Contrastive principal component analysis (cPCA) to explore patterns specific to a dataset

Interesting open access paper Exploring patterns enriched in a dataset with contrastive principal component analysis, by Abid, Zhang, Bagaria & Zou, Nature Communications (2018) 9:2134.

Abstract (emphasis mine):

Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis

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Human ancestry: how to work your own PCA, ADMIXTURE analyses for human evolutionary and genealogical studies

I wrote two days ago in the post anouncing the revised version (October 2017) of the Indo-European demic diffusion model, about dumping the information I had on doing PCA and ADMIXTURE analyses as ‘drafts’, without reviewing them, in the new section of this website called Human Ancestry.

I had some time today to review them, and to correct gross mistakes in the texts, so that they might be more usable now…

I began to work with free datasets to see if I could learn something more about results of recent Genetic research by working with the available … Read the rest “Human ancestry: how to work your own PCA, ADMIXTURE analyses for human evolutionary and genealogical studies”