PT  - JOURNAL ARTICLE
AU  - Christian H. Holland
AU  - Bence Szalai
AU  - Julio Saez-Rodriguez
TI  - Transfer of regulatory knowledge from human to mouse for functional genomic analysis
AID  - 10.1101/532739
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 532739
4099  - http://biorxiv.org/content/early/2019/01/29/532739.short
4100  - http://biorxiv.org/content/early/2019/01/29/532739.full
AB  - Transcriptome profiling followed by differential gene expression analysis often leads to unclear lists of genes which are hard to analyse and interpret. Functional genomic tools are powerful approaches for downstream analysis, as they summarize the large and noisy gene expression space in a smaller number of biological meaningful features. In particular, methods that estimate the activity of processes by mapping transcripts level to process members are popular. However, footprints of either a pathway or transcription factor (TF) on gene expression show superior performance over mapping-based gene sets. These footprints are largely developed for human and their usability in the broadly-used model organism Mus musculus is uncertain. Evolutionary conservation of the gene regulatory system suggests that footprints of human pathways and TFs can functionally characterize mice data. In this paper we analyze this hypothesis. We perform a comprehensive benchmark study exploiting two state-of-the-art footprint methods, DoRothEA and an extended version of PROGENy. These methods infer TF and pathway activity, respectively. Our results show that both can recover mouse perturbations, confirming our hypothesis that footprints are conserved between mice and humans. Subsequently, we illustrate the usability of PROGENy and DoRothEA by recovering pathway/TF-disease associations from newly generated disease sets. Additionally, we provide pathway and TF activity scores for a large collection of human and mouse perturbation and disease experiments (2,374). We believe that this resource, available for interactive exploration and download (https://saezlab.shinyapps.io/footprint_scores/), can have broad applications including the study of diseases and therapeutics.