Dimension Reduction with GLMplot

This function computes and plots generalized principal components analysis for dimension reduction of count expression matrix.

GLMplot(
  exploredds,
  L = 2,
  plotly = FALSE,
  savePlot = FALSE,
  filePlot = NULL,
  ...
)

Arguments

exploredds	object of class DESeq2::DESeqDataSet(), generated from exploreDDS function.
L	desired number of latent dimensions (positive integer).
plotly	logical: when FALSE (default), the ggplot2 plot will be returned. TRUE option returns the plotly version of the plot.
savePlot	logical: when FALSE (default), the plot will not be saved. If TRUE the plot will be saved, and requires the filePlot argument.
filePlot	file name where the plot will be saved. For more information, please consult the ggplot2::ggsave() function.
...	additional parameters for the glmpca::glmpca() function.

Value

returns an object of ggplot or plotly class.

References

F. William Townes and Kelly Street (2020). glmpca: Dimension Reduction of Non-Normally Distributed Data. R package version 0.2.0. https://CRAN.R-project.org/package=glmpca

Examples

## Targets file
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(file = targetspath, format = "matrix", 
delim = "-")
#> 
## Count table file
countMatrixPath <- system.file("extdata", "countDFeByg.xls", 
package = "systemPipeR")
countMatrix <- read.delim(countMatrixPath, row.names = 1)
## Plot
exploredds <- exploreDDS(countMatrix, targets, cmp = cmp[[1]], 
preFilter = NULL, transformationMethod = "raw")
GLMplot(exploredds, plotly = FALSE)