Morse-clustering of a Topological Data Analysis Network Identifies Phenotypes of Asthma Based on Blood Gene Expression Profiles
James P R Schofield, Fabio Strazzeri, Jeannette Bigler, Michael Boedigheimer, Ian M Adcock, Kian Fan Chung, Aruna Bansal, Richard Knowles, Sven-Erik Dahlen, Craig E. Wheelock, Kai Sun, Ioannis Pandis, John Riley, Charles Auffray, Bertrand De Meulder, Diane Lefaudeux, Devi Ramanan, Ana R Sousa, Peter J Sterk, Rob M. Ewing · bioRxiv (Cold Spring Harbor Laboratory) · 2019
Abstract Stratified medicine requires discretisation of disease populations for targeted treatments. We have developed and applied a discrete Morse theory clustering algorithm to a Topological Data Analysis (TDA) network model of 498 gene expression profiles of peripheral blood from asthma and healthy participants. The Morse clustering algorithm defined nine clusters, BC1-9, representing molecular phenotypes with discrete phenotypes including Type-1, 2 & 17 cytokine inflammatory pathways. The TDA network model and clusters were also characterised by activity of glucocorticoid receptor signalling associated with different expression profiles of glucocorticoid receptor (GR), according to microarray probesets targeted to the start or end of the GR mRNA’s 3’ UTR; suggesting differential GR mRNA processing as a possible driver of asthma phenotypes including steroid insensitivity.