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-TNFalpha therapy. We provide relevant experimental validation that corroborates the accuracy and advantage of our method.
