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

Nucleic Acids Res. 2014;42(16):10288-306. doi: 10.1093/nar/gku722. Epub 2014 Aug 21.


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.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Autophagy-Related Proteins
  • Carrier Proteins / genetics
  • Cluster Analysis
  • Computational Biology / methods*
  • Epithelium / metabolism
  • Gene Expression Profiling*
  • Gene Expression Regulation
  • Gene-Environment Interaction
  • Humans
  • Immunity / genetics
  • Inflammatory Bowel Diseases / drug therapy
  • Mice
  • Microbiota
  • Multivariate Analysis
  • Niacinamide / metabolism
  • Principal Component Analysis
  • Salmonella Infections, Animal / genetics
  • Transcription, Genetic
  • Wound Healing / genetics


  • Atg16l1 protein, mouse
  • Autophagy-Related Proteins
  • Carrier Proteins
  • Niacinamide