I could only manage the “parallel” viewing by making the plot very small. The default is IPD=63.5 for the”parallel" technique.
I specified IPD=-63.5 to switch to “cross-eyed’ mode. I specified the limits of the graph to add a bit of white space around the points. Guides(colour=guide_legend(override.aes=list(alpha=1))) +įacet_stereo(IPD=-63.5) + theme(legend.position='bottom') + Ggplot(pca.df, aes(x=PC1, y=PC2, depth=-PC3, color=super.population)) + Now the same data with a stereogram using the ggforce R package: # add some white space around the points Ggplot(pca.df, aes(x=PC1, y=PC3, color=super.population)) + Guides(colour=guide_legend(override.aes=list(alpha=1))) The typical PCA looks like this: ggplot(pca.df, aes(x=PC1, y=PC2, color=super.population)) + Homozygous refs were then converted to 0, heterozygous variants to 1 and homozygous alts to 2. I downloaded a subset of variants in the 1000 Genomes Project Phase 3 that had at least 10 alternate alleles in called genotypes in autosomes (0.1% of variants with AC>=10). We’ll use PC1/ PC2 for the x/y axis, as usual, and PC3 for the “depth” dimension. STEREOGRAM SHARK HOW TO
Now let’s see how to make a stereogram of the typical PCA plot using the 1000 Genomes Project data. Interbranchial septum Septal canal Lamellae Filaments FIGURE 7-2 Stereogram of a portion of two gill filaments from one - half of a gill arch of the dogfish. Making a PCA stereogram using the 1000 Genomes Project data
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