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PNAS:Differential principal component analysis of ChIP-seq  

2013-04-24 09:03:44|  分类: 生物信息分析 |  标签: |举报 |字号 订阅

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Abstract

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 genomic 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.

PNAS:Differential principal component analysis of ChIP-seq - 喜欢吃桃子 - wangyufeng的博客
 dPCA. The objective of dPCA is to compare two conditions. Each condition has multiple TF or HM ChIP-seq datasets. Each dataset has several replicates. dPCA attempts to characterize differences at a list of user-specified genomic loci. The plot shows the major steps of dPCA.
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