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Fig. 2 | BMC Bioinformatics

Fig. 2

From: A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data

Fig. 2

(top) The proportions of the variances explained by the first 6 and 10 PCs. The data consists of 150 samples, each having 1458 SNPs. (bottom) The CPU time of implementing MCPCA_PopGen given different choice of q when the number of SNPs ranges from 1000 to 16,000. MCPCA-TG: MCPCA applied to the true genotype data; MCPCA-DS: MCPCA applied to the genotype dosage data; MCPCA-Intv, MCPCA-Freq, and MCPCA-Jenks: MCPCA using the equal width, equal frequency, and Jenks binning methods

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BMC Bioinformatics

ISSN: 1471-2105

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