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Research Article

Differential principal component analysis of ChIP-seq

Hongkai Ji, Xia Li, Qian-fei Wang, and Yang Ning
  1. aDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205;
  2. bCAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, People’s Republic of China; and
  3. cUniversity of Chinese Academy of Sciences, Beijing 100049, People's Republic of China

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PNAS April 23, 2013 110 (17) 6789-6794; https://doi.org/10.1073/pnas.1204398110
Hongkai Ji
aDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205;
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  • For correspondence: hji@jhsph.edu
Xia Li
bCAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, People’s Republic of China; and
cUniversity of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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Qian-fei Wang
bCAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, People’s Republic of China; and
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Yang Ning
aDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205;
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  1. Edited by Hongyu Zhao, Yale University, New Haven, CT, and accepted by the Editorial Board February 25, 2013 (received for review March 18, 2012)

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Abstract

We propose differential principal component analysis (dPCA) for analyzing multiple ChIP- sequencing datasets to identify differential protein–DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein–DNA interactions.

  • allele-specific binding
  • differential binding
  • histone modification
  • next-generation sequencing
  • RNA-seq

Footnotes

  • ↵1To whom correspondence should be addressed. E-mail: hji{at}jhsph.edu.
  • Author contributions: H.J. designed research; H.J. performed research; H.J. and Y.N. contributed new reagents/analytic tools; H.J., X.L., and Q.-f.W. analyzed data; and H.J. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission. H.Z. is a guest editor invited by the Editorial Board.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1204398110/-/DCSupplemental.

Freely available online through the PNAS open access option.

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dPCA of ChIP-seq
Hongkai Ji, Xia Li, Qian-fei Wang, Yang Ning
Proceedings of the National Academy of Sciences Apr 2013, 110 (17) 6789-6794; DOI: 10.1073/pnas.1204398110

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dPCA of ChIP-seq
Hongkai Ji, Xia Li, Qian-fei Wang, Yang Ning
Proceedings of the National Academy of Sciences Apr 2013, 110 (17) 6789-6794; DOI: 10.1073/pnas.1204398110
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