A statistical framework for power calculations in ChIP-seq experiments

• Published in: Bioinformatics 2014-03, Vol.30 (6), p.753-760
• Main Authors: Zuo, Chandler, Keleş, Sündüz
• Format: Article
• Language: English
• Subjects: Binding; Bioinformatics; Chips; Chromatin Immunoprecipitation - methods; Escherichia coli - genetics; High-Throughput Nucleotide Sequencing - methods; Mathematical models; Oligonucleotide Array Sequence Analysis - methods; Original Papers; Pilot Projects; Samples; Sequencing; Statistical analysis; Statistical methods
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btt200

ChIP-seq technology enables investigators to study genome-wide binding of transcription factors and mapping of epigenomic marks. Although the availability of basic analysis tools for ChIP-seq data is rapidly increasing, there has not been much progress on the related design issues. A challenging question for designing a ChIP-seq experiment is how deeply should the ChIP and the control samples be sequenced? The answer depends on multiple factors some of which can be set by the experimenter based on pilot/preliminary data. The sequencing depth of a ChIP-seq experiment is one of the key factors that determine whether all the underlying targets (e.g. binding locations or epigenomic profiles) can be identified with a targeted power. We developed a statistical framework named CSSP (ChIP-seq Statistical Power) for power calculations in ChIP-seq experiments by considering a local Poisson model, which is commonly adopted by many peak callers. Evaluations with simulations and data-driven computational experiments demonstrate that this framework can reliably estimate the power of a ChIP-seq experiment at different sequencing depths based on pilot data. Furthermore, it provides an analytical approach for calculating the required depth for a targeted power while controlling the false discovery rate at a user-specified level. Hence, our results enable researchers to use their own or publicly available data for determining required sequencing depths of their ChIP-seq experiments and potentially make better use of the multiplexing functionality of the sequencers. Evaluation of power for multiple public ChIP-seq datasets indicate that, currently, typical ChIP-seq studies are powered well for detecting large fold changes of ChIP enrichment over the control sample, but they have considerably less power for detecting smaller fold changes. Available at www.stat.wisc.edu/~zuo/CSSP. [e-mail] Supplementary data are available at Bioinformatics online.