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

Yoshimasa Aoto, Tsuyoshi Hachiya, Kazuhiro Okumura, Sumitaka Hase, Kengo Sato, Yuichi Wakabayashi, Yasubumi Sakakibara

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere0188285
JournalPLoS One
Volume12
Issue number11
DOIs
Publication statusPublished - 2017 Nov 1

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Genes
transcriptome
Transcriptome
Gene expression
High-Throughput Nucleotide Sequencing
sampling
Tumors
Gene Expression Profiling
Multigene Family
multigene family
Throughput
RNA
Tissue
analytical methods
Cluster Analysis
data analysis
Software
genes
methodology
Technology

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

DEclust : A statistical approach for obtaining differential expression profiles of multiple conditions. / Aoto, Yoshimasa; Hachiya, Tsuyoshi; Okumura, Kazuhiro; Hase, Sumitaka; Sato, Kengo; Wakabayashi, Yuichi; Sakakibara, Yasubumi.

In: PLoS One, Vol. 12, No. 11, e0188285, 01.11.2017.

Research output: Contribution to journalArticle

Aoto, Yoshimasa ; Hachiya, Tsuyoshi ; Okumura, Kazuhiro ; Hase, Sumitaka ; Sato, Kengo ; Wakabayashi, Yuichi ; Sakakibara, Yasubumi. / DEclust : A statistical approach for obtaining differential expression profiles of multiple conditions. In: PLoS One. 2017 ; Vol. 12, No. 11.
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