Abstract
Large-scale, open-access data sets such as the Genotype Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA) include multi-omic data on large numbers of samples along with extensive clinical and phenotypic information. These datasets provide a unique opportunity to discover correlations among clinical and genomic data features that can lead to testable hypotheses and new discoveries. SEAHORSE (http://seahorse.networkmedicine.org/) is a web-based database and search tool for exploratory data analysis in which we have pre-computed statistical associations between available data elements. An easy-to-use user interface allows users to explore significant associations using tabulated summary statistics, data visualizations, and functional enrichment analyses (using RNA-seq data) for identified sets of genes. We describe the motivation and construction of SEAHORSE and demonstrate its utility by documenting several surprising association patterns observed across multiple tissues in GTEx and multiple different cancer types in TCGA.