Turning publicly available gene expression data into discoveries using gene set context analysis.
Gene Set Context Analysis (GSCA) is an open source software package to help researchers use massive amounts of publicly available gene expression data (PED) to make discoveries. Users can interactively visualize and explore gene and gene set activities in 25,000+ consistently normalized human and mouse gene expression samples representing diverse biological contexts (e.g. different cells, tissues and disease types, etc.). By providing one or multiple genes or gene sets as input and specifying a gene set activity pattern of interest, users can query the expression compendium to systematically identify biological contexts associated with the specified gene set activity pattern. In this way, researchers with new gene sets from their own experiments may discover previously unknown contexts of gene set functions and hence increase the value of their experiments. GSCA has a graphical user interface (GUI). The GUI makes the analysis convenient and customizable. Analysis results can be conveniently exported as publication quality figures and tables. GSCA is available at https://github.com/zji90/GSCA. This software significantly lowers the bar for biomedical investigators to use PED in their daily research for generating and screening hypotheses, which was previously difficult because of the complexity, heterogeneity and size of the data.
Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.
Zhicheng
Z
Ji
Department of Molecular Biosciences, The University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA Institute for Cellular and Molecular Biology, The University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA.
Steven A
SA
Vokes
Abramson Cancer Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
Chi V
CV
Dang
Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA hji@jhu.edu.
Hongkai
H
Ji
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D066264:Datasets as Topic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D012984:Software