title: Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.
abstract: Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a fully unsupervised in silico method, for identifying subpopulation marker genes directly from the original mixed gene expressions in scatter space that can improve molecular analyses in many biological contexts. Validated with predesigned mixtures, CAM on the gene expression data from peripheral leukocytes, brain tissue, and yeast cell cycle, revealed novel marker genes that were otherwise undetectable using existing methods. Importantly, CAM requires no a priori information on the number, identity, or composition of the subpopulations present in mixed samples, and does not require the presence of pure subpopulations in sample space. This advantage is significant in that CAM can achieve all of its goals using only a small number of heterogeneous samples, and is more powerful to distinguish between phenotypically similar subpopulations.
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
authors.forename: Niya
authors.initials: N
authors.lastname: Wang
authors.affiliation: Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC 20007, USA.
authors.forename: Eric P
authors.initials: EP
authors.lastname: Hoffman
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
authors.forename: Lulu
authors.initials: L
authors.lastname: Chen
authors.affiliation: Pediatric Oncology Branch, National Institutes of Health, Gaithersburg, MD 20877, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Chen
authors.affiliation: Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois 60607, USA.
authors.forename: Chunyu
authors.initials: C
authors.lastname: Liu
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
authors.forename: Guoqiang
authors.initials: G
authors.lastname: Yu
authors.affiliation: Department of Medicine, Wake Forest University, Winston-Salem, NC 27157, USA.
authors.forename: David M
authors.initials: DM
authors.lastname: Herrington
authors.affiliation: Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000818:Animals; D015415:Biomarkers / Q000378:metabolism; D001921:Brain / Q000378:metabolism; D020869:Gene Expression Profiling; D005800:Genes, Fungal; D008099:Liver / Q000378:metabolism; D008168:Lung / Q000378:metabolism; D008954:Models, Biological; D009928:Organ Specificity; D051381:Rats; D059467:Transcriptome; D015003:Yeasts
chemical_list: D015415:Biomarkers