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PCA_critique
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# PCA_critique This depository accompanies the manuscript: **Principal Component Analyses (PCA)‑based findings in population genetic studies are highly biased and must be reevaluated** Eran Elhaik Lund University, Department of Biology, Lund, Sweden, 22362 _**Abstract:**_ Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well‑cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color‑based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000‑216,000 genetic studies should be reevaluated. An alternative mixed‑admixture population genetic model is discussed. Please address all correspondence to Eran Elhaik at eran.elhaik@bio.lu.se *Keywords:* Principal Component Analyses (PCA), population genetics, ancient DNA, origins, biogeography, admixture The files in this depository can be used to replicate all the analyses and figures included in the manuscirpt. The depository includes the following files: * Matlab_code_V3.zip - All the Matlab's code necessray to replicate the figures and analyses in this masnucript. * Main_PCA_Colors_html.zip - All the analyses for the color populations with the results in a single html file (Matlab publish) * Main_PCA_Pops_html.zip - All the analyses for the real populations with the results in a single html file (Matlab publish) Link to the publicaiton: https://www.nature.com/articles/s41598-022-14395-4 Supplementary materials (Supplementary Information 1) and Supplementary Texts 1-3 (Supplementary Information 2) are available at the publisher's site: https://www.nature.com/articles/s41598-022-14395-4 The genomic data associated with the code can be found here: https://figshare.com/articles/dataset/PCA_critique_zip/21755864
