Integrative cancer patient stratification via subspace merging.

ABSTRACT:

Motivation

Technologies that generate high-throughput omics data are flourishing, creating enormous, publicly available repositories of multi-omics data. As many data repositories continue to grow, there is an urgent need for computational methods that can leverage these data to create comprehensive clusters of patients with a given disease.

Results

Our proposed approach creates a patient-to-patient similarity graph for each data type as an intermediate representation of each omics data type and merges the graphs through subspace analysis on a Grassmann manifold. We hypothesize that this approach generates more informative clusters by preserving the complementary information from each level of omics data. We applied our approach to The Cancer Genome Atlas (TCGA) breast cance

SUBMITTER: Ding H 

PROVIDER: S-EPMC6513164 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature