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

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

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.

Suggested Citation

  • Yoshimasa Aoto & Tsuyoshi Hachiya & Kazuhiro Okumura & Sumitaka Hase & Kengo Sato & Yuichi Wakabayashi & Yasubumi Sakakibara, 2017. "DEclust: A statistical approach for obtaining differential expression profiles of multiple conditions," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-15, November.
    • Handle: RePEc:plo:pone00:0188285
    DOI: 10.1371/journal.pone.0188285
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    Cited by:

    1. Bárbara Andrade Barbosa & Saskia D. Asten & Ji Won Oh & Arantza Farina-Sarasqueta & Joanne Verheij & Frederike Dijk & Hanneke W. M. Laarhoven & Bauke Ylstra & Juan J. Garcia Vallejo & Mark A. Wiel & Y, 2021. "Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Diem-Trang Tran & Aditya Bhaskara & Balagurunathan Kuberan & Matthew Might, 2020. "A graph-based algorithm for RNA-seq data normalization," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.

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