COCOA is a method for understanding variation among samples. COCOA can be used with data that includes genomic coordinates such as DNA methylation. To describe the method on a high level, COCOA uses a database of "region sets" and principal component analysis (PCA) of your data to identify sources of variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. In contrast to some other common techniques, COCOA is unsupervised, meaning that samples do not have to be divided into groups such as case/control or healthy/disease, although COCOA works in those situations as well. Also, COCOA focuses on continuous variation between samples instead of having cutoffs. Because of this, COCOA can be used as a complementary method alongside "differential" methods that find discrete differences between groups of samples and it can also be used in situations where there are no groups. COCOA can identify biologically meaningful sources of variation between samples and increase understanding of variation in your data.
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