Home > Events > 2013 Seminars & Colloquia > HONGKAI JI - Johns Hopkins Bloomberg School of Public Health

HONGKAI JI - Johns Hopkins Bloomberg School of Public Health

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” Differential principal component analysis of ChIP-seq”
10 October 2013 from 4:00 PM to 5:00 PM
201 Thomas Bldg.
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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.