Abstract
Availability of large collections of public omics data creates a wide range of hands-on training opportunities for biomedical investigators. The organization and outcome of a workshop focusing on implementation of modular repertoire analysis and visualization approaches is described here. This analytic strategy has been developed specifically for mining of blood transcriptome data. Workshop participants conducted modular repertoire analyses of public blood transcriptome datasets generated from patients with acute respiratory syncytial virus infection (RSV). Analyses were conducted using a recently described 382-module repertoire framework. Next, group-level, as well as individual-level modular fingerprint plots, were generated. A meta-analysis encompassing 490 subjects was also conducted at the module-level by combining outputs from analyses of six independent datasets. Clustering was used to identify distinct patient subgroups in this consolidated patient cohort.
