forestPlot: Draw forest plot for differences betweeen cohorts.

In maftools: Summarize, Analyze and Visualize MAF Files

Description

Draw forest plot for differences betweeen cohorts.

Usage

forestPlot(
  mafCompareRes,
  pVal = 0.05,
  fdr = NULL,
  color = NULL,
  geneFontSize = 1.2,
  titleSize = 1.2,
  lineWidth = 2.2
)

Arguments

mafCompareRes	results from mafCompare
pVal	p-value threshold. Default 0.05.
fdr	fdr threshold. Default NULL. If provided uses adjusted pvalues (fdr).
color	vector of colors for cohorts. Default NULL.
geneFontSize	Font size for gene symbols. Default 1.2
titleSize	font size for titles. Default 1.2
lineWidth	line width for CI bars. Default 2.2

Details

Plots results from link{mafCompare} as a forest plot with x-axis as log10 converted odds ratio and differentially mutated genes on y-axis.

Value

Nothing

See Also

mafCompare

Examples

##Primary and Relapse APL
primary.apl <- system.file("extdata", "APL_primary.maf.gz", package = "maftools")
relapse.apl <- system.file("extdata", "APL_relapse.maf.gz", package = "maftools")
##Read mafs
primary.apl <- read.maf(maf = primary.apl)
relapse.apl <- read.maf(maf = relapse.apl)
##Perform analysis and draw forest plot.
pt.vs.rt <- mafCompare(m1 = primary.apl, m2 = relapse.apl, m1Name = 'Primary',
m2Name = 'Relapse', minMut = 5)
forestPlot(mafCompareRes = pt.vs.rt)

Example output

-Reading
-Validating
--Non MAF specific values in Variant_Classification column:
  ITD
-Silent variants: 45 
-Summarizing
-Processing clinical data
--Missing clinical data
-Finished in 0.421s elapsed (0.224s cpu) 
-Reading
-Validating
--Non MAF specific values in Variant_Classification column:
  ITD
-Silent variants: 19 
-Summarizing
-Processing clinical data
--Missing clinical data
-Finished in 0.159s elapsed (0.110s cpu) 

maftools documentation built on Feb. 6, 2021, 2 a.m.