto-optic device may be simultaneously irradiated with each of the lasers. In other embodiments, the acousto-optic device is sequentially irradiated with each of the lasers. Where more than one laser is employed to irradiate the acousto-optic device sequentially, the time each laser irradiates the acousto-optic device may independently be 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microsecond or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more and including 60 microseconds or more. For example, methods may include irradiating the acousto-optic device with the laser for a duration which ranges from 0.001 microseconds to 100 microseconds, such as from 0.01 microseconds to 75 microseconds, such as from 0.1 microseconds to 50 microseconds, such as from 1 microsecond to 25 microseconds and including from 5 microseconds to 10 microseconds. In embodiments where the acousto-optic device is sequentially irradiated with two or more lasers, the duration the acousto-optic device is irradiated by each laser may be the same or different. 
     In embodiments, methods include applying radiofrequency drive signals to the acousto-optic device to generate angularly deflected laser beams. Two or more radiofrequency drive signals may be applied to the acousto-optic device to generate an output laser beam with the desired number of angularly deflected laser beams, such as 3 or more radiofrequency drive signals, such as 4 or more radiofrequency drive signals, such as 5 or more radiofrequency drive signals, such as 6 or more radiofrequency drive signals, such as 7 or more radiofrequency drive signals, such as 8 or more radiofrequency drive signals, such as 9 or more radiofrequency drive signals, such as 10 or more radiofrequency drive signals, such as 15 or more radiofrequency drive signals, such as 25 or more radiofrequency drive signals, such as 50 or more radiofrequency drive signals and including 100 or more radiofrequency drive signals. 
     The angularly deflected laser beams produced by the radiofrequency drive signals each have an intensity based on the amplitude of the applied radiofrequency drive signal. In some embodiments, methods include applying radiofrequency drive signals having amplitudes sufficient to produce angularly deflected laser beams with a desired intensity. In some instances, each applied radiofrequency drive signal independently has an amplitude from about 0.001 V to about 500 V, such as from about 0.005 V to about 400 V, such as from about 0.01 V to about 300 V, such as from about 0.05 V to about 200 V, such as from about 0.1 V to about 100 V, such as from about 0.5 V to about 75 V, such as from about 1 V to 50 V, such as from about 2 V to 40 V, such as from 3 V to about 30 V and including from about 5 V to about 25 V. Each applied radiofrequency drive signal has, in some embodiments, a frequency of from about 0.001 MHz to about 500 MHz, such as from about 0.005 MHz to about 400 MHz, such as from about 0.01 MHz to about 300 MHz, such as from about 0.05 MHz to about 200 MHz, such as from about 0.1 MHz to about 100 MHz, such as from about 0.5 MHz to about 90 MHz, such as from about 1 MHz to about 75 MHz, such as from about 2 MHz to about 70 MHz, such as from about 3 MHz to about 65 MHz, such as from about 4 MHz to about 60 MHz and including from about 5 MHz to about 50 MHz. 
     In these embodiments, the angularly deflected laser beams in the output laser beam are spatially separated. Depending on the applied radiofrequency drive signals and desired irradiation profile of the output laser beam, the angularly deflected laser beams may be separated by 0.001 μm or more, such as by 0.005 μm or more, such as by 0.01 μm or more, such as by 0.05 μm or more, such as by 0.1 μm or more, such as by 0.5 μm or more, such as by 1 μm or more, such as by 5 μm or more, such as by 10 μm or more, such as by 100 μm or more, such as by 500 μm or more, such as by 1000 μm or more and including by 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap, such as with an adjacent angularly deflected laser beam along a horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (such as overlap of beam spots) may be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, such as an overlap of 0.01 μm or more, such as an overlap of 0.05 μm or more, such as an overlap of 0.1 μm or more, such as an overlap of 0.5 μm or more, such as an overlap of 1 μm or more, such as an overlap of 5 μm or more, such as an overlap of 10 μm or more and including an overlap of 100 μm or more. 
     In certain instances, the flow stream is irradiated with a plurality of beams of frequency-shifted light and a cell in the flow stream is imaged by fluorescence imaging using radiofrequency tagged emission (FIRE) to generate a frequency-encoded image, such as those described in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as described in U.S. Pat. Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,111; and U.S. Patent Publication Nos. 2017/0133857; 2017/0328826; 2017/0350803; 2018/0275042; 2019/0376895 and 2019/0376894 the disclosures of which are herein incorporated by reference. 
     The sample may be irradiated with one or more of the above-mentioned light sources, such as 2 or more light sources, such as 3 or more light sources, such as 4 or more light sources, such as 5 or more light sources and including 10 or more light sources. The light source may include any combination of types of light sources. For example, in some embodiments, the methods include irradiating the sample in the flow stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers and one or more solid-state lasers. In some instances, the light source includes a plurality of lasers, such as where the plurality of lasers are configured to irradiate the flow stream at positions that are spaced apart from each other. For example, the light source may include 2 or more lasers that are spaced apart from each other along a longitudinal axis of the flow stream, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more and including 10 or more lasers that are spaced apart from each other along a longitudinal axis of the flow stream. In certain embodiments, each of the plurality of lasers is spaced apart from each other (e.g., such that each laser irradiates a different part of the flow stream) by 0.0001 mm or more, such as by 0.0005 mm or more, such as by 0.001 mm or more, such as by 0.005 mm or more, such as by 0.01 mm or more, such as by 0.05 mm or more, such as by 0.1 mm or more, such as by 0.5 mm or more, such as by 1 mm or more, such as by 2 mm or more, such as by 3 mm or more, such as by 4 mm or more, such as by 5 mm or more, such as by 10 mm or more, such as by 20 mm or more, such as by 30 mm or more, such as by 40 mm or more, such as by 50 mm or more and including by 100 mm or more. Depending on the flow rate of the particle through the flow stream, the time separation between irradiation of the particle by each laser may be from 0.001 μs 500 μs, such as from 0.005 μs to 400 μs, such as from 0.01 μs to 300 μs, such as from 0.05 μs to 200 μs, such as from 0.1 μs to 100 μs, such as from 0.5 μs to 75 μs and including from 1 μs to 50 μs. 
     The sample may be irradiated with wavelengths ranging from 200 nm to 1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000 nm, such as from 350 nm to 900 nm and including from 400 nm to 800 nm. For example, where the light source is a broadband light source, the sample may be irradiated with wavelengths from 200 nm to 900 nm. In other instances, where the light source includes a plurality of narrow band light sources, the sample may be irradiated with specific wavelengths in the range from 200 nm to 900 nm. For example, the light source may be plurality of narrow band LEDs (1 nm-25 nm) each independently emitting light having a range of wavelengths between 200 nm to 900 nm. In other embodiments, the narrow band light source includes one or more lasers (such as a laser array) and the sample is irradiated with specific wavelengths ranging from 200 nm to 700 nm, such as with a laser array having gas lasers, excimer lasers, dye lasers, metal vapor lasers and solid-state laser as described above. 
     Where more than one light source is employed, the sample may be irradiated with the light sources simultaneously or sequentially, or a combination thereof. For example, the sample may be simultaneously irradiated with each of the light sources. In other embodiments, the flow stream is sequentially irradiated with each of the light sources. Where more than one light source is employed to irradiate the sample sequentially, the time each light source irradiates the sample may independently be 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microsecond or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more and including 60 microseconds or more. For example, methods may include irradiating the sample with the light source (e.g. laser) for a duration which ranges from 0.001 microseconds to 100 microseconds, such as from 0.01 microseconds to 75 microseconds, such as from 0.1 microseconds to 50 microseconds, such as from 1 microsecond to 25 microseconds and including from 5 microseconds to 10 microseconds. In embodiments where sample is sequentially irradiated with two or more light sources, the duration sample is irradiated by each light source may be the same or different. 
     The time period between irradiation by each light source may also vary, as desired, being separated independently by a delay of 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microsecond or more, such as 5 microseconds or more, such as by 10 microseconds or more, such as by 15 microseconds or more, such as by 30 microseconds or more and including by 60 microseconds or more. For example, the time period between irradiation by each light source may range from 0.001 microseconds to 60 microseconds, such as from 0.01 microseconds to 50 microseconds, such as from 0.1 microseconds to 35 microseconds, such as from 1 microsecond to 25 microseconds and including from 5 microseconds to 10 microseconds. In certain embodiments, the time period between irradiation by each light source is 10 microseconds. In embodiments where sample is sequentially irradiated by more than two (i.e., 3 or more) light sources, the delay between irradiation by each light source may be the same or different. 
     The sample may be irradiated continuously or in discrete intervals. In some instances, methods include irradiating the sample in the sample with the light source continuously. In other instances, the sample in is irradiated with the light source in discrete intervals, such as irradiating every 0.001 millisecond, every 0.01 millisecond, every 0.1 millisecond, every 1 millisecond, every 10 milliseconds, every 100 milliseconds and including every 1000 milliseconds, or some other interval. 
     Particle Analyzer Systems 
     Aspects of the present disclosure also include systems (e.g., a particle analyzer) having a light detection system for practicing the subject methods. In embodiments, light detection systems include a detector component having a photodetector configured to detect light from a sample irradiated in a flow stream and to generate event data signals in response to events detected with the light detection system and a modulator component configured to bin the generated event data signals in a plurality of binning windows comprising overlapping data bins. In some embodiments, the detector component includes one or more of light scatter photodetectors, fluorescence light photodetectors and light loss photodetectors. In some instances, the detector component includes a forward scatter photodetector. In some instances, the detector component includes a side scatter photodetector. In some instances, the detector component includes a back scatter photodetector. In some instances, the detector component includes one or more fluorescence photodetectors. In some instances, the detector component includes a light loss photodetector. In some instances, the detector component includes a darkfield photodetector. In some instances, the detector component includes a brightfield photodetector. 
     The photodetectors of the subject light detection systems may by any photosensor, such as active-pixel sensors (APSs), avalanche photodiodes (APDs), image sensors, charge-coupled devices (CODs), intensified charge-coupled devices (ICCDs), complementary metal-oxide semiconductor (CMOS) image sensors or N-type metal-oxide semiconductor (NMOS) image sensors, light emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors or photodiodes and combinations thereof, among other types of photodetectors. 
     In some embodiments, the light detection system includes two or more photodetectors, such as 3 or more, such as 4 or more, such as 5 or more and including 10 or more photodetectors. In some instances, the photodetectors form a photodetector array. The term “photodetector array” is used in its conventional sense to refer to an arrangement or series of two or more photodetectors that are configured to detect light. In embodiments, photodetector arrays may include 2 or more photodetectors, such as 3 or more photodetectors, such as 4 or more photodetectors, such as 5 or more photodetectors, such as 6 or more photodetectors, such as 7 or more photodetectors, such as 8 or more photodetectors, such as 9 or more photodetectors, such as 10 or more photodetectors, such as 12 or more photodetectors and including 15 or more photodetectors. In certain embodiments, photodetector arrays include 5 photodetectors. The photodetectors may be arranged in any geometric configuration as desired, where arrangements of interest include, but are not limited to a square configuration, rectangular configuration, trapezoidal configuration, triangular configuration, hexagonal configuration, heptagonal configuration, octagonal configuration, nonagonal configuration, decagonal configuration, dodecagonal configuration, circular configuration, oval configuration as well as irregular shaped configurations. The photodetectors in the photodetector array may be oriented with respect to the other (as referenced in an X-Z plane) at an angle ranging from 10° to 180°, such as from 15° to 170°, such as from 20° to 160°, such as from 25° to 150°, such as from 30° to 120° and including from 45° to 90°. In embodiments, the detector component is configured to generate data signals in 4 or more photodetector channels, such as 16 or more, such as 32 or more and including in 64 or more photodetector channels. In some embodiments, the detector component is configured to generate data signals in one or more photodetector channels for each separated spectral component of light. In some instances, adjacent spectral components of the light generate data signals in adjacent photodetector channels. In some instances, adjacent spectral components of the light generate data signals in non-adjacent photodetector channels. 
     In certain embodiments, light detection systems of interest include a photodiode array having more than one photodiode, such as two or more photodiodes, such as three or more, such as five or more and including 10 or more photodiodes, where each photodiode may have an active detecting surface area of each region that ranges from 0.01 cm 2  to 10 cm 2 , such as from 0.05 cm 2  to 9 cm 2 , such as from, such as from 0.1 cm 2  to 8 cm 2 , such as from 0.5 cm 2  to 7 cm 2  and including from 1 cm 2  to 5 cm 2 . 
     In embodiments of the present disclosure, photodetectors of interest are configured to measure collected light at one or more wavelengths, such as at 2 or more wavelengths, such as at 5 or more different wavelengths, such as at 10 or more different wavelengths, such as at 25 or more different wavelengths, such as at 50 or more different wavelengths, such as at 100 or more different wavelengths, such as at 200 or more different wavelengths, such as at 300 or more different wavelengths and including measuring light from particles in the flow stream at 400 or more different wavelengths. In embodiments, the photodetectors are configured to measure light continuously or in discrete intervals. In some instances, detectors of interest are configured to take measurements of the collected light continuously. In other instances, detectors of interest are configured to take measurements in discrete intervals, such as measuring light every 0.001 millisecond, every 0.01 millisecond, every 0.1 millisecond, every 1 millisecond, every 10 milliseconds, every 100 milliseconds and including every 1000 milliseconds, or some other interval. 
     In some embodiments, light detection systems include a wavelength separator configured to spectrally separate light from the sample into a plurality of spectral ranges of light. The wavelength separator in certain instances is an optical component that is configured to separate light collected from the sample into predetermined spectral ranges. In some embodiments, the wavelength separator is configured to spatially diffract light collected from the sample into predetermined spectral ranges. The wavelength separator may be any convenient light separation protocol, such as a spectrometer, a prism, a diffraction grating, one or more dichroic mirrors, bandpass filters or beam splitters. In certain instances, the wavelength separator includes a spectrometer. 
     In some instances, the wavelength separator generates two or more predetermined spectral ranges of light, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, such as 10 or more, such as 15 or more, such as 25 or more, such as 50 or more, such as 75 or more and including 100 or more predetermined spectral ranges of light. In certain instances, the wavelength separator is configured to spectrally separate a plurality of ranges of light having wavelengths from 200 nm to 1200 nm, such as from 300 nm to 1100 nm, such as from 400 nm to 1000 nm, such as from 500 nm to 900 nm, such as from 600 nm to 800 nm. In some embodiments, each predetermined spectral range may have a spectral width of 0.0001 nm or more, such as 0.0005 nm or more, such as 0.001 nm or more, such as 0.005 nm or more, such as 0.01 nm or more, such as 0.05 nm or more, such as 0.1 nm or more, such as 0.5 nm or more, such as 1 nm or more, such as 5 nm or more and including 10 nm or more. For instance, in one example each predetermined spectral range has a spectral width of 5 nm, such as where one predetermined spectral separated range of light is from 210 nm to 215 nm. 
     In certain embodiments, the wavelength separator is configured to separate light collected from the sample into predetermined spectral ranges by passing light having a predetermined spectral range and reflecting one or more remaining spectral ranges of light. In some embodiments, the wavelength separator is configured to separate light collected from the sample into predetermined spectral ranges by passing light having a predetermined spectral range and absorbing one or more remaining spectral ranges of light. In some embodiments, the wavelength separator includes a linear variable optical filter. In some instances, the wavelength separator includes one or more linear variable bandpass filters. For example, the wavelength separator may include a long pass linear variable bandpass filter, a short pass linear variable bandpass filter or a combination of a long pass linear variable bandpass filter and a short pass linear variable bandpass filter. In other embodiments, the wavelength separator includes one or more linear variable edge filters. For example, the wavelength separator may include a long pass linear variable edge filter, a short pass linear variable edge filter or a combination of a long pass linear variable edge filter and a short pass linear variable edge filter. 
     In certain embodiments, the long pass linear variable bandpass filter is a linear variable bandpass filter which passes wavelengths of light across the bandpass filter that are greater than 400 nm, greater than 450 nm, greater than 500 nm, greater than 550 nm, greater than 600 nm, greater than 650 nm, greater than 700 nm, greater than 750 nm, greater than 800 nm. In certain instances, the wavelength separator includes a short pass linear variable bandpass filter. In certain embodiments, the short pass linear variable bandpass filter is a linear variable linear variable bandpass filter which passes wavelengths of light across the bandpass filter that are 600 nm or less, 550 nm or less, 500 nm or less, 450 nm or less, 400 nm or less, 350 nm or less, 300 nm or less, 250 nm or less, 200 nm or less. In some embodiments, the wavelength separator component includes one or more long pass linear variable bandpass filters and one or more short pass linear variable bandpass filters. In certain embodiments, the wavelength separator component includes a long pass linear variable bandpass filter and a short pass linear variable bandpass filter. 
     In some embodiments, the wavelength separator includes one or more linear variable edge filters such as 2 or more, such as 3 or more, such as 4 or more and including 5 or more linear variable edge filters. In certain embodiments, the long pass linear variable edge filter is a linear variable edge filter which passes wavelengths of light across the edge filter that are greater than 400 nm, greater than 450 nm, greater than 500 nm, greater than 550 nm, greater than 600 nm, greater than 650 nm, greater than 700 nm, greater than 750 nm, greater than 800 nm. In certain instances, the wavelength separator includes a short pass linear variable edge filter. In certain embodiments, the short pass linear variable edge filter is a linear variable edge filter which passes wavelengths of light across the edge filter that are 600 nm or less, 550 nm or less, 500 nm or less, 450 nm or less, 400 nm or less, 350 nm or less, 300 nm or less, 250 nm or less, 200 nm or less. In some embodiments, the wavelength separator component includes one or more long pass linear variable edge filters and one or more short pass linear variable edge filters. In certain embodiments, the wavelength separator component includes a long pass linear variable edge filter and a short pass linear variable edge filter. 
     In some embodiments, the detector component includes an optical adjustment component that is capable of changing the spatial width of the light from the irradiated sample or some other characteristic of the light, such as for example, propagation direction, wavelength, beam width, beam intensity and focal spot. Optical adjustment protocols may be any convenient device which adjusts one or more characteristics of the light, including but not limited to lenses, mirrors, filters, fiber optics, wavelength separators, pinholes, slits, collimating protocols and combinations thereof. 
     In embodiments, the light detection system includes a modulator component configured to bin the generated event data signals in a plurality of binning windows comprising overlapping data bins. In certain instances, the modulator component includes an integrated circuit. In embodiments, the integrated circuit device may be a field programmable gated array (FPGA), an application specific integrated circuit (ASIC) or a complex programmable logic device (CPLD), or some other integrated circuit device. In some embodiments, the modulator component includes an integrated circuit (e.g., a field programmable gate array, FPGA) having programming for binning event data signals from two or more different photodetector channels. In some embodiments, the event data signals that are binned include fluorescence intensity of the light detected in response to events. In some embodiments, the event data signals that are binned include the fluorescence expression of the events detected by the light detection system. In certain embodiments, the event data signals that are binned include time values of the events detected by the light detection system. 
     In some embodiments, the modulator component includes one or more amplifiers. In some embodiments, the amplifier component includes a plurality of amplifiers, such as transimpedence amplifiers, summing amplifiers, differential amplifiers or a combination thereof. In some instances, the modulator component includes an amplifier for each photodetector channel, such as where the modulator component includes 2 or more amplifiers, such as 4 or more, such as 8 or more, such as 12 or more, such as 16 or more, such as 20 or more, such as 24 or more, such as 28 or more, such as 32 or more, such as 36 or more, such as 40 or more, such as 44 or more, such as 48 or more, such as 52 or more, such as 56 or more, such as 60 or more and including 64 or more amplifiers. 
     In some embodiments, the modulator component includes a first amplifier component which is configured to amplify data signals from each photodetector channel and a second amplifier component configured to amplify data signals from the electronic switch component. In some instances, the first amplifier component includes a plurality of transimpedance amplifiers and the second amplifier component includes a plurality of differential amplifiers. 
     In some instances, the modulator component includes an electronic switch (e.g., a digital switch circuit) configured to bin data signals from two or more different photodetector channels. In certain instances, the switch is configured to multiplex or de-multiplex the output data signals from each photodetector channel. Depending on the number of photodetector channels and amplifiers (as described above) employed in the light detection system, the electronic switch component may include 2 or more electronic switches, such as 3 or more electronic switches, such as 4 or more electronic switches, such as 5 or more electronic switches, such as 6 or more electronic switches, such as 7 or more electronic switches, such as 8 or more electronic switches, such as 9 or more electronic switches, such as 10 or more electronic switches, such as 15 or more electronic switches, such as 25 or more electronic switches, such as 50 or more electronic switches, such as 100 or more electronic switches, such as 250 or more electronic switches, such as 500 or more electronic switches, such as 750 or more electronic switches and including 1000 or more electronic switches. 
     In some embodiments, light detection systems of interest include a photodetector array having N photodetector channels and an amplifier component having N transimpedance amplifiers, where N is an integer from 4 to 1000. In certain instances, the light detection system includes a photodetector array having N photodetector channels and a modulator component with N transimpedance amplifiers and an electronic switch component having an array of N×N switches. In some instances, the photodetector array may be a photodiode array having N photodiodes. The modulator component in these embodiments may include N transimpedence amplifiers and an array of N×N switches. In some embodiments, N is 8. In other embodiments, N is 16. In other embodiments, N is 32. In other embodiments, N is 64. In other embodiments, N is 128. 
     In some instances, the modulator component is configured to bin into each binning window a predetermined time frame of data acquisition (i.e., generated event data signals) by the particle analyzer. In some instances, each binning window includes a data acquisition time frame of 0.001 μs or more, such as 0.005 μs or more, such as 0.01 μs or more, such as 0.05 μs or more, such as 0.1 μs or more, such as 0.5 μs or more, such as 1 μs or more, such as 5 μs or more, such as 10 μs or more, such as 50 μs or more, such as 100 μs or more, such as 500 μs or more and including a data acquisition time frame of 1000 μs or more. In certain instances, each binning window includes a data acquisition time frame of from 0.001 μs to 5000 μs, such as from 0.005 μs to 4500 μs, such as 0.01 μs to 4000 μs, such as 0.05 μs to 3500 μs, such as from 0.1 μs to 3000 μs, such as from 0.5 μs to 2500 μs, such as from 1 μs to 2000 μs, such as from 5 μs to 1000 μs, such as from 10 μs to 500 μs and including a data acquisition time frame of from 50 μs to 100 μs. Each binning window may be the same duration or may be different. In some embodiments, the modulator component is configured to bin a predetermined number of event data signals into each binning window, such as where 5 or more event data signals are binned in each binning window, such as 10 event data signals or more, such as 25 event data signals or more, such as 50 event data signals or more, such as 75 event data signals or more, such as 100 event data signals or more, such as 125 event data signals or more, such as 150 event data signals or more, such as 175 event data signals or more, such as 200 event data signals or more, such as 300 event data signals or more, such as 400 event data signals or more, such as 500 event data signals or more, such as 600 event data signals or more, such as 700 event data signals or more, such as 800 event data signals or more, such as 900 event data signals or more and including binning 1000 event data signals or more in each binning window. 
     In some embodiments, the modulator component is configured to bin one or more of the generated event data signals in two or more of the binning windows (i.e., event data signals generated where the binning windows overlap). In some instances, the modulator component is configured to bin one or more of the generated event data signals in two or more of the binning windows that overlap by a time frame of 0.001 μs or more, such as 0.005 μs or more, such as 0.01 μs or more, such as 0.05 μs or more, such as 0.1 μs or more, such as 0.5 μs or more, such as 1 μs or more, such as 5 μs or more, such as 10 μs or more, such as 50 μs or more, such as 100 μs or more, such as 500 μs or more and including where the binning windows overlap by a time frame of 1000 μs or more. In certain instances, the binning windows overlap by a time frame of from 0.001 μs to 5000 μs, such as from 0.005 μs to 4500 μs, such as 0.01 μs to 4000 μs, such as 0.05 μs to 3500 μs, such as from 0.1 μs to 3000 μs, such as from 0.5 μs to 2500 μs, such as from 1 μs to 2000 μs, such as from 5 μs to 1000 μs, such as from 10 to 500 μs and including from 50 μs to 100 μs. 
     In some embodiments, the modulator component is configured to adjust the binning window in response to a change in the rate of data acquisition by the particle analyzer, such as where the flow rate of the flow stream is changed. In one example, the modulator component is configured to increase the size of one or more binning windows in response to a change in the flow rate of the flow stream, such as by 5% or more, such as by 10% or more, such as by 25% or more and including by 50% or more. In another example, the modulator component is configured to decrease the size of one or more binning windows in response to a change in the flow rate of the flow stream, such as by 5% or more, such as by 10% or more, such as by 25% or more and including by 50% or more. 
     In some embodiments, the modulator component is configured to apply a transformation to each event data signal. In some instances, the transformation is one or more of a centered log-ratio transformation, an additive log-ratio transformation, an isometric log-ratio transformation. 
     In some embodiments, systems include a processor having memory operably coupled to the processor where the memory includes instructions stored thereon, which when executed by the processor, cause the processor to calculate a median parameter for each data bin of the overlapping data bins. In some instances, the memory includes instructions for calculating a mean parameter for each data bin of the overlapping data bins. In some instances, the memory includes instructions to calculate a median or mean fluorescence intensity of events for each data bin. In some instances, the memory includes instructions for calculating a median or mean expression level for events for each data bin. 
     In some embodiments, the memory includes instructions for assessing the plurality of binning windows for outlier events includes comparing the median parameter for each data bin with an outlier threshold. In some instances, the outlier threshold is a median absolute deviation (MAD) outlier threshold. In some instances, the outlier threshold is a median tolerance percentage outlier threshold. In some embodiments, the memory includes instructions for dynamically calculating the outlier threshold, such as in real-time. In some instances, the memory includes instructions to dynamically adjust the outlier threshold in real-time in response to binning of each of the generated event data signals. In certain embodiments, systems include a processor having memory operably coupled to the processor where the memory includes instructions stored thereon, which when executed by the processor, cause the processor to implement a dynamic algorithm, such as a machine learning algorithm for calculating or adjusting a calculated outlier threshold. In some instances, the memory includes instructions for calculating an outlier threshold by the machine learning algorithm. In certain instances, the memory includes instructions for determining the outlier threshold with the machine learning algorithm. Machine-learning protocols of interest which may be programmed into the memory of the system may include, but are not limited to artificial neural networks, decision tree learning, decision tree predictive modeling, support vector machines, Bayesian networks, dynamic Bayesian networks, genetic algorithms among other machine learning protocols. 
     In some embodiments, systems include a processor having memory operably coupled to the processor where the memory includes instructions stored thereon, which when executed by the processor, cause the processor to classify one or more data bins in the plurality of binning windows as an outlier-containing data bin, such as where 2 or more data bins are classified as being an outlier-containing data bin, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more and including 10 or more. In some instances, the memory includes instructions to classify a data bin as being an outlier-containing data bin when a median (or mean) parameter is determined to be greater than the outlier threshold. In certain instances, the parameter is fluorescence intensity of events for each data bin and the memory includes instructions for classifying the data bin as being an outlier-containing data bin when the median or mean fluorescence intensity of event data signals is greater than the calculated outlier threshold (e.g., a median absolute deviation outlier threshold or median tolerance percentage outlier threshold). In other instances, the parameter is expression level for events for each data bin and the memory includes instructions for classifying the data bin as being an outlier-containing data bin when the median or mean expression level for events is greater than the calculated outlier threshold. In some embodiments, the memory includes instructions to classify a data bin as being an outlier-containing data bin when a median (or mean) parameter is determined to be within (i.e., greater than or less than) a predetermined percentage of the outlier threshold, such as where the median parameter (e.g., fluorescence intensity of the events or expression level) is within 10% of the outlier threshold, such as within 9% of the outlier threshold, such as within 8% of the outlier threshold, such as within 7% of the outlier threshold, such as within 6% of the outlier threshold, such as within 5% of the outlier threshold, such as within 4% of the outlier threshold, such as within 3% of the outlier threshold, such as within 2% of the outlier threshold and including where the median parameter is within 1% of the outlier threshold. In some instances, the memory includes instructions for identifying the event data signals of an outlier-containing data bin as being an outlier. 
     In certain embodiments, systems of interest include a flow rate sensor to measure the flow rate of the flow stream. The flow rate sensor may be any convenient protocol which measures the rate of fluidic flow, such as through a flow cell and include but are not limited to positive displacement flow sensors, mass flow sensors, velocity flow sensors as well as sensors which include feedback monitoring having optical scatter sensors, laser scatter sensors, laser diffraction sensors, optical absorption sensors as well as emission sensors. In certain embodiments, the flow rate sensor is configured to measure the rate of sample flow in the flow cell, such as where the flow rate of sample in the flow stream is 1 μL/min (microliter per minute) or more, such as 2 μL/min or more, such as 3 μL/min or more, such as 5 μL/min or more, such as 10 μL/min or more, such as 25 μL/min or more, such as 50 μL/min or more, such as 75 μL/min or more, such as 100 μL/min or more, such as 250 μL/min or more, such as 500 μL/min or more, such as 750 μL/min or more and including 1000 μL/min or more. In some embodiments, the memory includes instructions for generating an alert that an outlier is detected when assessing the plurality of data bins for outlier events. In some instances, the memory includes instructions for generating an alert that there is a change in flow rate in the flow stream of the particle analyzer. In one example, the memory includes instructions for generating an alert that there is an increase in the flow rate of the flow stream, such as when there is an increase in the flow rate by 1% or more, such as by 2% or more, such as by 3% or more, such as by 4% or more, such as 5% or more, such as by 10% or more, such as by 25% or more, such as by 50% or more, such as by 75% or more, such as by 90% or more and including where there is an increase in the flow rate of the flow stream by 95% or more. In one example, an alert may be generated that there is a decrease in the flow rate of the flow stream, such as when there is a decrease in the flow rate by 1% or more, such as by 2% or more, such as by 3% or more, such as by 4% or more, such as 5% or more, such as by 10% or more, such as by 25% or more, such as by 50% or more, such as by 75% or more, such as by 90% or more and including where there is a decrease in the flow rate of the flow stream by 95% or more. In some instances, the memory includes instructions for correlating one or more of the subject methods in response change in the flow rate in the flow, such as where the change in flow rate is correlated with the rate of generating event data signals, the rate of binning of the generated event data signals, the size of each binning window (as described above) or the assessment of the plurality of binning windows for outlier events in the generated data signals. In certain instances, the rate of generating the event data signals by the particle analyzer is adjusted (i.e., increased or decreased) in response to the generated alert that there is a change in the flow rate of the flow stream. In other instances, the rate of binning of the generated event data signals is adjusted in response to the generated alert that there is a change in the flow rate of the flow stream. In other instances, the size of each binning window is adjusted in response to the generated alert that there is a change in the flow rate of the flow stream. In other instances, the assessment of the plurality of binning windows for outlier events in the generated data signals is adjusted in response to the generated alert that there is a change in the flow rate of the flow stream. 
     In some embodiments, the memory includes instructions to generate an alert that there is a clog, bubble or entrainment in the flow stream in the particle analyzer. In certain instances, the memory includes instructions for comparing the measured flow rate of the flow stream with an expected flow rate of the flow stream and reassessing the data bins classified as being an outlier-containing data bin based on the compared flow rate of the flow stream. In some instances, the memory includes instructions for reclassifying one or more data bins are based on the compared flow rate of the flow stream. In some instances, an alert is not generated when the measured flow rate is within a predetermined threshold of the expected flow rate, for example where the measured flow rate is within 20% or less of the expected flow rate, such as within 15% or less, such as within 10% or less, such as within 9% or less, such as within 8% or less, such as within 7% or less, such as within 6% or less, such as within 5% or less, such as within 4% or less, such as within 3% or less, such as within 2% or less, such as within 1% or less and including not generating an alert when the measured flow rate is within 0.1% or less of the expected flow rate. 
     In some embodiments, the memory includes instructions for generating an alert due to an irregular parameter in the light detection system or due to a change to a parameter of the light detection system. In some instances, the memory includes instructions for generating an alert when there is a change (or a detected irregularity) to the photodetector voltage to one or more of the photodetectors of the light detection system. In some instances, the memory includes instructions for comparing the median or mean fluorescence intensity of the plurality of data bins with one or more parameters of the light detection system and reassessing the data bins classified as being an outlier-containing data bin based on the change (or a detected irregularity) to the parameter of the light detection system. 
     In some embodiments, the memory includes instructions for removing one or more of the reassessed data bins that were classified as being an outlier-containing data bins when a change, anomaly or irregularity is detected in the flow stream (e.g., flow rate) or a parameter of the light detection system (e.g., photodetector voltage). One or more of the outlier-containing data bins may be removed from the acquired data of the sample, such as where all of the outlier-containing data bins are removed when an alert is generated in connection with the flow stream or parameter of the light detection system. In other embodiments, the memory includes instructions for reloading the sample and re-running sample acquisition by the particle analyzer when a change, anomaly or irregularity is detected in the flow stream or a parameter of the light detection system. 
     In some embodiments, the system includes memory with instructions to perform a calibration of the particle analyzer in response to one or more data bins being classified as outlier-containing data bins. In some instances, instructions for calibration of the particle analyzer include instructions for detect light with the light detection system from a standard composition irradiated by the light source in the flow stream, instructions to generate event data signals in response to events detected with the light detection system, instructions to bin the generated event data signals into a plurality of binning windows having overlapping data bins, instructions to assess the generated data signals in the plurality of binning windows and instructions to determine that no outlier-containing data bins are present. 
     In some embodiments, systems of the present disclosure further include a light source for irradiating a sample having particles in a flow stream. Systems of interest include a light source configured to irradiate a sample in a flow stream. In embodiments, the light source may be any suitable broadband or narrow band source of light. Depending on the components in the sample (e.g., cells, beads, non-cellular particles, etc.), the light source may be configured to emit wavelengths of light that vary, ranging from 200 nm to 1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000 nm, such as from 350 nm to 900 nm and including from 400 nm to 800 nm. For example, the light source may include a broadband light source emitting light having wavelengths from 200 nm to 900 nm. In other instances, the light source includes a narrow band light source emitting a wavelength ranging from 200 nm to 900 nm. For example, the light source may be a narrow band LED (1 nm-25 nm) emitting light having a wavelength ranging between 200 nm to 900 nm. 
     In some embodiments, the light source is a laser. Lasers of interest may include pulsed lasers or continuous wave lasers. For example, the laser may be a gas laser, such as a helium-neon laser, argon laser, krypton laser, xenon laser, nitrogen laser, CO 2  laser, CO laser, argon-fluorine (ArF) excimer laser, krypton-fluorine (KrF) excimer laser, xenon chlorine (XeCl) excimer laser or xenon-fluorine (XeF) excimer laser or a combination thereof; a dye laser, such as a stilbene, coumarin or rhodamine laser; a metal-vapor laser, such as a helium-cadmium (HeCd) laser, helium-mercury (HeHg) laser, helium-selenium (HeSe) laser, helium-silver (HeAg) laser, strontium laser, neon-copper (NeCu) laser, copper laser or gold laser and combinations thereof; a solid-state laser, such as a ruby laser, an Nd:YAG laser, NdCrYAG laser, Er:YAG laser, Nd:YLF laser, Nd:YVO 4  laser, Nd:YCa 4 O(BO 3 ) 3  laser, Nd:YCOB laser, titanium sapphire laser, thulim YAG laser, ytterbium YAG laser, ytterbium 2 O 3  laser or cerium doped lasers and combinations thereof; a semiconductor diode laser, optically pumped semiconductor laser (OPSL), or a frequency doubled- or frequency tripled implementation of any of the above mentioned lasers. 
     In other embodiments, the light source is a non-laser light source, such as a lamp, including but not limited to a halogen lamp, deuterium arc lamp, xenon arc lamp, a light-emitting diode, such as a broadband LED with continuous spectrum, superluminescent emitting diode, semiconductor light emitting diode, wide spectrum LED white light source, an multi-LED integrated. In some instances, the non-laser light source is a stabilized fiber-coupled broadband light source, white light source, among other light sources or any combination thereof. 
     In certain embodiments, the light source is a light beam generator that is configured to generate two or more beams of frequency shifted light. In some instances, the light beam generator includes a laser, a radiofrequency generator configured to apply radiofrequency drive signals to an acousto-optic device to generate two or more angularly deflected laser beams. In these embodiments, the laser may be a pulsed lasers or continuous wave laser. The acousto-optic device may be any convenient acousto-optic protocol configured to frequency shift laser light using applied acoustic waves. In certain embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in the subject system is configured to generate angularly deflected laser beams from the light from the laser and the applied radiofrequency drive signals. The radiofrequency drive signals may be applied to the acousto-optic device with any suitable radiofrequency drive signal source, such as a direct digital synthesizer (DDS), arbitrary waveform generator (AWG), or electrical pulse generator. 
     In embodiments, a controller is configured to apply radiofrequency drive signals to the acousto-optic device to produce the desired number of angularly deflected laser beams in the output laser beam, such as being configured to apply 3 or more radiofrequency drive signals, such as 4 or more radiofrequency drive signals, such as 5 or more radiofrequency drive signals, such as 6 or more radiofrequency drive signals, such as 7 or more radiofrequency drive signals, such as 8 or more radiofrequency drive signals, such as 9 or more radiofrequency drive signals, such as 10 or more radiofrequency drive signals, such as 15 or more radiofrequency drive signals, such as 25 or more radiofrequency drive signals, such as 50 or more radiofrequency drive signals and including being configured to apply 100 or more radiofrequency drive signals. 
     In some instances, to produce an intensity profile of the angularly deflected laser beams in the output laser beam, the controller is configured to apply radiofrequency drive signals having an amplitude that varies such as from about 0.001 V to about 500 V, such as from about 0.005 V to about 400 V, such as from about 0.01 V to about 300 V, such as from about 0.05 V to about 200 V, such as from about 0.1 V to about 100 V, such as from about 0.5 V to about 75 V, such as from about 1 V to 50 V, such as from about 2 V to 40 V, such as from 3 V to about 30 V and including from about 5 V to about 25 V. Each applied radiofrequency drive signal has, in some embodiments, a frequency of from about 0.001 MHz to about 500 MHz, such as from about 0.005 MHz to about 400 MHz, such as from about 0.01 MHz to about 300 MHz, such as from about 0.05 MHz to about 200 MHz, such as from about 0.1 MHz to about 100 MHz, such as from about 0.5 MHz to about 90 MHz, such as from about 1 MHz to about 75 MHz, such as from about 2 MHz to about 70 MHz, such as from about 3 MHz to about 65 MHz, such as from about 4 MHz to about 60 MHz and including from about 5 MHz to about 50 MHz. 
     In certain embodiments, the controller has a processor having memory operably coupled to the processor such that the memory includes instructions stored thereon, which when executed by the processor, cause the processor to produce an output laser beam with angularly deflected laser beams having a desired intensity profile. For example, the memory may include instructions to produce two or more angularly deflected laser beams with the same intensities, such as 3 or more, such as 4 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more and including memory may include instructions to produce 100 or more angularly deflected laser beams with the same intensities. In other embodiments, the may include instructions to produce two or more angularly deflected laser beams with different intensities, such as 3 or more, such as 4 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more and including memory may include instructions to produce 100 or more angularly deflected laser beams with different intensities. 
     In certain instances, light beam generators configured to generate two or more beams of frequency shifted light include laser excitation modules as described in U.S. Pat. Nos. 9,423,353; 9,784,661 and 10,006,852 and U.S. Patent Publication Nos. 2017/0133857 and 2017/0350803, the disclosures of which are herein incorporated by reference. 
     In certain embodiments, systems further include a flow cell configured to propagate the sample in the flow stream. Any convenient flow cell which propagates a fluidic sample to a sample interrogation region may be employed, where in some embodiments, the flow cell includes a proximal cylindrical portion defining a longitudinal axis and a distal frustoconical portion which terminates in a flat surface having the orifice that is transverse to the longitudinal axis. The length of the proximal cylindrical portion (as measured along the longitudinal axis) may vary ranging from 1 mm to 15 mm, such as from 1.5 mm to 12.5 mm, such as from 2 mm to 10 mm, such as from 3 mm to 9 mm and including from 4 mm to 8 mm. The length of the distal frustoconical portion (as measured along the longitudinal axis) may also vary, ranging from 1 mm to 10 mm, such as from 2 mm to 9 mm, such as from 3 mm to 8 mm and including from 4 mm to 7 mm. The diameter of the of the flow cell nozzle chamber may vary, in some embodiments, ranging from 1 mm to 10 mm, such as from 2 mm to 9 mm, such as from 3 mm to 8 mm and including from 4 mm to 7 mm. 
     In certain instances, the flow cell does not include a cylindrical portion and the entire flow cell inner chamber is frustoconically shaped. In these embodiments, the length of the frustoconical inner chamber (as measured along the longitudinal axis transverse to the nozzle orifice), may range from 1 mm to 15 mm, such as from 1.5 mm to 12.5 mm, such as from 2 mm to 10 mm, such as from 3 mm to 9 mm and including from 4 mm to 8 mm. The diameter of the proximal portion of the frustoconical inner chamber may range from 1 mm to 10 mm, such as from 2 mm to 9 mm, such as from 3 mm to 8 mm and including from 4 mm to 7 mm. 
     In some embodiments, the sample flow stream emanates from an orifice at the distal end of the flow cell. Depending on the desired characteristics of the flow stream, the flow cell orifice may be any suitable shape where cross-sectional shapes of interest include, but are not limited to: rectilinear cross sectional shapes, e.g., squares, rectangles, trapezoids, triangles, hexagons, etc., curvilinear cross-sectional shapes, e.g., circles, ovals, as well as irregular shapes, e.g., a parabolic bottom portion coupled to a planar top portion. In certain embodiments, flow cell of interest has a circular orifice. The size of the nozzle orifice may vary, in some embodiments ranging from 1 μm to 20000 μm, such as from 2 μm to 17500 μm, such as from 5 μm to 15000 μm, such as from 10 μm to 12500 μm, such as from 15 μm to 10000 μm, such as from 25 μm to 7500 μm, such as from 50 μm to 5000 μm, such as from 75 μm to 1000 μm, such as from 100 μm to 750 μm and including from 150 μm to 500 μm. In certain embodiments, the nozzle orifice is 100 μm. 
     In some embodiments, the flow cell includes a sample injection port configured to provide a sample to the flow cell. In embodiments, the sample injection system is configured to provide suitable flow of sample to the flow cell inner chamber. Depending on the desired characteristics of the flow stream, the rate of sample conveyed to the flow cell chamber by the sample injection port may be 1 μL/min or more, such as 2 μL/min or more, such as 3 μL/min or more, such as 5 μL/min or more, such as 10 μL/min or more, such as 15 μL/min or more, such as 25 μL/min or more, such as 50 μL/min or more and including 100 μL/min or more, where in some instances the rate of sample conveyed to the flow cell chamber by the sample injection port is 1 μL/sec or more, such as 2 μL/sec or more, such as 3 μL/sec or more, such as 5 μL/sec or more, such as 10 μL/sec or more, such as 15 μL/sec or more, such as 25 μL/sec or more, such as 50 μL/sec or more and including 100 μL/sec or more. 
     The sample injection port may be an orifice positioned in a wall of the inner chamber or may be a conduit positioned at the proximal end of the inner chamber. Where the sample injection port is an orifice positioned in a wall of the inner chamber, the sample injection port orifice may be any suitable shape where cross-sectional shapes of interest include, but are not limited to: rectilinear cross sectional shapes, e.g., squares, rectangles, trapezoids, triangles, hexagons, etc., curvilinear cross-sectional shapes, e.g., circles, ovals, etc., as well as irregular shapes, e.g., a parabolic bottom portion coupled to a planar top portion. In certain embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice may vary depending on shape, in certain instances, having an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 to 3.0 mm, e.g., 0.5 mm to 2.5 mm, such as from 0.75 mm to 2.25 mm, such as from 1 mm to 2 mm and including from 1.25 mm to 1.75 mm, for example 1.5 mm. 
     In certain instances, the sample injection port is a conduit positioned at a proximal end of the flow cell inner chamber. For example, the sample injection port may be a conduit positioned to have the orifice of the sample injection port in line with the flow cell orifice. Where the sample injection port is a conduit positioned in line with the flow cell orifice, the cross-sectional shape of the sample injection tube may be any suitable shape where cross-sectional shapes of interest include, but are not limited to: rectilinear cross sectional shapes, e.g., squares, rectangles, trapezoids, triangles, hexagons, etc., curvilinear cross-sectional shapes, e.g., circles, ovals, as well as irregular shapes, e.g., a parabolic bottom portion coupled to a planar top portion. The orifice of the conduit may vary depending on shape, in certain instances, having an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 to 3.0 mm, e.g., 0.5 mm to 2.5 mm, such as from 0.75 mm to 2.25 mm, such as from 1 mm to 2 mm and including from 1.25 mm to 1.75 mm, for example 1.5 mm. The shape of the tip of the sample injection port may be the same or different from the cross-section shape of the sample injection tube. For example, the orifice of the sample injection port may include a beveled tip having a bevel angle ranging from 1° to 10°, such as from 2° to 9°, such as from 3° to 8°, such as from 4° to 7° and including a bevel angle of 5°. 
     In some embodiments, the flow cell also includes a sheath fluid injection port configured to provide a sheath fluid to the flow cell. In embodiments, the sheath fluid injection system is configured to provide a flow of sheath fluid to the flow cell inner chamber, for example in conjunction with the sample to produce a laminated flow stream of sheath fluid surrounding the sample flow stream. Depending on the desired characteristics of the flow stream, the rate of sheath fluid conveyed to the flow cell chamber by the may be 254/sec or more, such as 50 μL/sec or more, such as 75 μL/sec or more, such as 100 μL/sec or more, such as 250 μL/sec or more, such as 500 μL/sec or more, such as 750 μL/sec or more, such as 1000 μL/sec or more and including 2500 μL/sec or more. 
     In some embodiments, the sheath fluid injection port is an orifice positioned in a wall of the inner chamber. The sheath fluid injection port orifice may be any suitable shape where cross-sectional shapes of interest include, but are not limited to: rectilinear cross-sectional shapes, e.g., squares, rectangles, trapezoids, triangles, hexagons, etc., curvilinear cross-sectional shapes, e.g., circles, ovals, as well as irregular shapes, e.g., a parabolic bottom portion coupled to a planar top portion. The size of the sample injection port orifice may vary depending on shape, in certain instances, having an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 to 3.0 mm, e.g., 0.5 mm to 2.5 mm, such as from 0.75 mm to 2.25 mm, such as from 1 mm to 2 mm and including from 1.25 mm to 1.75 mm, for example 1.5 mm. 
     In some embodiments, systems further include a pump in fluid communication with the flow cell to propagate the flow stream through the flow cell. Any convenient fluid pump protocol may be employed to control the flow of the flow stream through the flow cell. In certain instances, systems include a peristaltic pump, such as a peristaltic pump having a pulse damper. The pump in the subject systems is configured to convey fluid through the flow cell at a rate suitable for detecting light from the sample in the flow stream. In some instances, the rate of sample flow in the flow cell is 1 μL/min (microliter per minute) or more, such as 2 μL/min or more, such as 3 μL/min or more, such as 5 μL/min or more, such as 10 μL/min or more, such as 25 μL/min or more, such as 50 μL/min or more, such as 75 μL/min or more, such as 100 μL/min or more, such as 250 μL/min or more, such as 500 μL/min or more, such as 750 μL/min or more and including 1000 μL/min or more. For example, the system may include a pump that is configured to flow sample through the flow cell at a rate that ranges from 1 μL/min to 500 μL/min, such as from 1 μL/min to 250 μL/min, such as from 1 μL/min to 100 μL/min, such as from 2 μL/min to 90 μL/min, such as from 3 μL/min to 80 μL/min, such as from 4 μL/min to 70 μL/min, such as from 5 μL/min to 60 μL/min and including rom 10 μL/min to 50 μL/min. In certain embodiments, the flow rate of the flow stream is from 5 μL/min to 6 μL/min. 
     In certain embodiments, light detection systems having the plurality of photodetectors as described above are part of or positioned in a particle analyzer, such as a particle sorter. In certain embodiments, the subject systems are flow cytometric systems that includes the photodiode and amplifier component as part of a light detection system for detecting light emitted by a sample in a flow stream. Suitable flow cytometry systems may include, but are not limited to, those described in Ormerod (ed.), Flow  Cytometry: A Practical Approach , Oxford Univ. Press (1997); Jaroszeski et al. (eds.),  Flow Cytometry Protocols , Methods in Molecular Biology No. 91, Humana Press (1997);  Practical Flow Cytometry,  3rd ed., Wiley-Liss (1995); Virgo, et al. (2012)  Ann Clin Biochem . January; 49(pt 1):17-28; Linden, et. al.,  Semin Throm Hemost.  2004 October; 30(5):502-11; Alison, et al.  J Pathol,  2010 December; 222(4):335-344; and Herbig, et al. (2007)  Crit Rev Ther Drug Carrier Syst.  24(3):203-255; the disclosures of which are incorporated herein by reference. In certain instances, flow cytometry systems of interest include BD Biosciences FACSCanto™ flow cytometer, BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Accuri™ C6 Plus flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortessa™ X-20 flow cytometer, BD Biosciences FACSPresto™ flow cytometer, BD Biosciences FACSVia™ flow cytometer and BD Biosciences FACSCalibur™ cell sorter, a BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter or the like. 
     In some embodiments, the subject systems are flow cytometric systems, such those described in U.S. Pat. Nos. 10,663,476; 10,620,111; 10,613,017; 10,605,713; 10,585,031; 10,578,542; 10,578,469; 10,481,074; 10,302,545; 10,145,793; 10,113,967; 10,006,852; 9,952,076; 9,933,341; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9,095,494; 9,092,034; 8,975,595; 8,753,573; 8,233,146; 8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039; 4,987,086; 4,498,766; the disclosures of which are herein incorporated by reference in their entirety. 
     In some embodiments, the subject systems are particle sorting systems that are configured to sort particles with an enclosed particle sorting module, such as those described in U.S. Patent Publication No. 2017/0299493, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of the sample are sorted using a sort decision module having a plurality of sort decision units, such as those described in U.S. Patent Publication No. 2020/0256781, the disclosure of which is incorporated herein by reference. In some embodiments, the subject systems include a particle sorting module having deflector plates, such as described in U.S. Patent Publication No. 2017/0299493, filed on Mar. 28, 2017, the disclosure of which is incorporated herein by reference. 
     In certain instances, flow cytometry systems of the invention are configured for imaging particles in a flow stream by fluorescence imaging using radiofrequency tagged emission (FIRE), such as those described in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013) as well as described in U.S. Pat. Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,111; and U.S. Patent Publication Nos. 2017/0133857; 2017/0328826; 2017/0350803; 2018/0275042; 2019/0376895 and 2019/0376894 the disclosures of which are herein incorporated by reference. 
     In some embodiments, systems are particle analyzers where the particle analysis system  401  ( FIG.  4 A ) can be used to analyze and characterize particles, with or without physically sorting the particles into collection vessels.  FIG.  4 A  shows a functional block diagram of a particle analysis system for computational based sample analysis and particle characterization. In some embodiments, the particle analysis system  401  is a flow system. The particle analysis system  401  shown in  FIG.  4 A  can be configured to perform, in whole or in part, the methods described herein such as. The particle analysis system  401  includes a fluidics system  402 . The fluidics system  402  can include or be coupled with a sample tube  405  and a moving fluid column within the sample tube in which particles  403  (e.g. cells) of a sample move along a common sample path  409 . 
     The particle analysis system  401  includes a detection system  404  configured to collect a signal from each particle as it passes one or more detection stations along the common sample path. A detection station  408  generally refers to a monitored area  407  of the common sample path. Detection can, in some implementations, include detecting light or one or more other properties of the particles  403  as they pass through a monitored area  407 . In  FIG.  4 A , one detection station  408  with one monitored area  407  is shown. Some implementations of the particle analysis system  401  can include multiple detection stations. Furthermore, some detection stations can monitor more than one area. 
     Each signal is assigned a signal value to form a data point for each particle. As described above, this data can be referred to as event data. The data point can be a multidimensional data point including values for respective properties measured for a particle. The detection system  404  is configured to collect a succession of such data points in a first-time interval. 
     The particle analysis system  401  can also include a control system  306 . The control system  406  can include one or more processors, an amplitude control circuit and/or a frequency control circuit. The control system shown can be operationally associated with the fluidics system  402 . The control system can be configured to generate a calculated signal frequency for at least a portion of the first-time interval based on a Poisson distribution and the number of data points collected by the detection system  404  during the first time interval. The control system  406  can be further configured to generate an experimental signal frequency based on the number of data points in the portion of the first time interval. The control system  406  can additionally compare the experimental signal frequency with that of a calculated signal frequency or a predetermined signal frequency. 
       FIG.  4 B  shows a system  400  for flow cytometry in accordance with an illustrative embodiment of the present invention. The system  400  includes a flow cytometer  410 , a controller/processor  490  and a memory  495 . The flow cytometer  410  includes one or more excitation lasers  415   a - 415   c , a focusing lens  420 , a flow chamber  425 , a forward scatter detector  430 , a side scatter detector  435 , a fluorescence collection lens  440 , one or more beam splitters  445   a - 445   g , one or more bandpass filters  450   a - 450   e , one or more longpass (“LP”) filters  455   a - 455   b , and one or more fluorescent detectors  460   a - 460   f.    
     The excitation lasers  115   a - c  emit light in the form of a laser beam. The wavelengths of the laser beams emitted from excitation lasers  415   a - 415   c  are 488 nm, 633 nm, and 325 nm, respectively, in the example system of  FIG.  4 B . The laser beams are first directed through one or more of beam splitters  445   a  and  445   b . Beam splitter  445   a  transmits light at 488 nm and reflects light at 633 nm. Beam splitter  445   b  transmits UV light (light with a wavelength in the range of 10 to 400 nm) and reflects light at 488 nm and 633 nm. 
     The laser beams are then directed to a focusing lens  420 , which focuses the beams onto the portion of a fluid stream where particles of a sample are located, within the flow chamber  425 . The flow chamber is part of a fluidics system which directs particles, typically one at a time, in a stream to the focused laser beam for interrogation. The flow chamber can comprise a flow cell in a benchtop cytometer or a nozzle tip in a stream-in-air cytometer. 
     The light from the laser beam(s) interacts with the particles in the sample by diffraction, refraction, reflection, scattering, and absorption with re-emission at various different wavelengths depending on the characteristics of the particle such as its size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or in the particle. The fluorescence emissions as well as the diffracted light, refracted light, reflected light, and scattered light may be routed to one or more of the forward scatter detector  430 , the side scatter detector  435 , and the one or more fluorescent detectors  460   a - 460   f  through one or more of the beam splitters  445   a - 445   g , the bandpass filters  450   a - 450   e , the longpass filters  455   a - 455   b , and the fluorescence collection lens  440 . 
     The fluorescence collection lens  440  collects light emitted from the particle-laser beam interaction and routes that light towards one or more beam splitters and filters. Bandpass filters, such as bandpass filters  450   a - 450   e , allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter  450   a  is a 510/20 filter. The first number represents the center of a spectral band. The second number provides a range of the spectral band. Thus, a 510/20 filter extends 10 nm on each side of the center of the spectral band, or from 500 nm to 520 nm. Shortpass filters transmit wavelengths of light equal to or shorter than a specified wavelength. Longpass filters, such as longpass filters  455   a - 455   b , transmit wavelengths of light equal to or longer than a specified wavelength of light. For example, longpass filter  455   a , which is a 670 nm longpass filter, transmits light equal to or longer than 670 nm. Filters are often selected to optimize the specificity of a detector for a particular fluorescent dye. The filters can be configured so that the spectral band of light transmitted to the detector is close to the emission peak of a fluorescent dye. 
     Beam splitters direct light of different wavelengths in different directions. Beam splitters can be characterized by filter properties such as shortpass and longpass. For example, beam splitter  445   g  is a 620 SP beam splitter, meaning that the beam splitter  445   g  transmits wavelengths of light that are 620 nm or shorter and reflects wavelengths of light that are longer than 620 nm in a different direction. In one embodiment, the beam splitters  445   a - 445   g  can comprise optical mirrors, such as dichroic mirrors. 
     The forward scatter detector  430  is positioned slightly off axis from the direct beam through the flow cell and is configured to detect diffracted light, the excitation light that travels through or around the particle in mostly a forward direction. The intensity of the light detected by the forward scatter detector is dependent on the overall size of the particle. The forward scatter detector can include a photodiode. The side scatter detector  435  is configured to detect refracted and reflected light from the surfaces and internal structures of the particle, and tends to increase with increasing particle complexity of structure. The fluorescence emissions from fluorescent molecules associated with the particle can be detected by the one or more fluorescent detectors  460   a - 460   f . The side scatter detector  435  and fluorescent detectors can include photomultiplier tubes. The signals detected at the forward scatter detector  430 , the side scatter detector  435  and the fluorescent detectors can be converted to electronic signals (voltages) by the detectors. This data can provide information about the sample. 
     One of skill in the art will recognize that a flow cytometer in accordance with an embodiment of the present invention is not limited to the flow cytometer depicted in  FIG.  4 B , but can include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in various different configurations. 
     In operation, cytometer operation is controlled by a controller/processor  490 , and the measurement data from the detectors can be stored in the memory  495  and processed by the controller/processor  490 . Although not shown explicitly, the controller/processor  190  is coupled to the detectors to receive the output signals therefrom, and may also be coupled to electrical and electromechanical components of the flow cytometer  400  to control the lasers, fluid flow parameters, and the like. Input/output (I/O) capabilities  497  may be provided also in the system. The memory  495 , controller/processor  490 , and I/O  497  may be entirely provided as an integral part of the flow cytometer  410 . In such an embodiment, a display may also form part of the I/O capabilities  497  for presenting experimental data to users of the cytometer  400 . Alternatively, some or all of the memory  495  and controller/processor  490  and I/O capabilities may be part of one or more external devices such as a general purpose computer. In some embodiments, some or all of the memory  495  and controller/processor  490  can be in wireless or wired communication with the cytometer  410 . The controller/processor  490  in conjunction with the memory  495  and the I/O  497  can be configured to perform various functions related to the preparation and analysis of a flow cytometer experiment. 
     The system illustrated in  FIG.  4 B  includes six different detectors that detect fluorescent light in six different wavelength bands (which may be referred to herein as a “filter window” for a given detector) as defined by the configuration of filters and/or splitters in the beam path from the flow cell  425  to each detector. Different fluorescent molecules used for a flow cytometer experiment will emit light in their own characteristic wavelength bands. The particular fluorescent labels used for an experiment and their associated fluorescent emission bands may be selected to generally coincide with the filter windows of the detectors. However, as more detectors are provided, and more labels are utilized, perfect correspondence between filter windows and fluorescent emission spectra is not possible. It is generally true that although the peak of the emission spectra of a particular fluorescent molecule may lie within the filter window of one particular detector, some of the emission spectra of that label will also overlap the filter windows of one or more other detectors. This may be referred to as spillover. The I/O  497  can be configured to receive data regarding a flow cytometer experiment having a panel of fluorescent labels and a plurality of cell populations having a plurality of markers, each cell population having a subset of the plurality of markers. The I/O  497  can also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experiment data, such as label spectral characteristics and flow cytometer configuration data can also be stored in the memory  495 . The controller/processor  490  can be configured to evaluate one or more assignments of labels to markers. 
       FIG.  5    shows a functional block diagram for one example of a particle analyzer control system, such as an analytics controller  500 , for analyzing and displaying biological events. An analytics controller  500  can be configured to implement a variety of processes for controlling graphic display of biological events. 
     A particle analyzer or sorting system  502  can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometric event data. The particle analyzer  502  can be configured to provide biological event data to the analytics controller  500 . A data communication channel can be included between the particle analyzer or sorting system  502  and the analytics controller  500 . The biological event data can be provided to the analytics controller  500  via the data communication channel. 
     The analytics controller  500  can be configured to receive biological event data from the particle analyzer or sorting system  502 . The biological event data received from the particle analyzer or sorting system  502  can include flow cytometric event data. The analytics controller  500  can be configured to provide a graphical display including a first plot of biological event data to a display device  506 . The analytics controller  500  can be further configured to render a region of interest as a gate around a population of biological event data shown by the display device  506 , overlaid upon the first plot, for example. In some embodiments, the gate can be a logical combination of one or more graphical regions of interest drawn upon a single parameter histogram or bivariate plot. In some embodiments, the display can be used to display particle parameters or saturated detector data. 
     The analytics controller  500  can be further configured to display the biological event data on the display device  506  within the gate differently from other events in the biological event data outside of the gate. For example, the analytics controller  500  can be configured to render the color of biological event data contained within the gate to be distinct from the color of biological event data outside of the gate. The display device  506  can be implemented as a monitor, a tablet computer, a smartphone, or other electronic device configured to present graphical interfaces. 
     The analytics controller  500  can be configured to receive a gate selection signal identifying the gate from a first input device. For example, the first input device can be implemented as a mouse  510 . The mouse  510  can initiate a gate selection signal to the analytics controller  500  identifying the gate to be displayed on or manipulated via the display device  506  (e.g., by clicking on or in the desired gate when the cursor is positioned there). In some implementations, the first device can be implemented as the keyboard  508  or other means for providing an input signal to the analytics controller  500  such as a touchscreen, a stylus, an optical detector, or a voice recognition system. Some input devices can include multiple inputting functions. In such implementations, the inputting functions can each be considered an input device. For example, as shown in  FIG.  5   , the mouse  510  can include a right mouse button and a left mouse button, each of which can generate a triggering event. 
     The triggering event can cause the analytics controller  500  to alter the manner in which the data is displayed, which portions of the data is actually displayed on the display device  506 , and/or provide input to further processing such as selection of a population of interest for particle sorting. 
     In some embodiments, the analytics controller  500  can be configured to detect when gate selection is initiated by the mouse  510 . The analytics controller  500  can be further configured to automatically modify plot visualization to facilitate the gating process. The modification can be based on the specific distribution of biological event data received by the analytics controller  500 . 
     The analytics controller  500  can be connected to a storage device  504 . The storage device  504  can be configured to receive and store biological event data from the analytics controller  500 . The storage device  504  can also be configured to receive and store flow cytometric event data from the analytics controller  500 . The storage device  504  can be further configured to allow retrieval of biological event data, such as flow cytometric event data, by the analytics controller  500 . 
     A display device  506  can be configured to receive display data from the analytics controller  500 . The display data can comprise plots of biological event data and gates outlining sections of the plots. The display device  506  can be further configured to alter the information presented according to input received from the analytics controller  500  in conjunction with input from the particle analyzer  502 , the storage device  504 , the keyboard  508 , and/or the mouse  510 . 
     In some implementations, the analytics controller  500  can generate a user interface to receive example events for sorting. For example, the user interface can include a control for receiving example events or example images. The example events or images or an example gate can be provided prior to collection of event data for a sample, or based on an initial set of events for a portion of the sample. 
       FIG.  6 A  is a schematic drawing of a particle sorter system  600  (e.g., the particle analyzer or sorting system  502 ) in accordance with one embodiment presented herein. In some embodiments, the particle sorter system  600  is a cell sorter system. As shown in  FIG.  6 A , a drop formation transducer  602  (e.g., piezo-oscillator) is coupled to a fluid conduit  601 , which can be coupled to, can include, or can be, a nozzle  603 . Within the fluid conduit  601 , sheath fluid  604  hydrodynamically focuses a sample fluid  606  comprising particles  609  into a moving fluid column  608  (e.g., a stream). Within the moving fluid column  608 , particles  609  (e.g., cells) are lined up in single file to cross a monitored area  611  (e.g., where laser-stream intersect), irradiated by an irradiation source  612  (e.g., a laser). Vibration of the drop formation transducer  602  causes moving fluid column  608  to break into a plurality of drops  610 , some of which contain particles  609 . 
     In operation, a detection station  614  (e.g., an event detector) identifies when a particle of interest (or cell of interest) crosses the monitored area  611 . Detection station  614  feeds into a timing circuit  628 , which in turn feeds into a flash charge circuit  630 . At a drop break off point, informed by a timed drop delay (Δt), a flash charge can be applied to the moving fluid column  608  such that a drop of interest carries a charge. The drop of interest can include one or more particles or cells to be sorted. The charged drop can then be sorted by activating deflection plates (not shown) to deflect the drop into a vessel such as a collection tube or a multi-well or microwell sample plate where a well or microwell can be associated with drops of particular interest. As shown in  FIG.  6 A , the drops can be collected in a drain receptacle  638 . 
     A detection system  616  (e.g., a drop boundary detector) serves to automatically determine the phase of a drop drive signal when a particle of interest passes the monitored area  611 . An exemplary drop boundary detector is described in U.S. Pat. No. 7,679,039, which is incorporated herein by reference in its entirety. The detection system  616  allows the instrument to accurately calculate the place of each detected particle in a drop. The detection system  616  can feed into an amplitude signal  620  and/or phase  618  signal, which in turn feeds (via amplifier  622 ) into an amplitude control circuit  626  and/or frequency control circuit  624 . The amplitude control circuit  626  and/or frequency control circuit  624 , in turn, controls the drop formation transducer  602 . The amplitude control circuit  626  and/or frequency control circuit  624  can be included in a control system. 
     In some implementations, sort electronics (e.g., the detection system  616 , the detection station  614  and a processor  640 ) can be coupled with a memory configured to store the detected events and a sort decision based thereon. The sort decision can be included in the event data for a particle. In some implementations, the detection system  616  and the detection station  614  can be implemented as a single detection unit or communicatively coupled such that an event measurement can be collected by one of the detection system  616  or the detection station  614  and provided to the non-collecting element. 
       FIG.  6 B  is a schematic drawing of a particle sorter system, in accordance with one embodiment presented herein. The particle sorter system  600  shown in  FIG.  6 B , includes deflection plates  652  and  654 . A charge can be applied via a stream-charging wire in a barb. This creates a stream of droplets  610  containing particles  610  for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scatter and fluorescence information. The information for a particle is analyzed such as by sorting electronics or other detection system (not shown in  FIG.  6 B ). The deflection plates  652  and  654  can be independently controlled to attract or repel the charged droplet to guide the droplet toward a destination collection receptacle (e.g., one of  672 ,  674 ,  676 , or  678 ). As shown in  FIG.  6 B , the deflection plates  652  and  654  can be controlled to direct a particle along a first path  662  toward the receptacle  674  or along a second path  668  toward the receptacle  678 . If the particle is not of interest (e.g., does not exhibit scatter or illumination information within a specified sort range), deflection plates may allow the particle to continue along a flow path  664 . Such uncharged droplets may pass into a waste receptacle such as via aspirator  670 . 
     The sorting electronics can be included to initiate collection of measurements, receive fluorescence signals for particles, and determine how to adjust the deflection plates to cause sorting of the particles. Example implementations of the embodiment shown in  FIG.  6 B  include the BD FACSAria™ line of flow cytometers commercially provided by Becton, Dickinson and Company (Franklin Lakes, N.J.). 
     Computer-Controlled Systems 
     Aspects of the present disclosure further include computer-controlled systems, where the systems further include one or more computers for complete automation or partial automation of the methods described herein. In some embodiments, systems include a computer having a computer readable storage medium with a computer program stored thereon, where the computer program when loaded on the computer includes instructions for practicing the subject methods. Computer programs according to certain embodiments include instructions for detecting light with a light detection system from a sample irradiated by a light source in a flow stream, instructions for generating event data signals in response to events detected with the light detection system, instructions for binning the generated event data signals in a plurality of binning windows comprising overlapping data bins and instructions for assessing the plurality of binning windows for outlier events in the generated data signals. 
     In some embodiments, the computer program includes instructions for binning a predetermined number of event data signals in each window. In some embodiments, the computer program includes instructions for sequentially binning the event data signals based on time of event detection by the light detection system. In some embodiments, the computer program includes instructions for binning one or more event data signals into two or more binning windows, such as where the two or more binning windows overlap by a predetermined time frame. For example, the binning windows may overlap by a time frame of from 0.001 μs to 100 μs. 
     In some embodiments, the computer program includes instructions for applying a transformation to each event data signal. In certain embodiments, the transformation is one or more of a centered log-ratio transformation, an additive log-ratio transformation, an isometric log-ratio transformation or a combination thereof. In some embodiments, the computer program includes instructions for calculating a median parameter for each data bin of the overlapping data bins. In some embodiments, the computer program includes instructions for calculating a median or mean fluorescence intensity of events for each data bin. In some embodiments, the computer program includes instructions for calculating a median or mean expression level of events for each data bin. 
     In some embodiments, the computer program includes instructions for assessing the plurality of binning windows for outlier events by comparing the median parameter for each data bin with an outlier threshold. In some instances, the outlier threshold is a median absolute deviation (MAD) outlier threshold. In some instances, the outlier threshold is a median tolerance percentage outlier threshold. In some embodiments, the computer program includes instructions for dynamically calculating the outlier threshold in real-time, such as for dynamically adjusting the outlier threshold in real-time in response to binning of the generated event data signals. In some embodiments, the computer program includes instructions for classifying a data bin as an outlier-containing data bin when the median parameter is determined to be greater than the predetermined outlier threshold. In some instances, the computer program includes instructions for identifying the event data signals of each outlier-containing data bin as being an outlier. 
     In some embodiments, the computer program includes instructions for generating an alert that an outlier is detected when assessing the plurality of data bins for outlier events. In some instances, the computer program includes instructions for generating an alert that there is a change in flow rate in the flow stream of the particle analyzer. In some instances, the computer program includes instructions for generating an alert that there is a clog in the particle analyzer. 
     In certain instances, the computer program includes instructions for measuring flow rate of the flow stream with a flow rate sensor. In some instances, the computer program includes instructions for comparing the measured flow rate of the flow stream with an expected flow rate of the flow stream and algorithm for reassessing the data bins classified as being an outlier-containing data bin based on the compared flow rate of the flow stream. In some embodiments, the computer program includes instructions for reclassifying the data bin based on the compared flow rate of the flow stream. In some embodiments, the computer program includes instructions for not generating an alert when the measured flow rate is within a predetermined threshold of the expected flow rate, such as where the predetermined threshold is within 5% or less of the expected flow rate. In some embodiments, the computer program includes instructions for comparing the median fluorescence intensity of the plurality of data bins with one or more parameters of the light detection system and algorithm for reassessing the data bins classified as being an outlier-containing data bin based on the parameter of the light detection system (e.g., photodetector voltage of one or more of the photodetectors of the light detection system). In certain embodiments, the computer program includes instructions for performing a calibration of the particle analyzer in response to one or more data bins being classified as outlier-containing data bins. In certain instances, computer program instructions for calibration of the particle analyzer include instructions for detecting light with the light detection system from a standard composition irradiated by the light source in the flow stream, instructions for generating event data signals in response to events detected with the light detection system, instructions for binning the generated event data signals into a plurality of binning windows having overlapping data bins, instructions for assessing the generated data signals in the plurality of binning windows and instructions for determining that no outlier-containing data bins are present. 
     In embodiments, the system includes an input module, a processing module and an output module. The subject systems may include both hardware and software components, where the hardware components may take the form of one or more platforms, e.g., in the form of servers, such that the functional elements, i.e., those elements of the system that carry out specific tasks (such as managing input and output of information, processing information, etc.) of the system may be carried out by the execution of software applications on and across the one or more computer platforms represented of the system. 
     Systems may include a display and operator input device. Operator input devices may, for example, be a keyboard, mouse, or the like. The processing module includes a processor which has access to a memory having instructions stored thereon for performing the steps of the subject methods. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, memory storage devices, and input-output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or it may be one of other processors that are or will become available. The processor executes the operating system and the operating system interfaces with firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages, such as Java, Perl, C++, other high level or low level languages, as well as combinations thereof, as is known in the art. The operating system, typically in cooperation with the processor, coordinates and executes functions of the other components of the computer. The operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, processors include analog electronics which allows the user to manually align a light source with the flow stream based on the first and second light signals. In some embodiments, the processor includes analog electronics which provide feedback control, such as for example negative feedback control. 
     The system memory may be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic medium such as a resident hard disk or tape, an optical medium such as a read and write compact disc, flash memory devices, or other memory storage device. The memory storage device may be any of a variety of known or future devices, including a compact disk drive, a tape drive, a removable hard disk drive, or a diskette drive. Such types of memory storage devices typically read from, and/or write to, a program storage medium (not shown) such as, respectively, a compact disk, magnetic tape, removable hard disk, or floppy diskette. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store a computer software program and/or data. Computer software programs, also called computer control logic, typically are stored in system memory and/or the program storage device used in conjunction with the memory storage device. 
     In some embodiments, a computer program product is described comprising a computer usable medium having control logic (computer software program, including program code) stored therein. The control logic, when executed by the processor the computer, causes the processor to perform functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementation of the hardware state machine so as to perform the functions described herein will be apparent to those skilled in the relevant arts. 
     Memory may be any suitable device in which the processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including magnetic or optical disks or tape or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor suitably programmed from a computer readable medium carrying necessary program code. Programming can be provided remotely to processor through a communication channel, or previously saved in a computer program product such as memory or some other portable or fixed computer readable storage medium using any of those devices in connection with memory. For example, a magnetic or optical disk may carry the programming, and can be read by a disk writer/reader. Systems of the invention also include programming, e.g., in the form of computer program products, algorithms for use in practicing the methods as described above. Programming according to the present invention can be recorded on computer readable media, e.g., any medium that can be read and accessed directly by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; portable flash drive; and hybrids of these categories such as magnetic/optical storage media. 
     The processor may also have access to a communication channel to communicate with a user at a remote location. By remote location is meant the user is not directly in contact with the system and relays input information to an input manager from an external device, such as a a computer connected to a Wide Area Network (“WAN”), telephone network, satellite network, or any other suitable communication channel, including a mobile telephone (i.e., smartphone). 
     In some embodiments, systems according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and/or transmitter for communicating with a network and/or another device. The communication interface can be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., Radio-Frequency Identification (RFID), Zigbee communication protocols, WiFi, infrared, wireless Universal Serial Bus (USB), Ultra Wide Band (UWB), Bluetooth® communication protocols, and cellular communication, such as code division multiple access (CDMA) or Global System for Mobile communications (GSM). 
     In one embodiment, the communication interface is configured to include one or more communication ports, e.g., physical ports or interfaces such as a USB port, an RS-232 port, or any other suitable electrical connection port to allow data communication between the subject systems and other external devices such as a computer terminal (for example, at a physician&#39;s office or in hospital environment) that is configured for similar complementary data communication. 
     In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the subject systems to communicate with other devices such as computer terminals and/or networks, communication enabled mobile telephones, personal digital assistants, or any other communication devices which the user may use in conjunction. 
     In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing Internet Protocol (IP) through a cell phone network, Short Message Service (SMS), wireless connection to a personal computer (PC) on a Local Area Network (LAN) which is connected to the internet, or WiFi connection to the internet at a WiFi hotspot. 
     In one embodiment, the subject systems are configured to wirelessly communicate with a server device via the communication interface, e.g., using a common standard such as 802.11 or Bluetooth® RF protocol, or an IrDA infrared protocol. The server device may be another portable device, such as a smart phone, Personal Digital Assistant (PDA) or notebook computer; or a larger device such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), as well as an input device, such as buttons, a keyboard, mouse or touch-screen. 
     In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored in the subject systems, e.g., in an optional data storage unit, with a network or server device using one or more of the communication protocols and/or mechanisms described above. 
     Output controllers may include controllers for any of a variety of known display devices for presenting information to a user, whether a human or a machine, whether local or remote. If one of the display devices provides visual information, this information typically may be logically and/or physically organized as an array of picture elements. A graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing graphical input and output interfaces between the system and a user, and for processing user inputs. The functional elements of the computer may communicate with each other via system bus. Some of these communications may be accomplished in alternative embodiments using network or other types of remote communications. The output manager may also provide information generated by the processing module to a user at a remote location, e.g., over the Internet, phone or satellite network, in accordance with known techniques. The presentation of data by the output manager may be implemented in accordance with a variety of known techniques. As some examples, data may include SQL, HTML or XML documents, email or other files, or data in other forms. The data may include Internet URL addresses so that a user may retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject systems may be any type of known computer platform or a type to be developed in the future, although they typically will be of a class of computer commonly referred to as servers. However, they may also be a main-frame computer, a workstation, or other computer type. They may be connected via any known or future type of cabling or other communication system including wireless systems, either networked or otherwise. They may be co-located or they may be physically separated. Various operating systems may be employed on any of the computer platforms, possibly depending on the type and/or make of computer platform chosen. Appropriate operating systems include Windows, iOS, Oracle Solaris, Linux, IBM i, Unix, and others. 
       FIG.  7    depicts a general architecture of an example computing device  700  according to certain embodiments. The general architecture of the computing device  700  depicted in  FIG.  7    includes an arrangement of computer hardware and software components. The computing device  700  may include many more (or fewer) elements than those shown in  FIG.  7   . It is not necessary, however, that all of these generally conventional elements be shown in order to provide an enabling disclosure. As illustrated, the computing device  700  includes a processing unit  710 , a network interface  720 , a computer readable medium drive  730 , an input/output device interface  740 , a display  750 , and an input device  760 , all of which may communicate with one another by way of a communication bus. The network interface  720  may provide connectivity to one or more networks or computing systems. The processing unit  710  may thus receive information and instructions from other computing systems or services via a network. The processing unit  710  may also communicate to and from memory  770  and further provide output information for an optional display  750  via the input/output device interface  740 . The input/output device interface  740  may also accept input from the optional input device  760 , such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device. 
     The memory  770  may contain computer program instructions (grouped as modules or components in some embodiments) that the processing unit  710  executes in order to implement one or more embodiments. The memory  770  generally includes RAM, ROM and/or other persistent, auxiliary or non-transitory computer-readable media. The memory  770  may store an operating system  772  that provides computer program instructions for use by the processing unit  710  in the general administration and operation of the computing device  700 . The memory  770  may further include computer program instructions and other information for implementing aspects of the present disclosure. 
     Non-Transitory Computer-Readable Storage Medium 
     Aspects of the present disclosure further include non-transitory computer readable storage mediums having instructions for practicing the subject methods. Computer readable storage mediums may be employed on one or more computers for complete automation or partial automation of a system for practicing methods described herein. In certain embodiments, instructions in accordance with the method described herein can be coded onto a computer-readable medium in the form of “programming”, where the term “computer readable medium” as used herein refers to any non-transitory storage medium that participates in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include a floppy disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD-R, magnetic tape, non-volatile memory card, ROM, DVD-ROM, Blue-ray disk, solid state disk, and network attached storage (NAS), whether or not such devices are internal or external to the computer. A file containing information can be “stored” on computer readable medium, where “storing” means recording information such that it is accessible and retrievable at a later date by a computer. The computer-implemented method described herein can be executed using programming that can be written in one or more of any number of computer programming languages. Such languages include, for example, Python, Java, Java Script, C, C#, C++, Go, R, Swift, PHP, as well as any many others. 
     Non-transitory computer readable storage medium according to certain embodiments includes instructions having algorithm for detecting light with a light detection system from a sample irradiated by a light source in a flow stream, algorithm for generating event data signals in response to events detected with the light detection system, algorithm for binning the generated event data signals in a plurality of binning windows comprising overlapping data bins and algorithm for assessing the plurality of binning windows for outlier events in the generated data signals. In some embodiments, the non-transitory computer readable storage medium includes algorithm for binning a predetermined number of event data signals in each window. In some embodiments, non-transitory computer readable storage medium includes algorithm for sequentially binning the event data signals based on time of event detection by the light detection system. In some embodiments, the non-transitory computer readable storage medium includes algorithm for binning one or more event data signals into two or more binning windows, such as where the two or more binning windows overlap by a predetermined time frame. For example, the binning windows may overlap by a time frame of from 0.001 μs to 100 μs. 
     In some embodiments, the non-transitory computer readable storage medium includes algorithm for applying a transformation to each event data signal. In certain embodiments, the transformation is one or more of a centered log-ratio transformation, an additive log-ratio transformation, an isometric log-ratio transformation or a combination thereof. In some embodiments, the non-transitory computer readable storage medium includes algorithm for calculating a median parameter for each data bin of the overlapping data bins. In some embodiments, the non-transitory computer readable storage medium includes algorithm for calculating a median or mean fluorescence intensity of events for each data bin. In some embodiments, the non-transitory computer readable storage medium includes algorithm for calculating a median or mean expression level of events for each data bin. 
     In some embodiments, the non-transitory computer readable storage medium includes algorithm for assessing the plurality of binning windows for outlier events by comparing the median parameter for each data bin with an outlier threshold. In some instances, the outlier threshold is a median absolute deviation (MAD) outlier threshold. In some instances, the outlier threshold is a median tolerance percentage outlier threshold. In some embodiments, the non-transitory computer readable storage medium includes algorithm for dynamically calculating the outlier threshold in real-time, such as for dynamically adjusting the outlier threshold in real-time in response to binning of the generated event data signals. In some embodiments, the non-transitory computer readable storage medium includes algorithm for classifying a data bin as an outlier-containing data bin when the median parameter is determined to be greater than the predetermined outlier threshold. In some instances, the non-transitory computer readable storage medium includes algorithm for identifying the event data signals of each outlier-containing data bin as being an outlier. 
     In some embodiments, the non-transitory computer readable storage medium comprises algorithm for generating an alert that an outlier is detected when assessing the plurality of data bins for outlier events. In some instances, the non-transitory computer readable storage medium includes algorithm for generating an alert that there is a change in flow rate in the flow stream of the particle analyzer. In some instances, the non-transitory computer readable storage medium includes algorithm for generating an alert that there is a clog in the particle analyzer. 
     In certain instances, the non-transitory computer readable storage medium includes algorithm for measuring flow rate of the flow stream with a flow rate sensor. In some instances, the non-transitory computer readable storage medium includes algorithm for comparing the measured flow rate of the flow stream with an expected flow rate of the flow stream and algorithm for reassessing the data bins classified as being an outlier-containing data bin based on the compared flow rate of the flow stream. In some embodiments, the non-transitory computer readable storage medium includes algorithm for reclassifying the data bin based on the compared flow rate of the flow stream. In some embodiments, the non-transitory computer readable storage medium includes algorithm for not generating an alert when the measured flow rate is within a predetermined threshold of the expected flow rate, such as where the predetermined threshold is within 5% or less of the expected flow rate. In some embodiments, the non-transitory computer readable storage medium includes algorithm for comparing the median fluorescence intensity of the plurality of data bins with one or more parameters of the light detection system and algorithm for reassessing the data bins classified as being an outlier-containing data bin based on the parameter of the light detection system (e.g., photodetector voltage of one or more of the photodetectors of the light detection system). In certain embodiments, the non-transitory computer readable storage medium includes algorithm for performing a calibration of the particle analyzer in response to one or more data bins being classified as outlier-containing data bins. In certain instances, algorithm for calibration of the particle analyzer include algorithm for detecting light with the light detection system from a standard composition irradiated by the light source in the flow stream, algorithm for generating event data signals in response to events detected with the light detection system algorithm for binning the generated event data signals into a plurality of binning windows having overlapping data bins, algorithm for assessing the generated data signals in the plurality of binning windows and algorithm for determining that no outlier-containing data bins are present. 
     The non-transitory computer readable storage medium may be employed on one or more computer systems having a display and operator input device. Operator input devices may, for example, be a keyboard, mouse, or the like. The processing module includes a processor which has access to a memory having instructions stored thereon for performing the steps of the subject methods. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, memory storage devices, and input-output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or it may be one of other processors that are or will become available. The processor executes the operating system and the operating system interfaces with firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages, such as those mentioned above, other high level or low level languages, as well as combinations thereof, as is known in the art. The operating system, typically in cooperation with the processor, coordinates and executes functions of the other components of the computer. The operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. 
     Kits 
     Aspects of the present disclosure further include kits, where kits include one or more of the components of light detection systems described herein. In some embodiments, kits include an integrated circuit device (e.g., an FPGA) a wavelength separator (e.g., a spectrometer), plurality of photodetectors, one or more electronic components such as transimpedance amplifiers, differential amplifiers, electronic switch components and programming for the subject systems, such as in the form of a computer readable medium (e.g., flash drive, USB storage, compact disk, DVD, Blu-ray disk, etc.) or instructions for downloading the programming from an internet web protocol or cloud server. Kits may also include an optical adjustment component, such as lenses, mirrors, filters, fiber optics, pinholes, slits, collimating protocols and combinations thereof. 
     Kits may further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, and the like. Yet another form of these instructions is a computer readable medium, e.g., diskette, compact disk (CD), portable flash drive, and the like, on which the information has been recorded. Yet another form of these instructions that may be present is a website address which may be used via the internet to access the information at a removed site. 
     Utility 
     The subject methods, systems and computer systems find use in a variety of applications where it is desirable to optimize particle identification, characterization and sorting. The subject methods, systems, and computer readable storage medium also find use in a variety of different applications where it is desirable to accurately and conveniently sort droplets with a flow cytometer. The present disclosure also finds use in automating a flow cytometer so as to provide for fast, reliable systems for characterizing and sorting cells from a biological sample. Embodiments of the present disclosure find use where minimizing the amount of reliance on human input and adjustments to the system are desired, such as in research and high throughput laboratory testing. The present disclosure also finds use where it is desirable to provide a flow cytometer with improved cell sorting accuracy, enhanced particle collection, particle charging efficiency, more accurate particle charging and enhanced particle deflection during cell sorting. In embodiments, the present disclosure reduces the need for user input or manual adjustment of the flow cytometer or between sample analysis with the flow cytometer. 
     The present disclosure also finds use in applications where cells prepared from a biological sample may be desired for research, laboratory testing or for use in therapy. In some embodiments, the subject methods and devices may facilitate the obtaining individual cells prepared from a target fluidic or tissue biological sample. For example, the subject methods and systems facilitate obtaining cells from fluidic or tissue samples to be used as a research or diagnostic specimen for diseases such as cancer. Likewise, the subject methods and systems facilitate obtaining cells from fluidic or tissue samples to be used in therapy. Methods and devices of the present disclosure allow for separating and collecting cells from a biological sample (e.g., organ, tissue, tissue fragment, fluid) with enhanced efficiency and low cost as compared to traditional flow cytometry systems. 
     Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it is readily apparent to those of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims. 
     Accordingly, the preceding merely illustrates the principles of the invention. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. 
     The scope of the present invention, therefore, is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of present invention is embodied by the appended claims. In the claims, 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) is expressly defined as being invoked for a limitation in the claim only when the exact phrase “means for” or the exact phrase “step for” is recited at the beginning of such limitation in the claim; if such exact phrase is not used in a limitation in the claim, then 35 U.S.C. § 112 (f) or 35 U.S.C. § 112(6) is not invoked.