Multivariate inference of pathway activity in host immunity and response to therapeutics.

Nucleic Acids Res
Authors
Keywords
Abstract

Developing a quantitative view of how biological pathways are regulated in response to environmental factors is central for understanding of disease phenotypes. We present a computational framework, named Multivariate Inference of Pathway Activity (MIPA), which quantifies degree of activity induced in a biological pathway by computing five distinct measures from transcriptomic profiles of its member genes. Statistical significance of inferred activity is examined using multiple independent self-contained tests followed by a competitive analysis. The method incorporates a new algorithm to identify a subset of genes that may regulate the extent of activity induced in a pathway. We present an in-depth evaluation of specificity, robustness, and reproducibility of our method. We benchmarked MIPA's false positive rate at less than 1%. Using transcriptomic profiles representing distinct physiological and disease states, we illustrate applicability of our method in (i) identifying gene-gene interactions in autophagy-dependent response to Salmonella infection, (ii) uncovering gene-environment interactions in host response to bacterial and viral pathogens and (iii) identifying driver genes and processes that contribute to wound healing and response to anti-TNFα therapy. We provide relevant experimental validation that corroborates the accuracy and advantage of our method.

Year of Publication
2014
Journal
Nucleic Acids Res
Volume
42
Issue
16
Pages
10288-306
Date Published
2014
ISSN
1362-4962
URL
DOI
10.1093/nar/gku722
PubMed ID
25147207
PubMed Central ID
PMC4176341
Links
Grant list
310372 / European Research Council / International
P30 DK043351 / DK / NIDDK NIH HHS / United States
AI089992 / AI / NIAID NIH HHS / United States
DK043351 / DK / NIDDK NIH HHS / United States