A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis

A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis

Abstract Motivation Detecting differentially expressed (DE) genes between disease and normal control group is one of the most common analyses in genome-wide transcriptomic data. Since most studies don’t have a lot of samples, researchers have used meta-analysis to group different datasets for the sa...

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Bibliographic Details
Main Authors: Wenyi Qin, Hui Lu
Format: Article
Language:English
Published: BMC 2018-02-01
Series:BioData Mining
Subjects:
Public data integration
Cross disease transcriptome
Gene expression
Differentially expressed
Online Access:http://link.springer.com/article/10.1186/s13040-018-0163-y

Internet

http://link.springer.com/article/10.1186/s13040-018-0163-y

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