A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer.

ABSTRACT:

Background

Robust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available.

Methods and findings

We implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature dedu

SUBMITTER: Qiu Q 

PROVIDER: S-EPMC3558433 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature