Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm.

ABSTRACT:

Background

Gene signatures are important to represent the molecular changes in the disease genomes or the cells in specific conditions, and have been often used to separate samples into different groups for better research or clinical treatment. While many methods and applications have been available in literature, there still lack powerful ones that can take account of the complex data and detect the most informative signatures.

Methods

In this article, we present a new framework for identifying gene signatures using Pareto-optimal cluster size identification for RNA-seq data. We first performed pre-filtering steps and normalization, then utilized the empirical Bayes test in Limma package to identify the differentially expressed genes (DEGs). Next, we used a multi-objective

SUBMITTER: Mallik S 

PROVIDER: S-EPMC6302366 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature