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This is ready for review @standage. Let's talk next week about it! |
standage
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Oct 15, 2025
| centroids = function(groups, pca, inpath, ID) { | ||
| dir.create(file.path(inpath, "Centroids_Plots"), showWarnings = FALSE, recursive=TRUE) | ||
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| ancestry_colors = read.table("/Users/rebecca.mitchell/Desktop/ancestry_colors.txt", header=T, sep="\t") %>% |
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To fix, if not already: hard-coded path
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| ncols=ncol(geno) | ||
| geno_filt=geno[,c(7:ncols)] | ||
| snps = data.frame("snp_id"=colnames(geno_filt)) | ||
| snps = snps %>% |
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Code autoformatting could give a more consistent style in these files. Something to consider.
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| ancestry_colors = read.table("/Users/rebecca.mitchell/Desktop/ancestry_colors.txt", header=T, sep="\t") %>% | ||
| add_row(id = "Unk", reg = "Unk", population = "Unk", color="red", superpop_color="red") %>% | ||
| add_row(id= "Centroid", reg = "Centroid", population = "Centroid", color = "black", superpop_color="black") |
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Add ancestry prediction using PCA to MixDeR. Reference data from 1000 Genomes is used in PCA.
The method first performs an initial deconvolution using the same method as established in the mixture deconvolution step utilizing the 1000 Genomes Global population allele frequency data. The 54 ancestry SNPs are then extracted from each single source inferred SNP profile, added to the 1000 Genomes reference dataset and PCA is performed for each contributor. The PCA plots are created and saved to the specified output directory. The user can then examine and determine if the contributor's ancestry can be determined. The user can then choose to use the predicted super population allele frequency data in the mixture deconvolution step (which will also be provided in MixDeR) or use the global allele frequency data.