Genome-wide chromatin immunoprecipitation experiments including ChIP-seq and ChIP-chip, jointly referred to as ChIP-X, are high-throughput technologies to map protein-DNA interactions in the genome. When multiple related ChIP-X data sets are available, separately analyzing each data set is not optimal because it may lack power to detect consistent but relatively weak signals in multiple studies. Jointly analyzing all data sets may allow one to borrow information across studies to improve signal detection. However, a common problem in data integration is the difficulty in handling data set-specific signals that cannot be dealt with by simply assuming that the signal status for each genomic locus is the same across all studies. An integration model that naively enumerates all possible study specificity patterns, conversely, has exponential complexity because there are 2D possible combinatorial signal presence and absence patterns for D studies. Correlation motifs provide a useful solution to this problem. By introducing a small number of latent probability vectors called correlation motifs, this approach can describe the major correlation structure among multiple data sets, which can then be used to guide information sharing across data sets. The correlation motif approach is capable of improving signal detection. At the same time, it does not have the problem of exponential model complexity and is flexible enough to handle all possible data set-specific signal configurations.
ASJC Scopus subject areas