NDLI: A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Gough, Albert Shun, Tong Ying Taylor, D. Lansing Schurdak, Mark
Description	Author Affiliation: Gough A ( University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA); Shun TY ( University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA.); Taylor DL ( University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA); Schurdak M ( University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA)
Abstract	Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.
File Format	HTM / HTML
ISSN	10462023
Volume Number	96
e-ISSN	10959130
Journal	Methods
Language	English
Publisher	Elsevier
Publisher Date	2016-03-01
Publisher Place	United States
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Life sciences, Biochemistry Epithelial Cells Ultrastructure Flow Cytometry Statistics & Numerical Data Gene Expression Regulation, Neoplastic High-throughput Screening Assays Microscopy Molecular Imaging Cell Line, Tumor Decision Trees Drug Effects Metabolism Standards Humans Interleukin-6 Pharmacology Phenotype Quality Control Reproducibility Of Results Stat3 Transcription Factor Genetics Signal Transduction Statistics, Nonparametric Journal Article Research Support, N.i.h., Extramural Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Molecular Biology

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