Publication Date
2014
Journal or Book Title
Bioinformatics
Abstract
MOTIVATION: Genomic analyses of many solid cancers have demonstrated extensive genetic heterogeneity between as well as within individual tumors. However, statistical methods for classifying tumors by subtype based on genomic biomarkers generally entail an all-or-none decision, which may be misleading for clinical samples containing a mixture of subtypes and/or normal cell contamination. RESULTS: We have developed a mixed-membership classification model, called glad, that simultaneously learns a sparse biomarker signature for each subtype as well as a distribution over subtypes for each sample. We demonstrate the accuracy of this model on simulated data, in-vitro mixture experiments, and clinical samples from the Cancer Genome Atlas (TCGA) project. We show that many TCGA samples are likely a mixture of multiple subtypes.
DOI
https://doi.org/10.1093/bioinformatics/btu618
Pages
225-232
Volume
31
Issue
2
Recommended Citation
Saddiki, Hachem; McAuliffe, Jon; and Flaherty, Patrick, "GLAD: A mixed-membership model for heterogeneous tumor subtype classification" (2014). Bioinformatics. 1274.
https://doi.org/10.1093/bioinformatics/btu618