A novel statistical method for quantitative comparison of multiple ChIP-seq datasets

Li Chen, Chi Wang, Zhaohui S. Qin, Hao Wu

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Motivation: ChIP-seq is a powerful technology to measure the protein binding or histone modification strength in the whole genome scale. Although there are a number of methods available for single ChIP-seq data analysis (e.g. 'peak detection'), rigorous statistical method for quantitative comparison of multiple ChIP-seq datasets with the considerations of data from control experiment, signal to noise ratios, biological variations and multiple-factor experimental designs is under-developed. Results: In this work, we develop a statistical method to perform quantitative comparison of multiple ChIP-seq datasets and detect genomic regions showing differential protein binding or histone modification. We first detect peaks from all datasets and then union them to form a single set of candidate regions. The read counts from IP experiment at the candidate regions are assumed to follow Poisson distribution. The underlying Poisson rates are modeled as an experiment-specific function of artifacts and biological signals. We then obtain the estimated biological signals and compare them through the hypothesis testing procedure in a linear model framework. Simulations and real data analyses demonstrate that the proposed method provides more accurate and robust results compared with existing ones.

Original languageEnglish
Pages (from-to)1889-1896
Number of pages8
JournalBioinformatics
Volume31
Issue number12
DOIs
StatePublished - Jun 15 2015

Bibliographical note

Publisher Copyright:
© The Author 2015. Published by Oxford University Press.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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