Differential principal component analysis of ChIP-seq
Differential principal component analysis of ChIP-seq
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DOI：
10.1073/pnas.1204398110
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发表时间：
2013-04-23
影响因子：
11.1
通讯作者：
Ning, Yang
中科院分区：
文献类型：
--
作者：
Ji, Hongkai;Li, Xia;Ning, Yang
我们提出差分主成分分析（dPCA）来分析多个chip测序数据集，以确定两种生物条件下蛋白质- dna的差异相互作用。dPCA将无监督模式发现、降维和统计推断集成到一个框架中。它使用少量的主成分来简洁地总结两种情况下主要的多蛋白协同差异模式。对于每种模式，它通过比较重复样本的条件间差异和条件内差异来检测和优先排序差异基因组位点。dPCA为高效分析大量chip测序数据，研究不同生物条件下基因调控的动态变化提供了独特的工具。我们通过分析转录因子结合位点和启动子的差异染色质模式以及等位基因特异性蛋白质- dna相互作用来证明这种方法。
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.