Plot the most significant components that come from isoPLSDA() analysis together with the density of the samples scores along those components.

isoPLSDAplot(pls, n = 2)

Arguments

pls

Output from isoPLSDA() function.

n

Number of components to plot.

Value

GGally::ggpairs() plot showing the scores for each sample using isomiRs/miRNAs expression to explain variation. base::data.frame object with a first column refering to the sample group, and the following columns refering to the score that each sample has for each component from the PLS-DA analysis.

Details

The function isoPLSDAplot helps to visualize the results from isoPLSDA(). It will plot the samples using the significant components (t1, t2, t3 ...) from the PLS-DA analysis and the samples score distribution along the components. It uses GGally::ggpairs() for the plot.

Examples

data(mirData) # Only miRNAs with > 10 reads in all samples. ids <- isoCounts(mirData, minc=10, mins=6) ids <- isoNorm(ids, formula=~group)
#> converting counts to integer mode
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the #> function: y = a/x + b, and a local regression fit was automatically substituted. #> specify fitType='local' or 'mean' to avoid this message next time.
pls.ids <- isoPLSDA(ids, "group", nperm = 2) isoPLSDAplot(pls.ids)