plotQC: Plot QC
In XCIR: XCI-inference

Description Usage Arguments Value Examples

This plot shows QC for skewing estimates

1	plotQC(xci_table, xcig = NULL, gene_names = "")

`xci_table`	A `data.table`. Data to plot. Should be the results of `betaBinomXI`, `getGenicDP` or one of the annotation functions.
`xcig`	A `character` vector. The names of the genes in the inactivated training set.
`gene_names`	A `character`. If left blank, only genes that are further than 20 to "all", all genes will be named. Set to "none" to remove all annotations. Alternately, a `character` vector can be passed to annotate specific genes of interest.

An invisible plot object.

library(data.table)
# Simulated data
dtf <- system.file("extdata/data2_vignette.tsv", package = "XCIR")
dt <- fread(dtf)
xcigf <- system.file("extdata/xcig_vignette.txt", package = "XCIR")
xcig <- readLines(xcigf)
# Run all models on the data
all <- betaBinomXI(dt, xciGenes = xcig)
# Simple BetaBinomial model and show histogram of skewing
bb <- betaBinomXI(dt, xciGenes = xcig, model = "BB", hist = TRUE)

# Plotting fits
stoshow <- paste0("sample", c(31, 33, 35, 40)) #interesting samples
plotQC(all[sample %in% stoshow], xcig = xcig)

# Summarizing results
# Sample information
samps <- sample_clean(all)
# Gene-level predictions
xcistates <- getXCIstate(all)