title: The statistical geometry of transcriptome divergence in cell-type evolution and cancer.
abstract: In evolution, body plan complexity increases due to an increase in the number of individualized cell types. Yet, there is very little understanding of the mechanisms that produce this form of organismal complexity. One model for the origin of novel cell types is the sister cell-type model. According to this model, each cell type arises together with a sister cell type through specialization from an ancestral cell type. A key prediction of the sister cell-type model is that gene expression profiles of cell types exhibit tree structure. Here we present a statistical model for detecting tree structure in transcriptomic data and apply it to transcriptomes from ENCODE and FANTOM5. We show that transcriptomes of normal cells harbour substantial amounts of hierarchical structure. In contrast, cancer cell lines have less tree structure, suggesting that the emergence of cancer cells follows different principles from that of evolutionary cell-type origination.
authors.affiliation: 1] Systems Biology Institute, Yale University, West Haven, Connecticut 06516, USA [2] Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA.
authors.forename: Cong
authors.initials: C
authors.lastname: Liang
authors.affiliation: RIKEN Center for Life Science Technologies (CLST), Division of Genomic Technologies, Yokohama 230-0045, Japan.
authors.forename: Alistair R R
authors.initials: AR
authors.lastname: Forrest
authors.affiliation: 1] Systems Biology Institute, Yale University, West Haven, Connecticut 06516, USA [2] Department of Ecology and Evolutionary Biology, Yale University, Prospect Street 165, OML Room 327, New Haven, Connecticut 06520-8106, USA.
authors.forename: Günter P
authors.initials: GP
authors.lastname: Wagner
mesh_terms: D005075:Biological Evolution; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics*; D059467:Transcriptome / Q000235:genetics*