String indicating R package computing single value decomposition (SVD).ĭefault program_svd = "Rspectra" for svds. Scaling = "sd" for centered SNPs divided by standard deviation (correlation-based PCA). Scaling = "center" for centering (covariance-based PCA). Default scaling = "drift" scales SNPs to control for expected allele frequency dispersion caused by genetic drift (SMARTPCA). Missing_impute = "remove" removes SNPs with at least one missing value. If no missing values present, no effect on computation.ĭefault missing_impute = "mean" replaces missing values of each SNP by mean of non-missing values across samples. Number 9 or string NA indicating missing value.ĭefault missing_value = 9 as in EIGENSTRAT. Logical indicating if PERMDISP is computed. Logical indicating if PERMANOVA is computed. Logical FALSE or numeric vector indicating row numbers (SNPs) to be removed from computations.ĭefault snp_remove = FALSE keeps all SNPs. Logical FALSE or numeric vector indicating column numbers (samples) to be removed from computations.ĭefault sample_remove = FALSE keeps all samples. Packed_data = TRUE for compressed or binary EIGENSTRAT ( PACKENDANCESTRYMAP).Ĭharacter or numeric vector assigning samples to groups. Logical value for EIGENSTRAT, irrelevant for text data.ĭefault packed_data = FALSE assumes uncompressed EIGENSTRAT. Optimized to run fast computation for big datasets. The latter follows the SMARTPCA implementation of Patterson, Price and Reich (2006). Genetic markers such as SNPs can be scaled by centering, z-scores and genetic drift-based dispersion. Computes Principal Component Analysis (PCA) for variable x sample genotype data, such as Single Nucleotide Polymorphisms (SNP), in combination with Permutational Multivariate Analysis of Variance (PERMANOVA) and Permutational Multivariate Analysis of Dispersion (PERMDISP).Ī wrapper of functions smart_pca, smart_permanova and smart_permdisp.
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