Abstract:
A computerized method and apparatus for analyzing numerical data pertaining to a sample assay comprising at least one biological sample, with the data including a set of data pertaining to each respective sample, and each set of data including a plurality of values each representing a condition of the respective sample at a point in time. The method and apparatus assigns a respective numerical value to each of the data values, corrects the data values by removing an additive background value from each of the data values, computes a single metric for each patient and compares that values to two known reference values to determine the genotype.

Description:
CROSS-REFERENCE TO RELATED PATENT AND APPLICATION  
       [0001]    This application is a utility patent application claiming benefit to previously filed U.S. Provisional Patent Application Serial No. 60/398,601 filed Jul. 26, 2002 and titled “Computerized Method and Apparatus for Analyzing Readings of Nucleic Assays”. Related subject matter is disclosed in a co-pending U.S. patent application of Andrew M. Kuhn, Tobin Hellyer, and Richard L. Moore entitled “Computerized Method and Apparatus for Analyzing Readings of Nucleic Acid Assays”, Ser. No. 09/574,031 and in U.S. Pat. No. 6,043,880 of Jeffrey P. Andrews, Christian V. O&#39;Keefe, Brian G. Scrivens, Willard C. Pope, Timothy Hansen and Frank L. Failing entitled “Automated Optical Reader for Nucleic Acid Assays”, the entire contents of said application and patent being expressly incorporated herein by reference. 
     
    
     
       BACKGROUND OF THE INVENTION  
         [0002]    1. Field of the Invention  
           [0003]    The present invention relates generally to a computerized method and apparatus for analyzing sets of readings taken of respective samples in a biological assay, such as a nucleic acid assay, to determine which samples possess a certain predetermined characteristic. More particularly, the present invention relates to a computerized method and apparatus that acquires optical readings of a biological sample taken at different times during a reading period, corrects for an additive background value present in the readings, and categorizes the corrected readings into one of several genetic variations (e.g., mutant, wild-type, etc.)  
           [0004]    2. Description of the Related Art  
           [0005]    Cataloging of the human genome has led to the discovery of millions of DNA sequence variations in humans, many of which are defined by a single nucleotide difference. In many cases, these single nucleotide polymorphisms (SNPs) can be associated with human diseases and conditions so that genotyping of patients can aid in the diagnosis and treatment of many conditions.  
           [0006]    The determination of a patient&#39;s genotype can be accomplished in various ways. Sequencing of a patient&#39;s DNA is a relatively expensive and time-consuming process. Other methods, such as DNA probes, can identify the presence of specific target sequences quickly and reliably. A test for the presence of a particular sequence of DNA can be completed in an hour or less using DNA probe technology.  
           [0007]    In the use of DNA probes for clinical diagnostic purposes, a nucleic acid amplification reaction is usually carried out to multiply the target nucleic acid into many copies or amplicons. Examples of nucleic acid amplification reactions include strand displacement amplification (SDA) and polymerase chain reaction (PCR). Unlike PCR, SDA is an isothermal process that does not require any external control over the progress of the reaction that causes amplification. Detection of the nucleic acid amplicons can be carried out in several ways, all involving hybridization (binding) between the target DNA and specific probes.  
           [0008]    Many common DNA probe detection methods involve the use of fluorescent dyes. One known detection method is fluorescence energy transfer. In this method, a detector probe is labeled with both a fluorescent dye that emits light when excited by an outside source and a quencher that suppresses the emission of light from the fluorescent dye in its native state. When DNA amplification occurs, the fluorescently labeled probe binds to the resulting amplicons, undergoing a change in secondary structure in the process that separates the fluor from the quencher molecule, thereby allowing fluorescence to be detected. The change in fluorescence is taken as an indication that the targeted DNA sequence is present in the sample.  
           [0009]    Several types of optical readers or scanners exist that are capable of exciting fluid samples with light and then detecting any light that is generated by the fluid samples in response to the excitation. For example, an X-Y plate scanning apparatus, such as the CytoFluor Series 4000 made by PerSeptive Biosystems, is capable of scanning a plurality of fluid samples stored in an array of microwells. The apparatus includes a scanning head for emitting light towards a particular sample and for detecting light generated from that sample. During operation, the optical head is moved to a suitable position with respect to one of the sample wells. A light-emitting device is activated to transmit light through the optical head toward the sample well. If the fluid sample in the well fluoresces in response to the emitted light, the fluorescent light is received by the scanning head and transmitted to an optical detector. The detected light is converted by the optical detector into an electrical signal, the magnitude of which is indicative of the intensity of the detected light. This electrical signal is processed by a computer to determine whether the target DNA is present or absent in the fluid sample based on the magnitude of the electrical signal. Each well in the microwell tray (e.g., 96 microwells total) can be read in this manner.  
           [0010]    Another more efficient and versatile sample well reading apparatus known as the BDProbeTec® ET system manufactured by Becton, Dickinson and Company is described in the above-referenced U.S. Pat. No. 6,043,880. In that system, a microwell array, such as the standard microwell array having 12 columns of eight microwells each (96 microwells total), is placed in a moveable stage that is driven past a scanning bar. The scanning bar includes eight light emitting/detecting ports that are spaced from each other at a distance substantially corresponding to the distance at which the microwells in each column are spaced from each other. Hence, an entire column of sample microwells can be read with each movement of the stage.  
           [0011]    As described in more detail below, the stage is moved back and forth over the light sensing bar, so that a plurality of readings of each sample microwell are taken at desired intervals. In one example, readings of each microwell are taken at one-minute intervals for a period of one hour. Accordingly, 60 readings of each microwell are taken during a well reading period. These readings are then used to determine which samples contain the particular targeted disease or condition.  
           [0012]    Several methods are known for analyzing the sample well reading data to determine whether a sample contained in the sample well includes the targeted genetic sequence(s). For instance, as discussed above, a nucleic acid amplification reaction will cause the target nucleic acid to multiply into many amplicons. The fluorescently-labeled probe that binds to the amplicons will fluoresce when excited with light. As the number of amplicons increases over time while the nucleic acid amplification reaction progresses, the amount of fluorescence correspondingly increases. Accordingly, after a predetermined period of time has elapsed (e.g., 1 hour), the magnitude of fluorescence emission from a sample having the targeted sequence (a “positive”) is much greater then the magnitude of fluorescence emission from a sample not having the targeted sequence (a “negative”). The magnitude of fluorescence of a sample without the targeted sequence essentially does not change throughout the duration of the test.  
           [0013]    Although the embodiments of this invention have been described in terms of increasing signal as amplification increases, there are similar systems where signal (fluorescence, etc.) decreases as amplification proceeds. Those skilled in the art will appreciate that such modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this invention. Accordingly, all such modifications are intended to be included within the scope of this invention.  
           [0014]    If present in the sample, the two target sequences, such as alleles A and B, are amplified through this procedure (in the same or separate microwells). The magnitude of amplification of each sequence could be compared to the other to determine the patient&#39;s genetic makeup. If the magnitude of fluorescence emission is large for allele A sequence and small for allele B, the patient&#39;s genotype would be homozygous for allele A. Conversely, if the magnitude of fluorescence emission is large for allele B and small for allele A, the patient would be homozygous for allele B. If both sequences showed significant fluorescence emissions, both sequences are present and the patient is heterozygous for alleles A and B.  
           [0015]    Therefore, the value of the last reading taken for each sequence can be compared to categorize the sample into one of several characteristics (e.g., allele A, allele B, heterozygous for alleles A and B). If neither sequence shows significant fluorescence emissions, one or both of the amplifications was inhibited by factors unrelated to the presence of the target sequences.  
           [0016]    Although this “endpoint detection” method can generally be effective in identifying the presence of a target DNA sequence, it is not uncommon for this method to incorrectly identify a “negative” sample as being “positive” for the sequence or vice versa. That is, the accuracy of the value of any-individual sample reading can be adversely effected by factors such as a bubble forming in the sample, obstruction of excitation light and/or fluorescence emission from the sample because of the presence of debris on the optical reader, and so on. Accordingly, if the final reading of a particular sample is erroneous and only that reading is analyzed, the likelihood of obtaining an erroneous result is high.  
           [0017]    To avoid these drawbacks, other methods have been developed. In one method, the overall change in the magnitudes of sample readings is calculated and compared to a known value having a magnitude indicative of a positive result. Accordingly, if the magnitude of change is greater than the predetermined value, the sample is identified as a positive sample containing the targeted sequence. On the other hand, if the magnitude of change is less than the predetermined value, the sample is identified as not containing the targeted sequence.  
           [0018]    Another known method is the acceleration index method, which measures incremental changes in the sample readings and compares those changes to a predetermined value. Although this method is generally effective, the accuracy of its results is susceptible to errors present in the individual readings.  
           [0019]    Accordingly, a continuing need exists for a method and apparatus to analyze data representative of readings taken of sample wells in order to classify the sample into one of a variety of genetic variations.  
         SUMMARY OF THE INVENTION  
         [0020]    An object of the present invention is to provide a method and apparatus for accurately interpreting the values of data obtained from taking readings of a biological sample to ascertain the particular genotype in the sample based on the data values.  
           [0021]    Another object of the invention is to provide a method and apparatus for use with an optical sample well reader, which accurately interprets data representing magnitudes of fluorescence emissions detected from the sample at predetermined periods of time, to ascertain the particular genotype in the sample.  
           [0022]    A further object of the invention is to provide a method and apparatus for analyzing data obtained from reading a biological sample contained in a sample well, and without using complicated arithmetic computations, correcting for errors in the data that could adversely affect the results of the analysis.  
           [0023]    These and other objects of the invention are substantially achieved by providing a computerized method and apparatus for analyzing numerical data pertaining to a sample assay comprising at least one biological sample, with the data including a set of data pertaining to each respective sample, and each set of data including a plurality of values each representing a condition of the respective sample at a point in time. The method and apparatus assigns a respective numerical value to each of the data values, removes an additive background value from each of the data values to produce corrected data values, compares the amplification results from two nucleic acid sequences to differentiate sequence variations, and controls the system to indicate the patient genotype based on a result of the comparison. Additionally, prior to differentiating sequence variation, filtering, normalizing and other correcting operations can be performed on the data to correct extraneous values in the data that could adversely affect the accuracy of the results.  
           [0024]    The method and apparatus can perform many of the above functions by representing the plurality of data values for each target sequence as points on a graph having a vertical axis representing the magnitudes of the values and a horizontal axis representing a period of time during which readings of the sample were taken to obtain said plurality of data values, correcting the data values from each sequence to eliminate an additive background value present in each of the data values to produce a corrected plot of points on the graph for each target sequence, with each of the points for each sequence of the corrected plot of points representing a magnitude of a corresponding one of the values. Another plurality of values is created that describes the relative magnitudes of the pluralities for each target sequence (e.g., allele A or allele B, mutant or wild-type) by taking logarithm of the ratio of allele A to allele B data values. This plurality of values is then summarized into a single metric for each patient sample by the most likely value in plurality of values based on a probability density estimate. This most likely value is compared to two known reference values to determine the genotype (e.g., allele A, allele B or heterozygous). For example, if the most likely value is between the two reference values, the sample may be determined to be heterozygous. If the value were above the larger (smaller) reference value, the sample would be allele A (allele B). The configuration of the reference values would depend on what target sequences are associated with each amplification curve. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0025]    The and other objects of the invention will be more readily appreciated from the following detailed description when read in conjunction with the accompanying drawings in which:  
         [0026]    [0026]FIG. 1 is a perspective view of an apparatus for optically reading sample wells of a sample well array, which employs an embodiment of the present invention to interpret the sample well readings;  
         [0027]    [0027]FIG. 2 is an exploded perspective view of a sample well tray for use in the sample well reading apparatus shown in FIG. 1;  
         [0028]    [0028]FIG. 3 is a detailed perspective view of a stage assembly employed in the apparatus shown in FIG. 1 for receiving and conveying a sample well tray assembly shown in FIG. 2;  
         [0029]    [0029]FIG. 4 is a diagram illustrating the layout of a light sensor bar and corresponding fiber optic cables, light emitting diodes and light detector employed in the apparatus shown in FIG. 1, in relation to a sample well tray being conveyed past the light sensor bar by the stage assembly shown in FIG. 3;  
         [0030]    [0030]FIG. 5 is a graph illustrating values representing the magnitudes of fluorescent emissions detected from a sample well of the sample well tray shown in FIG. 2 by the apparatus shown in FIG. 1, with the values being plotted as a function of the times at which their corresponding fluorescent emissions were detected;  
         [0031]    [0031]FIG. 6 is a flowchart showing steps of a method for normalizing, filtering, adjusting and interpreting the data in the graph shown in FIG. 5 according to an embodiment of the present invention;  
         [0032]    [0032]FIG. 7 is a flowchart showing steps of the dark correction processing step of the flowchart shown in FIG. 6;  
         [0033]    [0033]FIG. 8 is a flowchart showing steps of the dynamic normalization processing step of the flowchart shown in FIG. 6;  
         [0034]    [0034]FIG. 9 is a graph that results after performing the dark correction, impulse noise filter, and dynamic normalization steps in the flowchart show in FIG. 6 on the graph shown in FIG. 5;  
         [0035]    [0035]FIG. 10 is a flowchart showing steps of the step location and removal processing step of the flowchart show in FIG. 6;  
         [0036]    [0036]FIG. 11 is a graph that results from performing the step location and repair steps of the flowchart shown in FIG. 6 on the graph shown in FIG. 9;  
         [0037]    [0037]FIG. 12 is a flowchart showing steps of the well present determination step of the flowchart shown in FIG. 6;  
         [0038]    [0038]FIG. 13 is a flowchart showing steps of the background correction step of the flowchart shown in FIG. 6;  
         [0039]    [0039]FIG. 14 is a graph that results from performing the background correction step of the flowchart shown in FIG. 6 on the graph in FIG. 11;  
         [0040]    [0040]FIG. 15 is a flowchart showing steps of calculating the natural logarithm of amplification ratios;  
         [0041]    [0041]FIG. 16 is a flowchart showing the steps of density estimation for the log ratio values and determining the ratio value corresponding to the point of maximum density;  
         [0042]    [0042]FIG. 17 is a flowchart showing steps of assigning a final result to the sample using the maximum density value(s);  
         [0043]    [0043]FIG. 18 is a graph of mutant and wild-type amplifications for the example;  
         [0044]    [0044]FIG. 19 is a graph of log ratio data values over time for the example;  
         [0045]    [0045]FIG. 20 is a histogram of log ratio data values and probability density curve for the example; and  
         [0046]    [0046]FIG. 21 is a graph demonstrating the most likely value for the example. 
     
    
     DETAILED DESCRIPTION  
       [0047]    A well reading apparatus  100  according to an embodiment of the present invention is shown in FIG. 1. The apparatus  100  includes a keypad  102 , which enables an operator to enter data and thus control operation of the apparatus  100 . The apparatus  100  further includes a display screen  104 , such as an LCD display screen or the like, for displaying “soft keys” that allow the operator to enter data and control operation of the apparatus  100 , and for displaying information in response to the operator&#39;s commands, as well as data pertaining to the scanning information gathered from the samples in the manner described below. The apparatus also includes a storage device such as a disk drive  106  for storing data generated by the apparatus  100  or from which the apparatus can read data.  
         [0048]    The apparatus  100  further includes a door  108  that allows access to a stage assembly  110  and into which can be loaded a sample tray assembly  112 . As shown in FIG. 2, a sample tray assembly  112  includes a tray  114  into which is loaded a microwell array  116 , which can be a standard microwell array having 96 individual microwells  118  arranged in 12 columns of 8 microwells each. The tray  114  has openings  120 , which pass entirely through the tray and are arranged in 12 columns of eight microwells each, such that each opening  120  accommodates a microwell  118  of microwell array  116 . After the samples have been placed into the microwells  118 , a cover  122  can be secured over microwells  118  to retain each fluid sample in its respective microwell  118 . Further details of the sample tray assembly  112  and of sample collection techniques are described in the aforementioned U.S. Pat. No. 6,043,880.  
         [0049]    Each microwell can include two types of detector probes, as described below, for identifying a particular disease or for characterizing a genetic locus with one probe being specific for each allele. If the microwell array  116  is to be used to test for a particular disease or condition in each patient sample, the microwells  118  are arranged in groups of microwells and a fluid sample from a particular patient is placed in the group of wells corresponding to the particular patient.  
         [0050]    Some of the 96 microwells  118  in the microwell array  116  can be designated as control sample wells for a particular genotype, with one of the control sample wells containing a homozygous allele A sample, the other control well containing a control homozygous allele B sample, and a third microwell containing a heterozygous mixture of both alleles A and B. Also, additional microwells  118  that do not contain either allele can be designated as negative control microwells. Accordingly, in this example, a maximum of 92 patient samples can be tested for each microwell array  116  arranged in this manner (i.e., 92 samples plus 1 allele A control, 1 allele B control, 1 heterozygous control containing a mixture of alleles A and B and 1 negative control).  
         [0051]    Although the above description focuses on testing of patient samples, a similar approach can be used to test haploid organisms such as bacteria for genetic mutations. In this case, each microwell is used to discriminate the two alleles at a particular locus while appropriate positive and negative controls are also included for each genetic variant. Analysis of the fluorescent readings from the samples is similar regardless of the source of nucleic acid target.  
         [0052]    After the patient fluid samples have been placed in the appropriate microwells  118  of the microwell array  116  in sample tray assembly  112 , the sample tray assembly  112  is loaded into the stage assembly  110  of the well reading apparatus  100 . The stage assembly  110  is shown in more detail in FIG. 3. Specifically the stage assembly  110  includes an opening  124  for receiving a sample tray assembly  112 . The stage assembly  110  further includes a plurality of control wells  126  that are used in calibrating and verifying the integrity of the reading components of the well reading apparatus  100 . Among these control wells  126  is a column of eight calibration wells  127 , the purpose of which is described in more detail below. The stage assembly  110  further includes a cover  128  that covers the sample tray assembly  112  and control wells  126  when the sample tray assembly  112  has been loaded into the opening  124  and sample reading is to begin. Further details of the stage assembly  110  are described in the above-referenced U.S. Pat. No. 6,043,880.  
         [0053]    To read the samples contained in the microwells  118  of a sample tray assembly  112  that has been loaded into the stage assembly  110 , the stage assembly  110  is conveyed past a light sensing bar  130  as shown in FIG. 4. The light sensor bar  130  includes a plurality of light emitting/detecting ports  132 . The light emitting/detecting ports  132  are controlled to emit light towards a column of eight microwells  118  when the stage assembly  110  positions those microwells  118  over the light emitting/detecting ports, and to detect fluorescent light being emitted from the samples contained in those microwells  118 . In this example, the light sensor bar  130  includes eight light emitting/detecting ports  132  that are arranged to substantially align with the eight microwells  118  in a column of the microwell array  116  when that column of microwells  118  is positioned over the light emitting/detecting ports  132 .  
         [0054]    The light emitting/detecting ports  132  are coupled by respective fiber optic cables  134  to respective light emitting devices  136 , such as LEDs or the like. The light emitting/detecting ports  132  are further coupled by respective fiber optic cables  138  to an optical detector  140 , such as a photo multiplier tube or the like. Further details of the light sensor bar  130  and related components, as well as the manner in which the stage assembly  110  is conveyed past the light sensor bar  130  for reading the samples contained in the microwells  118 , are described in the above-referenced U.S. Pat. No. 6,043,880.  
         [0055]    In general, one reading for each microwell is taken at a particular interval in time, and additional readings of each microwell are taken at respective intervals in time for a predetermined duration of time. In this example, one microwell reading is obtained for each microwell  118  at approximately one-minute intervals for a period of one hour. One reading of each of the calibration wells  127 , as well as one “dark” reading for each of the light emitting/detecting ports  132 , is taken at each one-minute interval. Accordingly, 60 microwell readings of each microwell  118 , as well as 60 readings of each calibration well  127  and  60  dark readings, are obtained during the one-hour period.  
         [0056]    Additionally, this embodiment of the well reading apparatus has two independent optical systems, one for FAM dyes and one for ROX dyes. Each optical system contains eight optical channels, one for each row of a standard 96-well microtiter plate. An optical channel consists of a source LED, excitation filters, and a bifurcated fiber optic bundle that integrates source fibers and emission fibers into a single read position. All optical channels within one optical system terminate in a common set of emission filters and a photo multiplier tube (PMT). Each bifurcated fiber optic bundle couples light from the source LED to a position on the read head that interrogates a single well within a row of the microtiter plate  114 . The integrated ends of the eight optical fiber bundles for each optical system are attached to their respective read head that are positioned under a moving stage  110 . This configuration allows the row position to be selected by activating the appropriate LED, and the column position determined by moving the stage  110 . During operation, if fluid sample fluoresces in response to the emitted source light, the light produced by the fluorescence is received by the integrated end of the optical fiber and is transmitted through the second optical fiber to the PMT. The detected light is converted by the PMT into an electrical current, the magnitude of which is indicative of the intensity of the detected light.  
         [0057]    A reading is a measurement of the intensity of the fluorescent emission being generated by a microwell sample in response to excitation light emitted onto the sample. These intensity values are stored in magnitudes of relative fluorescent units (RFU). A reading of a sample having a high magnitude of fluorescent emissions will provide an RFU value much higher then that provided by a reading taken of a sample having low fluorescent emissions.  
         [0058]    Once the total number of readings (e.g., 60 readings) for each sample well have been taken, the readings for each sample must be interpreted by the well reading apparatus  100  so the well reading apparatus  100  can determine the presence of the targeted sequences and differentiate sequence variations. The micro processing unit of the well reading apparatus  100  is controlled by software to perform the following operations on the data representing the sample well readings. The operations being described are applied in essentially the same manner to the readings taken for each sample microwell  118 . Accordingly, for illustrative purposes, the operations will be described with regard to readings taken for one sample microwell  118 , which will be referred to as the first sample microwell  118 .  
         [0059]    As discussed above, during each one-minute interval in which all of the microwells  118  in the sample tray assembly  112  are read, the light sensor bar  130  reads the calibration wells one time. Hence, after 60 readings of each microwell sample have been taken, each calibration well  127  has been read 60 times by its respective light emitting/detecting port  132  of the light sensor bar  130 , which results in eight sets of 60 calibration well readings. For illustrative purposes, the calibration readings of the calibration well  127  that has been read by the light emitting/detecting port  132 , which has also read the first sample microwell  118  now being discussed, are represented as n 1  through n 60 . This procedure occurs for each of the fluorescent dyes.  
         [0060]    Additionally, as discussed above, during each one-minute interval, the optical detector  140  is controlled to obtain a “dark” reading in which a reading is taken without any of the light emitting devices  136  being activated. This allows the optical detector  140  to detect any ambient light that may be present in the system. The dark readings are taken for each light emitting/detecting port  132 . Accordingly, after 60 readings of every microwell  118  have been obtained, eight sets of 60 dark readings (i.e., one set of 60 dark readings for each of the eight light emitting/detecting portions  132 ) have been obtained. For illustrative purposes, the dark readings obtained by the light emitting/detecting port  132 , which read the first sample microwell  118  now being discussed, are represented as d 1  through d 60 .  
         [0061]    [0061]FIG. 5 is a graph showing the relationship of the 60 readings for one well that have been obtained during the one-hour reading period for one of the two targeted sequences. For illustrative purposes, these readings are represented as r 1  through r 60 . These readings are plotted on the graph of FIG. 5 with their RFU value being represented on the vertical axis with respect to the time in minutes at which the readings were taken during the reading period.  
         [0062]    As can be appreciated from the graph, the RFU values for the readings taken later in the reading period are greater than the RFU values of the readings taken at the beginning of the reading. For illustrative purposes, this example shows the trend in readings for a well that contains the particular target sequence for which the well is being tested.  
         [0063]    As can also be appreciated from FIG. 5, the graph of the “raw data” readings includes a noise spike and a step as shown. The process that will now be described eliminates any noise spikes, steps or other apparent abnormalities in the graphs that are the result of erroneous readings being taken of the sample well.  
         [0064]    The flowchart shown in FIG. 6 represents the overall process for interpreting the graph of well readings r 1  through r 60  shown in FIG. 5 to determine whether the well sample includes the particular target sequence(s) and the resulting genotype for which it is being tested. Steps  1000  through  1700  in FIG. 6 are applied separately to each of the two pluralities of target sequence data values. These pluralities may result from readings of two fluorescent wavelengths, each corresponding to a separate target sequence. The processes in FIG. 6 are performed by the controller (not shown) of the well reading apparatus  100  as controlled by software, which can be stored in a memory (not shown) resident in the well reading apparatus  100  or on a disk inserted into disk drive  106 .  
         [0065]    Data Value Correction  
         [0066]    The first process performed by the controller is data value correction. One skilled in the art will appreciate that the process of correcting the data values to correct or eliminate incorrect values may be performed following a variety of processes. For example, the followings steps may be performed to correct the data values prior to reducing the data values to a single value used for determining how the sample is categorized.  
         [0067]    Dark Correction Operation  
         [0068]    As shown in FIG. 6, the software initially controls the controller to perform a dark correction on the calibrator data readings n 1  through n 60  and on the well readings r 1  through r 60 . The details of this step are shown in the flowchart of FIG. 7.  
         [0069]    In particular, in Step  1010 , the dark reading values d 1  through d 60  are subtracted from the corresponding calibrator reading values n 1  through n 60 , respectively, to provide corrected calibrator readings cn 1  through cn 60 , respectively. That is, dark reading d 1  is subtracted from calibrator reading r 1  to provide corrected calibrator reading cn 1 , dark reading d 2  is subtracted from calibrator reading n 2  to provide corrected calibrator reading cn 2 , and so on.  
         [0070]    The processing then proceeds to Step  1020  in which the dark readings d 1  through d 60  are subtracted from their corresponding well readings r 1  through r 60 , respectively to provide corrected well readings cr 1  through cr 60 , respectively. That is, dark well reading d 1  is subtracted from well reading r 1  to provide corrected well reading c 1 , dark reading d 2  is subtracted from well reading r 2  to provide corrected well reading cr 2 , respectively, and so on.  
         [0071]    Smoothing Operation  
         [0072]    After all of the corrected calibrator readings and corrected well readings have been obtained, the processing continues to the filtering operations Step  1100  of the flowchart shown in FIG. 6, in which noise is filtered from the corrected calibrator readings cn 1  through cn 60 , which were obtained during Step  1010  described above. In an embodiment, a 5-point running median is applied to the corrected calibrator readings cn 1  through cn 60  to produce smoothed calibrator values, denoted as xn 1  through xn 60 .  
         [0073]    Normalization Operation  
         [0074]    Once all smoothed calibrator values xn 1  through xn 60  have been obtained, the processing continues to the dynamic normalization step  1200  shown in the flowchart of FIG. 6. The details of the dynamic normalization process are shown in the flowchart of FIG. 8. Specifically in this example, the smoothed calibrator values xn 1  through xn 60 , as well as the corrected well reading values cr 1  through cr 60 , are used to calculate dynamic normalization values in nr 1  through nr 60 .  
         [0075]    In Step  1210 , an arbitrary scalar value is set, which is employed in the calculations. In this example, the scalar value is 3000. The processing then proceeds to Step  1220 , where the scalar value, corrected well reading values, and smoothed normalized values are used to calculate dynamic normalization values. In particular, to calculate the dynamic normalization values, the corresponding corrected well value is multiplied by the scalar value and then that product is divided by the corresponding smoothed calibrator value. For instance, to obtain dynamic normalization value nr 1 , corrected well reading value cr 1  is multiplied by 3000 (the scalar value) and then that product is divided by the value of smoothed calibrator xn 1 . Similarly, dynamic normalization value nr 2  is calculated by multiplying corrected well reading value cr 2  by 3000 and then dividing that product by smoothed calibrator value xn 2 . This process continues until all 60 dynamic normalization values nr 1  through nr 60  have been obtained.  
         [0076]    Noise Spike Removal  
         [0077]    The processing then continues to perform the impulse noise filtering operation on the well data as shown in Step  1300  of the flowchart in FIG. 6. In Step  1300 , a smoothing procedure is applied to the dynamic normalization values nr 1  through nr 60  to obtain smoothed normalized values x 1  through x 60 . In an embodiment, the process includes two iterations of a three point running median filter.  
         [0078]    After Steps  1000  through  1300  of the flowchart in FIG. 6 have been performed as described above, the well readings have, therefore, been smoothened and normalized and are represented by the second smoothed normalized values z 1  through Z 50 . Accordingly, as shown in the graph of FIG. 9, when the second smoothed normalized values z 1  through z 60  are plotted with respect to a corresponding time periods in which their corresponding well readings have been obtained, the noise spike in the graph has been eliminated.  
         [0079]    However, these smoothing and normalizing operations did not remove the step, which is present in the graph as shown in FIG. 9. This increase in the reading values, which resulted in the step appearing in the graph, was likely caused by the presence of a bubble in the well that formed after the 50 th  well reading was obtained (i.e., after an elapsed time of 50 minutes), but before the 51 th  well reading was obtained. Accordingly, the magnitude of well reading values r 51  through r 60  and, hence, the magnitude of smoothed and normalized values z 51  through z 60  have been increased because of the presence of this bubble. Therefore, it is necessary to reduce the smoothed normalized values z 51  through z 60  by a value proportionate to the size of the step.  
         [0080]    Step Removal  
         [0081]    Step Detection. The step removal operation is performed in Step  1400  as shown in the flowchart in FIG. 6. Details of the step removal operation are set forth in the flowchart in FIG. 10.  
         [0082]    It has been determined that graphs of these types generally will have only one or possibly two steps and will almost never have more than two steps. Accordingly, all of the steps in the graph will have been located and removed after performing the step locating process two times. Accordingly, in Step  1405  in the flowchart of FIG. 10, a count value is set to allow the process to repeat a maximum of times. In this example, the count value is set at two to allow the process to repeat two times. The process then proceeds to step  1410 , where difference values dr 1  through dr 59  are calculated, which represent the differences between adjacent second smoothed normalized value z 1  through Z 60 . That is, the first difference value dr 1  is calculated as the value of second smoothed normalized value z 2  minus second smoothed normalized value z 1 . The second difference value dr 2  is calculated as the value of second smoothed normalized value Z 3  minus second smoothed normalized value z 2 . This process is repeated until  59  difference values dr 1  through dr 59  have been obtained.  
         [0083]    The processing then continues to Step  1415 , in which the difference values dr 1  through dr 59  are added together to provide an average total, which is then divided by 59 to provide a difference average &#39;dr. The processing then continues to Step  1420 , where a variance value var(dr) is calculated using a standard statistical formula.  
         [0084]    The process then continues to Step  1425  where a sum value “s” is calculated. This sum value is calculated by subtracting the difference average &#39;dr from each of the difference values dr 1  through dr 59 , taking each result to the fourth power to obtain a set of 59 quadrupled results, and then adding all of the 59 quadrupled results. That is, the difference average &#39;dr is subtracted from the first difference value dr 1  to provide a first result. That first result is then taken to the fourth power to provide a first quadrupled result. The difference average &#39;dr is subtracted from second difference value dr 2 , and the second result of the subtraction is taken to the fourth power to provide a second quadrupled result. This process is repeated for the remaining difference values dr 3  through dr 59  until all 59 quadrupled results have been calculated. The 59 quadrupled results are then added to provide the sum value “s”.  
         [0085]    In Step  1430 , the processing determines whether the process of removing the step is complete by determining if the variance value var(dr) is equal to zero. If the value of var(dr) is equal to zero, the processing proceeds to Step  1460 , where it is determined whether the count value is equal to 2. If the count value is equal to 2, the process continues to Steps  1500 . If the process is in its first iteration, the process continues to Step  1433 , where the count value is incremented by one, and Steps  1410  through  1425  are repeated as discussed above. However, if the value of var(dr) is not equal to zero, then the step detection process can proceed. To determine if the step is present, in Step  1435 , a critical value CRIT_VAL is set equal to 4.9. This critical value is generally chosen to maximize the probability of detecting a step based on statistical theory. The processing then proceeds to Step  1440 , where it is determined whether the quotient of the sum value “s” divided by the product of var(dr) squared and multiplied by 59 is greater than the CRIT_VAL. If the calculated quotient is not greater than CRIT_VAL, then a step is not present, and the processing continues to Step  1433 .  
         [0086]    Step Removal. However, if the quotient is greater than the value of CRIT_VAL, then the processing proceeds to Step  1445  where processing will be performed to determine the location of the step. This is accomplished by subtracting the difference average &#39;dr from each of the 1 through 59 difference values dr 1  through dr 59  to produce a difference result taking the absolute value of each of those difference results. The step corresponds to the pass associated with largest of the absolute values. Denote the pass where the step has occurred as maxpt_dr. As discussed above, in this example, it is presumed that the step occurred at value z 50 . Accordingly, maxpt_dr is set to 50.  
         [0087]    The process then continues to Step  1450  during which the median difference value of the difference values dr 1  through dr 59  is determined. Then, in Step  1455 , the smoothed normalized values occurring after the step are decreased by the difference average &#39;dr calculated for the smoothed normalized value at which the step occurred, and then increased by the median difference value calculated in Step  1450 . For example, the smoothed normalized values z 51  through z 60  are each decreased by the magnitude of difference dr 50  (the step occurred after the 50 th  reading) and then the smoothed normalized values z 51  through Z 60  are each increased by the median difference value calculated in Step  1450 . As shown in FIG. 11, this process has the affect of shifting the entire portion of the curve representing the RFU values of z 51  through z 60  downward, thus eliminating the step.  
         [0088]    The processing then proceeds to Step  1460  where it is determined whether the entire process has been repeated two times. If the value of count does not equal two, the value of count is increased by one in Step in  1435 , and the processing returns to Step  1410  and repeats as discussed above. However, if the value of count is equal to two, the processing proceeds to the periodic noise filter Step  1500  in the flowchart shown in FIG. 6.  
         [0089]    Periodic Noise Filtering Operation  
         [0090]    The periodic noise filtering operation  1500  is performed to further filter out erroneous values that may exist in the graph shown in FIG. 11 in which the step has been repaired. Specifically, a five-point moving median is applied to the read values z 1  through Z 60  represented in the graph of FIG. 11 to provide filtered values f 1  through f 60 .  
         [0091]    Well Present Operation  
         [0092]    When the data values for each set of values have been first corrected, the controller may perform a well present operation to determine whether a well was present or if the data obtained is entirely erroneous. The processing continues to Step  1600  shown in FIG. 6, in which the processing determines whether the filtered values f 1  through f 60 , which were derived from the above-described steps from the well readings r 1  through r 60 , respectively, were actually taken of a well, or, in other words, whether a well was actually present at that location in the microwell array  116  of the sample tray assembly  112 . Details of the well present determination processing are shown in the flowchart of FIG. 12.  
         [0093]    Specifically, in Step  1610 , a well reading average wp avg  is determined by adding the filter values f 10 , f 20 , f 30 , f 40  and f 50 , and dividing those values by 5. This well present average wp avg  is compared to a well threshold value WP_THRES, which in this example is set to 125.0. If, in Step  1620 , the processing determines that the well present average wp avg  is greater than zero and less than the threshold value WP_THRES for both targeted sequences, then the processing determines that no well is present and that the data obtained is entirely erroneous. The processing then proceeds to Step  2100  in the flowchart shown in FIG. 6 where processing for that well is concluded and the controller may provide an indication that the well was not present. However, if the processing determines in Step  1620  that either targeted sequence has a well present average wp avg  that is greater than the threshold value WP_THRES, then the process determines that a well is present and the processing continues to Step  1700  in the flowchart shown in FIG. 6.  
         [0094]    Background Correction Operation  
         [0095]    If the well present operation determines that indeed a well was present the controller may proceed to further correct, or adjust, the plurality of data values. In Step  1700 , the processing establishes a base line background correction. In Step  1710 , a median of filtered value based on, for example, the first five background values f 1  through f 5 , is calculated. Other ranges of filtered values, such as f 10  through f 15 , may be used, depending on the assay. This median filtered value is then subtracted from each of the filtered values f 1  through f 60 . Additionally, the filtered values used to calculate the median filtered value can each be set to zero after being used to calculate the median value, although this is not required. Further details of this background correction operation are shown in the flowchart of FIG. 13. The procedure is done independently for both of the targeted sequences. As shown in the graph of FIG. 14, this processing shifts the portion of the graph between filtered values f, and f 60  down toward the horizontal axis.  
         [0096]    Reducing Data Values  
         [0097]    Signal Ratio Operation  
         [0098]    Once the process defined by Steps  1000  through Steps  1720  has been applied independently to two pluralities of values corresponding to separate amplification sequences, the two pluralities are combined into a single plurality of data that measure the relative different between the two pluralities as shown in FIG. 17. An example of a method to relate the curves defined by Step  1720  in FIG. 13 is to take the ratio in step  1800  of the values provided by Step  1720  at each time point after the background slice defined in Step  1700 . To improve numerical stability, Step  1810  adds a small, known tolerance value (ε) to each data point prior to the division to avoid division by zero. For example, one set of values (a 6  though a 60 ) are divided by the other set of values (b 6  through b 60 ) to produce a third set of values equal to the ratios c 6 =a 6 /b 60  through c 60 =a 60 /b 60 . This division is defined in Step  1820  in FIG. 15. The method in this embodiment would then proceed to Step  1830  in FIG. 15 where logarithm of these ratios is calculated to produce d 6 =log(a 6 /b 6 ). Without loss of generality, the natural logarithm is used in all relevant calculations.  
         [0099]    Data Reduction Operation  
         [0100]    Once the data values for the two pluralities of values have been combined into a single plurality of data values, the plurality of data values is reduced to a single value representative of the plurality of values. For each sample, the plurality of values can be summarized into a single metric in Step  1900  that captures the distribution of the plurality, specifically the magnitude of the values. This procedure is summarized in a flowchart in FIG. 16. There are many different calculations to accomplish this (e.g., mean, median, etc.). In one embodiment, the method is to determine the most likely number that represents the plurality. To accomplish this, a non-parametric probability density (Silverman, 1986) is calculated for a range of possible values (FIG. 16), and the summary metric of the plurality is then the value that corresponds to the value associated with the largest probability density value.  
         [0101]    Step  1910  in FIG. 16 creates a grid of equally spaced values that span the range of log-ratio data points determined in Step  1830 . Step  1920  calculates the nonparametric density estimate for each of the grid values and Step  1930  determines the grid value associated with the largest probability density value.  
         [0102]    Genotype Determination  
         [0103]    Once the most likely number is determined, it is compared to two known reference values to determine how the sample is categorized. This process is depicted in FIG. 17. The most likely number is translated to a distinct genotype (e.g., allele A, allele B, heterozygous etc.). In other words, it has been determined from past data readings taken to detect the presence of the targeted sequences that the most likely values from Step  1930  for one genetic variation (e.g., allele A) will exceed a particular reference value and will be below a second reference value if another genetic variation is present (e.g., allele B). If the most likely value is less than the lower reference value (labeled as A in Step  2010  in FIG. 17), the sample is judged to have allele A (Step  2020 ). In Step  2030 , the most likely value is greater than the upper reference value (labeled as B in Step  2030  in FIG. 17), the sample is judged to have allele B (Step  2040 ). If an allele has not been assigned in Steps  2020  or  2040 , Step  2050  judges the sample to have allele A and B. Accordingly, the reference values are chosen to be values that will provide the most accurate indication as to the genotype of the sample. This can be accomplished by choosing reference values that simultaneously maximize sensitivity and specificity for each particular genetic variant at that locus.  
         [0104]    The processing then proceeds to Step  2100 , where the controller controls the well reading apparatus  100  to report the reported value and provide an indication that the sample in the corresponding well has the determined genotype. This indication can be in the form of a display on the display screen  108 , in the form data stored to a disk in the disk drive  106 , and/or in the form of a print-out by a printer resident in or attached to the well reading apparatus  100 .  
         [0105]    As discussed above, the manner in which the samples from patient number one collected in the other sample microwells are read and analyzed is essentially identical to that described above for the sample in the first sample microwell. Specifically, the 60 readings taken of the sample in each of the respective sample microwells are processed according to Steps  1000  through  2100  in FIG. 6 as described above.  
         [0106]    The above processing can then be performed for all of the patient samples (or wells) in essentially the same manner. As discussed above, if each patient sample is being tested for multiple genotypes, the microwell array  116  can accommodate samples from (96−4χ)χ) patients where χ is the number of genotypes under investigation. Thus, for analysis of three different genetic mutations from each sample, up to (96−(4×3))/3)=28 patients can be screened at one time. It may be possible to increase the number of patients whose samples can be analyzed at one time by permitting a single negative control without target DNA to act as a control for several different genetic tests.  
         [0107]    It is also noted that before any results are reported to patients, the values obtained from reading the allele A, allele B, heterozygous and negative control samples are processed in the manner described above with regard to Steps  1000  through  2100 , and the resulting values are analyzed to assure that the control samples have indeed been read correctly. If the readings of any of these control samples are incorrect (e.g., an allele A control has been identified as allele B or vice-versa), all of the sample readings corresponding to the particular genetic test for that locus are called into question for the entire run. All of the sample data for that test must be discarded, and new samples must be gathered in a new microwell array, and then read and evaluated in the manner described above.  
       EXAMPLE  
       [0108]    Sequence variations within the human β2AR gene and its upstream  5 ′ untranslated region were used as targets for the development of six different adapter-mediated SNP detection systems according to the method of the invention. SDA systems comprising two bumper primers, two amplification primers and two allele-specific signal primers were designed for each of six SNP sites (−654, −367, −47, +46, +491 and +523). The results listed in this example pertain only to the SNP+46. The two signal primers comprised identical sequences except for the diagnostic nucleotide that was positioned one base upstream from the -3′ terminus (N−1). The same pair of adapter sequences was appended to the 5′ ends of the signal primers to permit detection using a common pair of universal reporter probes. The variant position of the signal oligonucleotide contained adenosine (A), cytosine (C), guanine (G) or thymine (T). For the purposes of this study, “wild-type” allele (or allele A) refers to the sequence illustrated in GeneBank (Accession # M15169), while “mutant” (or allele B) represents the alternative nucleotide (SNP). β2AR target sequences containing allele A and/or allele B were cloned in to pUC19 from pooled human genomic DNA.  
         [0109]    Specific amplification products were detected by monitoring the change in fluorescence intensity associated with the hybridization of a reporter probe to the complement of the appropriate signal primer, the subsequent extension of the signal primer complement and cleavage of the resultant double stranded product. For each well, one fluorescein (FAM) (mutant signal) and one rhodamine (ROX) (wild-type signal) reading were made every minute during the course of the reaction. The FAM and ROX fluorescence readings for each sample were plotted over 60 minutes for one well in FIG. 19. The values on the y-axis are the values obtained in Step  1720 . There was a significant increase in ROX fluorescence, over time, compared to a minor increase FAM.  
         [0110]    [0110]FIG. 19 shows a graph of the log ratio values plotted over time for each data point that occurred after the data that define the background correction. A histogram of these values is provided in FIG. 20, along with the probability density estimate for these data. FIG. 21 demonstrates the steps that define the most likely value for these data (3.45). For this system, values that are between ±1 indicate a heterozygous genotype, whereas values below −1 indicate a mutant genotype and values above +1 indicate a wild-type genotype. This particular sample came from a wild-type.  
         [0111]    Although only a few exemplary embodiments of this invention have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this invention. Similarly, this invention is intended to be broad in scope and to the extent any limitation may appear to be drafted in means-plus-function format, it is intended to broadly cover any structure for achieving the described claim. Accordingly, all such modifications are intended to be included within the scope of this invention as defined in the claims that follow.