Generalized Poisson Regression Analysis of Spatially Correlated ChIP-Seq Data

• Activity Number: 558
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Min Chen*+ and Sherry Wang and Guanghua Xiao and Yang Xie
• Companies: The University of Texas Southwestern Medical Center at Dallas and Southern Methodist University and The University of Texas Southwestern Medical Center at Dallas and The University of Texas Southwestern Medical Center
• Keywords: ChIP-seq ; next generation sequencing ; spatial correlation ; auto-regressive model ; under-dispurtion

Abstract

ChIP-seq is a technology that combines chromatin immunoprecipitation and next generation sequencing to study the interactions of protein and DNA. It can precisely map DNA-binding sites in a genome-wide scale for proteins of interest and it is becoming increasingly popular nowadays. DNA binding peaks often show a spatial correlation pattern among nearby sites. We consider a generalized Poisson regression model with spatially varying coefficients to take into account spatial correlation that occurs among adjacent read counts through a first-order autoregressive model. Simulation studies and real data example suggest the method works well in terms of smoothing out noises and detecting real binding sites.