TY - JOUR
T1 - Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues
AU - Wang, Niya
AU - Hoffman, Eric P.
AU - Chen, Lulu
AU - Chen, Li
AU - Zhang, Zhen
AU - Liu, Chunyu
AU - Yu, Guoqiang
AU - Herrington, David M.
AU - Clarke, Robert
AU - Wang, Yue
N1 - Funding Information:
This work was funded in part by the National Institutes of Health under Grants NS029525, CA160036, CA184902, ES024988, CA149653, and HL111362.
PY - 2016/1/7
Y1 - 2016/1/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84954563283&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954563283&partnerID=8YFLogxK
U2 - 10.1038/srep18909
DO - 10.1038/srep18909
M3 - Article
C2 - 26739359
AN - SCOPUS:84954563283
SN - 2045-2322
VL - 6
JO - Scientific reports
JF - Scientific reports
M1 - 18909
ER -