Clustering analysis of microRNA and mRNA expression data from TCGA using maximum edge-weighted matching algorithms.

ABSTRACT:

Background

microRNA (miRNA) is a short RNA (~ 22 nt) that regulates gene expression at the posttranscriptional level. Aberration of miRNA expressions could affect their targeting mRNAs involved in cancer-related signaling pathways. We conduct clustering analysis of miRNA and mRNA using expression data from the Cancer Genome Atlas (TCGA). We combine the Hungarian algorithm and blossom algorithm in graph theory. Data analysis is done using programming language R and Python.

Methods

We first quantify edge-weights of the miRNA-mRNA pairs by combining their expression correlation coefficient in tumor (T_CC) and correlation coefficient in normal (N_CC). We thereby introduce a bipartite graph partition procedure to identify cluster candidates. Specifically, we propose six weight fo

SUBMITTER: Ding L 

PROVIDER: S-EPMC6683425 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature