plotQC: Plot QC

Description Usage Arguments Value Examples

View source: R/betabin.R

Description

This plot shows QC for skewing estimates

Usage

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plotQC(xci_table, xcig = NULL, gene_names = "", showsd = TRUE, mean = TRUE)

Arguments

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.

Value

An invisible plot object.

Examples

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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 ~~A~~~
bb <- betaBinomXI(dt, xciGenes = xcig, model = "M0", 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)

SRenan/XCIR documentation built on Oct. 8, 2021, 3:11 a.m.