Differential expression analysis on RNA-seq count data based on penalized matrix decomposition

Jin Xing Liu, Ying Lian Gao, Yong Xu, Chun Hou Zheng, Jia You

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


With the development of deep sequencing, vast amounts of RNA-Seq data have been generated. It is crucial how to extract and interpret the meaningful information contained in deep sequencing data. In this paper, based on penalized matrix decomposition (PMD), a novel method, named PMDSeq, was proposed to analyze RNA-seq count data. Firstly, to obtain the differential expression matrix, the matrix of RNA-seq count data was normalized. Secondly, the differential expression matrix was decomposed into three factor matrices. By imposing appropriate constraint on factor matrices, the PMDSeq method can highlight the differentially expressed genes. Thirdly, the proposed method can identify the differentially expressed genes based on the scaled eigensamples. Finally, we used gene ontology tools to check these differentially expressed genes. The experimental results on simulation and three real RNA-seq count data sets demonstrated the effectiveness of our method.
Original languageEnglish
Article number6746660
Pages (from-to)12-18
Number of pages7
JournalIEEE Transactions on Nanobioscience
Issue number1
Publication statusPublished - 1 Jan 2014


  • Deep sequencing
  • differential expression analysis
  • gene selection
  • matrix decomposition
  • RNA-seq data

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Pharmaceutical Science
  • Computer Science Applications
  • Electrical and Electronic Engineering


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