Automated deconvolution of structured mixtures from heterogeneous tumor genomic data.

ABSTRACT: With increasing appreciation for the extent and importance of intratumor heterogeneity, much attention in cancer research has focused on profiling heterogeneity on a single patient level. Although true single-cell genomic technologies are rapidly improving, they remain too noisy and costly at present for population-level studies. Bulk sequencing remains the standard for population-scale tumor genomics, creating a need for computational tools to separate contributions of multiple tumor clones and assorted stromal and infiltrating cell populations to pooled genomic data. All such methods are limited to coarse approximations of only a few cell subpopulations, however. In prior work, we demonstrated the feasibility of improving cell type deconvolution by taking advantage of substructure in gen

SUBMITTER: Roman T 

PROVIDER: S-EPMC5695636 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature