A novel statistical approach for identification of the master regulator transcription factor

• DOI: 10.1186/s12859-017-1499-x
• Publication Date: 2017-02-02T00:20:55Z
• Authors: Sinjini Sikdar; Susmita Datta
• Keywords: Master regulator; Identification; Statistical Analysis

Transcription factors are known to play key roles in carcinogenesis and therefore, gaining popularity as potential therapeutic targets drug development. A 'master regulator' transcription factor often appears control most of the regulatory activities other associated genes. This is at top hierarchy transcriptomic regulation. Therefore, it important identify target master regulator for proper understanding disease process identifying best option.We present a novel two-step computational approach identification genome. At first step our method we test whether there exists any system. We evaluate concordance two ranked lists using statistical measure. In case measure statistically significant, conclude that regulator. second step, identifies factor, if one.In simulation scenario, performs reasonably well validating existence when number subjects each treatment group large. application real datasets, ensures regulators biologically meaningful regulators. An R code implementing sample data can be found http://www.somnathdatta.org/software .We have developed screening just only gene expression data. Understanding structure finding help narrowing search space biomarkers complex diseases such cancer. addition provides an overview which global profiles consequently cell functioning.