Expanding the Immunology Toolbox: Embracing Public-Data Reuse and Crowdsourcing  Rachel Sparks, William W. Lau, John S. Tsang  Immunity  Volume 45, Issue 6, Pages 1191-1204 (December 2016) DOI: 10.1016/j.immuni.2016.12.008 Copyright © 2016 Terms and Conditions

Figure 1 Expanding the Immunologists’ Toolbox (A) The traditional research paradigm (left) focuses on hypothesis generation followed by generation of experimental data for hypothesis testing. An augmented, complementary paradigm (right) uses public data to generate and/or refine hypotheses prior to designing experiments. (B) A proposal for adding public-data exploration and reuse into the immunologists’ toolbox that involves (1) education in bioinformatics, computer programming, and relevant areas of statistics and applied mathematics; (2) development of software and “data commons” platforms to enable hands-on exploration of public data for hypothesis generation and testing; and (3) community engagement to create reusable content such as sample and sample group annotations and data compendia. Immunity 2016 45, 1191-1204DOI: (10.1016/j.immuni.2016.12.008) Copyright © 2016 Terms and Conditions

Figure B1 Basic Concept of Meta-Analysis Using Gene-Expression Data A collection of CGPs (comparison group pairs) where each CGP comprises two groups of gene-expression profiles (e.g., disease versus healthy sample groups) is shown. Meta-analysis is performed by deriving a CGP collection, typically from more than one study, and evaluating for coherent signals (genes with consistent increased or decreased expression) across the CGPs. One can use multiple statistical techniques to perform meta-analyses (see text in this Box). The output of meta-analysis typically includes lists of differentially expressed (i.e., with increased or decreased expression) genes, which can be used for downstream analyses, such as pathway enrichment analysis or module analysis. Immunity 2016 45, 1191-1204DOI: (10.1016/j.immuni.2016.12.008) Copyright © 2016 Terms and Conditions