Differential principal component analysis of ChIP-seq

Hongkai Ji, Xia Li, Qian Fei Wang, Yang Ning

Research output: Contribution to journalArticlepeer-review


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 geno-mic 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.

Original languageEnglish (US)
Pages (from-to)6789-6794
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number17
StatePublished - Apr 23 2013


  • Allele-specific binding
  • Differential binding
  • Histone modification
  • Next-generation sequencing
  • RNA-seq

ASJC Scopus subject areas

  • General


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