Data-driven characterization of molecular phenotypes across heterogeneous sample collections.

ABSTRACT: Existing large gene expression data repositories hold enormous potential to elucidate disease mechanisms, characterize changes in cellular pathways, and to stratify patients based on molecular profiles. To achieve this goal, integrative resources and tools are needed that allow comparison of results across datasets and data types. We propose an intuitive approach for data-driven stratifications of molecular profiles and benchmark our methodology using the dimensionality reduction algorithm t-distributed stochastic neighbor embedding (t-SNE) with multi-study and multi-platform data on hematological malignancies. Our approach enables assessing the contribution of biological versus technical variation to sample clustering, direct incorporation of additional datasets to the same low dimensiona

SUBMITTER: Mehtonen J 

PROVIDER: S-EPMC6648337 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature