This function can be run in two ways: 1) Without 'thresholds' specified. This will run t-SNE for the input vcf without filtering, and visualize the clustering of samples in two-dimensional space, coloring each sample according to a priori population assignment given in the popmap. 2) With 'thresholds' specified. This will filter your input vcf file to the specified missing data thresholds, and run a t-SNE clustering analysis for each filtering iteration. For each iteration, a 2D plot will be output showing clustering according to the specified popmap. This option is ideal for assessing the effects of missing data on clustering patterns.
assess_missing_data_tsne(
vcfR,
popmap = NULL,
thresholds = NULL,
perplexity = NULL,
iterations = NULL,
initial_dims = NULL,
clustering = TRUE
)a series of plots showing the clustering of all samples in two-dimensional space
a vcfR object
set of population assignments that will be used to color code the plots
a vector specifying the missing data filtering thresholds to explore
numerical value specifying the perplexity paramter during t-SNE (default: 5)
a numerical value specifying the number of iterations for t-SNE (default: 1000)
a numerical value specifying the number of initial_dimensions for t-SNE (default: 5)
use partitioning around medoids (PAM) to do unsupervised clustering on the output? (default = TRUE, max clusters = # of levels in popmap + 2)
assess_missing_data_tsne(vcfR = SNPfiltR::vcfR.example,
popmap = SNPfiltR::popmap,
thresholds = .8)
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