If program_svd = "Rspectra" this argument indicates number of PCA axes computed starting with PCA axis 1.ĭefault pc_axes = 2 computes PCA axes 1 and 2. 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 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.
File extension detected automatically whether text or EIGENSTRAT. SNP values must be count data (no decimals allowed). SNP = rows, samples = columns without row names or column headings. Optimized to run fast single value decomposition for big datasets. Such as single nucleotide polymorphisms (SNP) following Patterson, Price and Reich (2006).
SMARTPCA scaling controls for genetic drift when variables are bi-allelic genetic markers In smartsnp: Fast Multivariate Analyses of Big Genomic Dataĭescription Arguments Details Value References See Also ExamplesĬompute Principal Component Analysis (PCA) for variable x sample genotype data including covariance ( centered), correlation (z-score) and SMARTPCA scaling,Īnd implements projection of ancient samples onto modern PCA space.
0 kommentar(er)
