dataesr/scientific-paragraphs-categorization huggingface dataset:
The first goal was to share and disseminate the latest technologies, algorithms, databases, and best practices from each of the participating labs. The second goal was to share lists of validated antibodies and other affinity reagents applicable to RPPA to create a common database for all RPPA users. As the validation of high-quality affinity reagents remains the rate-limiting factor for the implementation of RPPA, a community effort would increase utility, decrease 2 Jiyong Liang and Gordon Mills, unpublished. redundancy, and decrease costs. A third goal was the development of general recommendations for sample collection and handling, arraying, data collection, and bioinformatics. The institution of standardized protocols was not the goal of the working group, as the technology is still evolving and a need for flexibility and innovation remains paramount.
Although the RPPA technology is currently based primarily on the use of antibodies as affinity reagents, other reagents such as aptamers could be applied. However, it is expected that the underlying principles of quality control and validation will remain a key challenge. A "validated" antibody must be shown to be specific, selective, and reproducible in the context for which it is to be used. For RPPA, a good correlation between explainable bands in Western blot and RPPA data should be demonstrated. Further, the antibody must perform robustly across different sample types and over time. It is critical to emphasize that the quality of many antibodies, both monoclonal and polyclonal, can change over time, requiring revalidation of different batches. Furthermore, ongoing studies can reveal previously unnoticed liabilities, requiring reevaluation of the validation status of many antibodies.
2-Perform antibody validation on a panel of standard cell lines with Western blot. Confirmation of results obtained from the Western blot with RPPA needs to be shown afterward ("reverse validation"). 3-Western-blot-based antibody validation using the material later intended for the use of the RPPA (e. g. human tissue lysates). In this case, potential cross-reactivity of the antibody should be detected before the antibody is used for RPPA. In either case, the two minimum criteria for antibody specificity are explainable single or multiple bands in a Western blot and good correlation between Western blot and RPPA results. Indeed, the correlation between Western blot and RPPA data provides a good indication of antibody quality. However, the number and spectrum of samples on which the correlation is established will affect its power.
On a typical Western blot, 10 to 15 samples are tested. If the same samples are present on the RPPA array, a standard Pearson correlation can be made. However, if the expression level among all samples is similar (a histone protein, for example), or if, in contrast, expression is seen in only one sample (a protein involved in apoptosis, for example), the correlation is not representative and is subject to potential artifacts. In addition, it can matter whether the correlation is based on raw data or on normalized data, and in the latter case how the normalization was done. Thus, correlation coefficients from different laboratories are not directly comparable. Nevertheless, they remain of importance as an objective indication of antibody performance and can be used to categorize antibodies with regard to their specificity as follows: (i) highly specific, (ii) use antibody with caution, or (iii) antibody is not reliable.
Indeed, reagents that perform well on cell line studies might not perform well in patient samples, and antibodies that provide accurate results with human tissues might not be usable with other species such as mouse (because of binding characteristics and cross-reactivity with murine immunoglobulin) and drosophila. Thus, a global antibody database would be cost effective and could provide important information to guide laboratories in the selection of antibodies to test in their own platforms and assay conditions (Fig. 2 ). Few studies have been published on antibody validation for RPPA. An important effort has been made by Major and colleagues (60) through the creation of AbMiner, a valuable database of more than 600 antibodies. This database includes Western blot results, from tests performed on a pool (mixture) of 60 cell lines, and refers to official naming of the genes and proteins targeted by each antibody.
However, to our knowledge, the database is not up to date (antibody validation was performed in 2001-2003), does not provide links to the validation data, and does not include comparison between RPPA and Western blot. The ASKMD database of 279 antibodies described by Spurrier and colleagues (  61 ) is no longer accessible. More recently, Mannsperger and colleagues  (62, 63)  tested antibodies via Western blot followed by RPPA on samples in which the target protein had been depleted by siRNA. In this manner, they described the validation of antibodies for up to 26 proteins. Although very powerful, this approach seems incompatible with the highthroughput validation of large panels of antibodies. Sevecka and colleagues (59) have attempted to increase the throughput of antibody validation for RPPA.
75) between normalized RPPA data and Western blot intensities. Using these criteria, 82 out of 383 antibodies were validated. Other groups have published lists and validation status for specific studies; however, the validation approaches and, in particular, primary data are often not available for inspection and quality control. These examples of published antibody validation sets show the different requirements (and pitfalls) of a global antibody database. In order to serve the RPPA community, such a database would be required to meet the following criteria: • Continuously updated by multiple laboratories • Includes details of methods used for validation • Includes the validation data (Western blot and RPPA images) • Indicates the correlation coefficient between Western blot and RPPA for each antibody Making such a database publically available is also expected to impact positively on antibody suppliers, as it would be in their best interest to have an important fraction of validated RPPA antibodies from their respective portfolios in this global database.
Detection and Processing-Possibly the largest divergence among RPPA laboratories concerns technical approaches employed for the visualization and analysis of antibody sig-FIG. 2. A global database providing a quality index for antibodies and comprehensive information about the validation process will be the first stop for researchers performing the RPPA technology. nals. Approaches include visualization via dye precipitation, chemiluminescence, near-infrared dyes, quantum dots, and ultrasensitive planar waveguide technologies. Different detection approaches require specialized instrumentation to generate high-resolution digital images that can subsequently be used for data analysis. Importantly, the choice of detection strategy can greatly influence the performance of the assay by reducing background and increasing the dynamic range and sensitivity.
In some cases RPPA detection hardware and analysis software are platform specific, as is the case for Zepto-READER and ZeptoVIEW software integrated within the Zeptosens platform provided by Zeptosens-Bayer Technology Services. Other hardware and software solutions are platform agnostic, such as standard/infrared microarray readers; conventional and infrared flatbed scanners; and microarray image analysis software like MicroVigene TM (VigeneTech), GenePix Pro (Molecular Devices), Array-Pro Analyzer (Media Cybernetics), and Mapix (Innopsys), each of which can be tailored toward RPPA. Each approach has its own particular strengths and limitations that must be considered in data analysis and in the interpretation of the resultant data. For this reason, it is not appropriate to recommend a particular approach at this point in time.
Importation of the sample name and its dilution step, corresponding to each spot, is an absolute requirement for further data analysis and can be done either at the level of the printer or before array quantification. In addition, the quantification software can propose a first local background correction, and the possibility of implementing additional analysis packages, for quality control and data processing, can be of interest. Current Packages in Use-A number of commercial software packages are currently in use for the analysis of RPPA data. These include MicroVigene (VigeneTech), Array Pro (Media Cybernetics), GenePix Pro (Molecular Devices), and Mapix (Innopsys). With the exception of MicroVigene, these packages are designed for standard microarray analysis, but they are sufficient to perform many of the basic functions required in the initial stages of RPPA analysis.
Of these, the non-parametric model makes the least restrictive assumption that the observed intensities increase monotonically with respect to the true protein concentrations. However, at very low or very high intensities, the models tend to saturate at either extreme, and the results become less reliable. Indeed, the key limitation is that several of the dilutions need to be in the "linear" range of the concentration curve. Further, contributions of background at high and low dilutions will be different, potentially displacing the curve. Processing Several Slides of the Same Antibody-Surprisingly, processing identical sets of samples on different slides labeled with the same antibody can result in datasets that have very different means and variances. Neeley et al. (68)  processed clinically similar acute lymphoblastic leukemia samples in two batches and observed differences in their protein data distributions.
Because the coefficient of proportionality of observed values versus true concentrations may vary from one antibody to the next, we cannot directly compare observed data values to make inferences about concentrations of different proteins. For instance, in the absence of calibrators, we cannot directly infer that the concentration of protein x is greater than that of protein y in sample j, or vice versa. Further, inherent characteristics of each antibody such as affinity and background can influence the apparent concentration of the analyte. These challenges must be borne in mind when performing analysis across different proteins. Many of the techniques described above are implemented in the SuperCurve  (46)  and NormaCurve (31) software. Processing Multiple Batches of Samples Across Several Different Antibodies-When processing multiple batches of samples, we have to consider potential batch effects.
Such temporal analysis of pathway signaling at post-translational levels provides additional context to the field of functional proteomics by informing on the precise sequence and duration of post-translational signaling events necessary to produce a specific functional outcome. Such datasets are ideally suited to building more accurate mathematical models that can be used to predict how dynamic remodeling of pathway networks can modify cell function  (74). However, the number of samples easily exceeds 1000 in these types of analyses  (59, 75). RPPA is currently the only technology capable of analyzing protein pathways in such large series of samples simultaneously, and the RPPA technology provides a tremendous contribution to our systems biology modeling of cancer based on network models  (76 -80)  and can serve to validate the integrity of established mathematical models  (81).
Thus, RPPA can be used to identify therapeutic response markers that might be readily suitable for the development of antibody-diagnostic tests to select patients for treatment study. The identification of pathway markers of drug resistance can be directly crossreferenced to approved drug or broader drug-target databases to build rational drug combination hypotheses for further testing  (90). RPPA has been used to identify new, unexpected mechanisms of targeted therapy resistance. In such work the ability to quantitatively measure the activation state of many dozens of signaling proteins at once was exploited  (83) , and the results pointed to the need to alter the way certain kinase inhibitors are selected and graduate within the lead optimization process. The application of these approaches at the preclinical phase might help reduce high attrition rates in drug discovery and development by supporting investment in movement of the most promising candidate drug biomarker and/or candidate drug combination strategies forward into clinical development.
We mention a few of them here, along with some of the solutions that are currently available in the RPPA armamentarium. Data Normalization for Clinical Applications-Assuming that initial quality control experiments have been satisfied in terms of antibody performance, the first concern is how to normalize the data obtained from the microarray. This is described in detail in a preceding section. Quantitation of the Data-In the early clinical applications of the RPPA technology, most investigators were satisfied to characterize their set of clinical samples based on relative protein levels across the samples analyzed. However, currently the best way to approach quantitation is to print standard curves of purified proteins corresponding to the specificity of the antibodies to be tested on the array.
" Simply scrutinizing the ranges said to be "normal" in conventional clinical chemistry will immediately show this to be true. In addition, the variance of signals from the patients will often be much tighter than the variance of signals from the control patients. This is because the diseasespecific patients may have more in common than a collection of people who can be included in the non-disease-specific category. Many statistical approaches depend on the assumption of a common variance in the samples being compared. Clinical statisticians can give advice on how to address this problem. Finally, examining the entire dataset from the vantage point of the hierarchical clustering algorithm will often identify subcategories of patients, both diseased and control, that had previously been expected to be homogeneous  (14, 35).
Such efforts have been recently posited for diseases such as node-negative lung cancer  (94). Recently, the RPPA technology was successfully implemented to uncover a new phenotype of breast cancer wherein the patients were found to have activated HER2 signaling (phosphorylated HER2) in the absence of FISH/IHC positivity  (35). This finding has been subsequently validated by others  (106)  and is underpinning evaluation of patient selection by HER2 phosphorylation status. Integration of RPPA data with other genomic, metabolomic, lipidomic, and drug sensitivity sets will inevitably provide additional insight into disease and drug mechanism studies. RPPA analysis of the activation state of proteins will provide an additional function context for genomic mutation and variant analysis data. A recent study published by the Cancer Genome Atlas Network applied multivariate statistical analysis to a combined dataset consisting of RPPA, genomic DNA copy number, DNA methylation, exome sequencing, mRNA arrays, and microRNA analysis of breast cancer patient biopsies  (107).
Broadscale multi-omic integration of RPPA phosphoprotein and protein data along with genomic, metabolomic, and other data types has been recently utilized across the NCI-60 cell line set  (95). Thus the integration of RPPA proteomic data with other omic datasets is well positioned to provide novel insights and enhance the value of any single omics approach. However, significant bioinformatics challenges in defining how to best integrate orthogonal datasets remain to be resolved. Data integration and the development of user-friendly software tools to support robust bioinformatic and statistical analysis of combined omics data are key priorities for future development. RPPA Platform of the Future-Future technologic advances can be envisioned that will simplify the RPPA workflow, reduce the complexity of the assay steps, and render a direct quantitative output.
Indeed, once the initial repertoire of high-quality antibodies is exhausted, the validation rate for new targets and antibodies will probably be in the range of 20%. Thus the commitment from the RPPA Workshop participants to share antibody lists and validation approaches is critical to cost containment and to democratizing the technology platform. 2. As the amount of high-quality data generated on the RPPA platform is rapidly increasing, it is becoming possible to consider the generation and dissemination of a "Human Proteomics Atlas" that would both complement and extend the utility of similar genomics efforts. This will require the development of a centralized user-friendly data repository for RPPA data storage and sharing, similar to those existing for gene expression profiling. RPPA data have already been hosted on several of the publicly available cancer genomics data portals, such as the University of California Santa Cruz Cancer Genomics Browser  (112, 113) , the MSKCC cBio portal  (114) , and the Cancer Genome Atlas portal.
The potential to implement RPPA into clinical practice creates a separate set of challenges. However, based on the opportunity and need for platforms that can quantitatively handle small patient samples, this opportunity requires an active community effort. Further, the standard operating procedures and rigor required for implementation into patient management will provide important information for the research laboratory, resulting in cross-fertilization that will benefit both approaches. As indicated above, the key outcome of the first three global RPPA Workshops was a consensus on the need to develop and implement processes to share best practices; to develop a highly annotated database of validated affinity reagents; to develop a database to manage, analyze, and share RPPA data; and to continue development of the platform through subsequent workshops and the creation of an RPPA society.
On the other hand, border zones between the vascular territories are less frequently infarcted  (Caviness et al. , 2002). Some lesions, e. g. anterior temporal resections, are relatively stereotyped. Although these issues are acknowledged in a general way, few studies report these relationships in such a fashion as to clarify the extent and density of brain coverage or quantify regional biases  (Mah et al. , 2014). In this paper, we examine what factors affect detectability of lesiondeficit associations using both discrete (PM3) and continuous (VLSM) approaches to incorporation of deficit. Our approach is to conduct systematic simulations based on lesion data in the Iowa Patient Registry, which provides a representative lesion sample with heterogeneous brain coverage. These simulations allow us to analyze the effect of factors such as sample size, location of the critical area of interest, probability of association of deficit with the lesion, and threshold of significance on sensitivity, specificity and localization bias.
Methods We developed a simulator in Python and Matlab that, given parameters describing the sample size, probability of deficit, and truth model, computes the statistically significant region that is associated with a deficit. Fig. 1  shows the overview of the lesion simulator (see the details below). The primary statistical metrics used for the simulation are PM3  (Rudrauf et al. , 2008) , and voxel-based lesion-symptom mapping (VLSM:  Bates et al. , 2003). PM3 is the proportion of subjects with a lesion at a given voxel and a deficit among those with a deficit, minus the proportion of subjects with a lesion and no deficit among those with no deficit, or Eq. (  1 ). We also report on performance of M3, defined as the number of subjects with a lesion at voxel and a deficit (N LD ) minus the number of subjects with a lesion at voxel but no deficit (N L ~D), (Eq.
, 2001; Tranel et al. , 1997; Tranel et al. , 2003; Tranel et al. , 2009; Young et al. , 2010). Exact analytical expressions for the distributions of probability of the M3 and PM3 distributions are described in  Rudrauf et al. (2008). PM3 ¼ N LD N D - N LD N D ð1Þ M3 ¼ N LD -N LD ð2Þ We performed simulations using PM3 for 100 critical regions (Fig. 2 ), varying systematically the sample size  (25, 50, 75, 100, 125, 150) , proportion of lesions in the critical parcel that were accompanied by deficit  (. 3,. 5,. 75, 1) , and the uncorrected threshold for statistical significance (. 001,. 005,. 01,. 05), followed by statistical evaluations of the simulation outputs. VLSM was implemented in Matlab in our simulator as described in  Bates et al. (2003). Because of computational demands, we limited our simulations of VLSM to a sample size of 150 and the case where all lesions causing suprathreshold damage to the critical parcel were associated with a deficit.
Input data to the simulator The simulator draws samples from a real lesion database. The input data in this study consists of (a) a lesion database consisting of 351 lesion masks drawn from and representative of the patient registry of the University of Iowa3s Division of Behavioral Neurology and Cognitive Neuroscience, in NIfTI format; and (b) an anatomically parcellated reference brain, in NIfTI format. Lesions were regions of circumscribed tissue destruction caused by cerebrovascular disease, herpes simplex encephalitis, or surgical resection. The lesions were expertly traced (by Dr. Hanna Damasio and students under her supervision) on a 3D T1-weighted MRI image of a standard normal reference brain, respecting anatomic landmarks, according to the MAP-3 procedure developed in the Laboratory of Human Neuroanatomy and Neuroimaging  (Damasio and Damasio, 1989; Damasio and Frank, 1992; Damasio et al.
Basic simulator algorithm For each statistical metric and for each combination of a set of parameters (CR, N, P, T, Thr) a random sample of N subjects is drawn from the lesion database. These subjects are inspected to determine whether they meet the criteria for having a lesion following a lesiondamage function (i. e. a minimum of damage to 20% of voxels in the critical region). Of those subjects who have lesions in the CR, some fraction (P) are assigned a deficit. Then, the statistics (either PM3 or M3) are calculated and thresholded significance maps are output for each of the given thresholds (Thr). This sampling process and creation of threshold significance maps are repeated for T iterations. Certain important intermediate parameters are calculated in the simulator loop, for each combination of input parameters.
Evaluations of the simulator outputs After running the simulator, we conducted statistical analyses to reduce and interpret the simulator outcomes. This involved characterizing the CRs and identifying how CR characteristics and simulator parameters related to simulator outcomes. This is because anatomical variability is an important implicit input variable. For example, some regions are more susceptible to damage, and will be more represented in the lesion database. To characterize this variability, we computed various measures describing the nature of the anatomic parcels (CRs), and lesion coverage of the CR given the population. These descriptive statistics included CR size, the average number of subjects with damage in the CR, and the proportion of damaged voxels in the CR (see Table  1  for the list of the CR descriptive statistics).
It can be seen from Eqs. (  1 ) and (  2 ) that these three parameters are sufficient to describe the M3 and PM3 statistics. The number of subjects with a lesion N L is determined by the lesion coverage in the lesion database. As we increase the sample size N, N L increases proportionally, because our sample is drawn from the same distribution. Therefore, if N L is proportional to N, the M3 and PM3 maps will be similar for any selections of deficit probability and N where P × N 1 =P × N 2 , given the simple truth model tested in our simulations.