Transcriptome profiling followed by differential gene expression analysis often leads
to lists of genes that are hard to analyze and interpret. Functional genomic tools
are powerful approaches for downstream analysis, as they summarize the large and noisy
gene expression space into 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 humans 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
(2374). 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.
