plotExpHeatmap: Visualize expression heatmap by chromosome for...

In JEFworks/badger: Bayesian approach for detecting copy number alterations from single cell RNA-seq data

Description

Visualize expression heatmap by chromosome for expression-based karyotyping

Usage

plotExpHeatmap(mat.tot, gos, zlim = c(-2, 2), window.size = 101,
  autosomesOnly = TRUE, orderCells = FALSE)

Arguments

mat.tot	Gene expression matrix
gos	Dataframe of positional information for each gene in the gene expression matrix
zlim	Lower and upper bounds for heatmap. Default: c(-2,2)
window.size	Window size for sliding window average. Default: 101
autosomesOnly	Boolean for whether to plot only autosomes. Default: TRUE, if false will also plot X chromosome
orderCells	Booean for whether to automaticaly cluster and order cells. Default: FALSE

Value

List of sliding window averaged expression by chromosome along with heatmap representation

Examples

data('MM16.counts')
mat <- log2(MM16.counts + 1)
data('Normal.counts')
mat.ref <- log2(Normal.counts + 1)
mats <- normalizedExpression(mat, mat.ref)
mat.tot <- cbind(mats[[1]], mats[[2]])
library(biomaRt) ## for gene coordinates
mart.obj <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", dataset = 'hsapiens_gene_ensembl', host = "jul2015.archive.ensembl.org")
gos <- getBM(values=rownames(mat.tot),attributes=c("ensembl_gene_id","chromosome_name","start_position","end_position"),filters=c("ensembl_gene_id"),mart=mart.obj)
gos$pos <- (gos$start_position + gos$end_position)/2
tl <- plotExpHeatmap(mat.tot, gos, zlim=c(-0.5,0.5), window.size = 201)

JEFworks/badger documentation built on May 7, 2019, 7:40 a.m.