A Bayesian Model for Poisson Processes for Power Prediction with ChIP-Seq Data

• Activity Number: 649
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Chen Zuo*+ and Sunduz Keles
• Companies: U of Wisconsin Madison and University of Wisconsin Madison
• Keywords: ChIP-seq ; Bayesian model ; statistical power ; false discovery rate

Abstract

Chromatin immunoprecipitation (ChIP) followed by high throughput sequencing (seq) enables investigators to study genome-wide enrichment of transcription factors and mapping of epigenomic marks. A challenging question for designing a ChIP-seq experiment is how deeply should the ChIP and the control samples be sequenced in order to identify the underlying targets (e.g., enrichment locations or epigenomic profiles) at a targeted power. We developed a statistical framework, named "CSSP" for ChIP-seq Statistical Power, to enable power analysis in ChIP-seq studies. We proposed a hierarchical model for the bin-level sequencing data and used empirical Bayesian methods to identify the hyper parameters associated with genomic variables. This framework thus provides an analytical approach for calculating the required sequencing depth for a targeted power while controlling the false discovery rate at a user-specified level. Evaluation of power for multiple publicly available ChIP-seq datasets indicate that current typical ChIP-seq studies are powered well for detecting large fold changes of ChIP enrichment.