This is an implementation of contrastive PCA in Python; that was described in this paper.
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When we want to find the variations & trends within some specific variable(s) of the data. But top variations within that dataset (detected by normal PCA) are outside the context of the variable(s) of interest.
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For example: Suppose we have a dataset of gene-expression levels of a cancer patient, and we want to identify molecular subtypes of cancer.
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If we applied standard PCA, the top PC may correspond to the demographic variables for individuals (like their age, sex, ..) instead of the subtypes of cancer.
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So we can use a CPCA in that case. It uses a background dataset, which does not contain the patterns of interest.
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To conclude, if we have multiple datasets, and we're interested in discovering the sources of variation that are enriched in one dataset relative to the other, we can use contrastive PCA.
