We propose Differential Principal Component Analysis (dPCA) for characterizing differences between two biological conditions with respect to multiple ChIP-seq data sets. dPCA describes major differential patterns between two conditions using a small number of principal components. Each component corresponds to a multi-dataset covariation pattern shared by many genomic loci. The analysis prioritizes genomic loci based on each pattern, and for each pattern, it identifies loci with significant between-condition changes after considering variability among replicate samples. This approach provides an integrated solution to dimension reduction, unsupervised pattern discovery, and statistical inference. We demonstrate dPCA through analyses of differential chromatin patterns at transcription factor binding sites and human promoters using ENCODE data.
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