TuBA: Tunable biclustering algorithm reveals clinically relevant tumor transcriptional profiles in breast cancer.

ABSTRACT:

Background

Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes' expression profiles to identify similarly altered signatures.

Results

TuBA's predictions are consistent in 3,940 breast invasive carcinoma samples from 3 independent sources, using different technologies for measuring gene expression (RNA sequencing and Microarra

SUBMITTER: Singh A 

PROVIDER: S-EPMC6582332 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature