![]() ![]() 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. PCA results may not be reliable, robust, or replicable as the field assumes. PCA adjustment also yielded unfavorable outcomes in association studies. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. ![]() The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. 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. 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). The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |