DEclust: A statistical approach for obtaining differential expression profiles of multiple conditions

High-throughput RNA sequencing technology is widely used to comprehensively detect and quantify cellular gene expression. Thus, numerous analytical methods have been proposed for identifying differentially expressed genes (DEGs) between paired samples such as tumor and control specimens, but few stu...

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Published inPloS one Vol. 12; no. 11; p. e0188285
Main Authors Aoto, Yoshimasa, Hachiya, Tsuyoshi, Okumura, Kazuhiro, Hase, Sumitaka, Sato, Kengo, Wakabayashi, Yuichi, Sakakibara, Yasubumi
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 21.11.2017
Public Library of Science (PLoS)
Subjects
Analytical methods
Bioinformatics
Biology and Life Sciences
Cancer
Cell cycle
Cluster analysis
Clustering
Data analysis
Data processing
Deoxyribonucleic acid
DNA
Engineering and Technology
Gene clusters
Gene expression
Gene sequencing
Genomes
Identification methods
Informatics
Medical research
Medicine and Health Sciences
Physical Sciences
Physiological aspects
Ribonucleic acid
RNA
RNA sequencing
Samples
Statistical analysis
Statistical methods
Tissues
Tumors
Japan
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Summary:High-throughput RNA sequencing technology is widely used to comprehensively detect and quantify cellular gene expression. Thus, numerous analytical methods have been proposed for identifying differentially expressed genes (DEGs) between paired samples such as tumor and control specimens, but few studies have reported methods for analyzing differential expression under multiple conditions. We propose a novel method, DEclust, for differential expression analysis among more than two matched samples from distinct tissues or conditions. As compared to conventional clustering methods, DEclust more accurately extracts statistically significant gene clusters from multi-conditional transcriptome data, particularly when replicates of quantitative experiments are available. DEclust can be used for any multi-conditional transcriptome data, as well as for extending any DEG detection tool for paired samples to multiple samples. Accordingly, DEclust can be used for a wide range of applications for transcriptome data analysis. DEclust is freely available at http://www.dna.bio.keio.ac.jp/software/DEclust.
Bibliography:Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0188285