GenePathwayOncoplots {pathwayTMB}

draw an GenePathwayOncoplots

Description

takes output generated by read.maf and draws an GenePathwayOncoplots.

Usage

GenePathwayOncoplots(
  maffile,
  gene_path,
  freq_matrix,
  risk_score,
  cut_off,
  final_character,
  isTCGA = FALSE,
  top = 20,
  clinicalFeatures = "sample_group",
  annotationColor = c("red", "green"),
  sortByAnnotation = TRUE,
  removeNonMutated = FALSE,
  drawRowBar = TRUE,
  drawColBar = TRUE,
  leftBarData = NULL,
  leftBarLims = NULL,
  rightBarData = NULL,
  rightBarLims = NULL,
  topBarData = NULL,
  logColBar = FALSE,
  draw_titv = FALSE,
  showTumorSampleBarcodes = FALSE,
  fill = TRUE,
  showTitle = TRUE,
  titleText = NULL
)

Arguments

maffile

an MAF object generated by read.maf.

gene_path

User input pathways geneset list.

freq_matrix

The mutations matrix,generated by 'get_mut_matrix'.

risk_score

Samples' PTMB-related risk score,which could be a biomarker for survival analysis and immunotherapy prediction.

cut_off

A threshold value(the median risk score as the default value).Using this value to divide the sample into high and low risk groups with different overall survival.

final_character

The pathway signature,use to map gene in the GenePathwayOncoplots.

isTCGA

Is input MAF file from TCGA source. If TRUE uses only first 12 characters from Tumor_Sample_Barcode.

top

how many top genes to be drawn,genes are arranged from high to low depending on the frequency of mutations. defaults to 20.

clinicalFeatures

columns names from 'clinical.data' slot of MAF to be drawn in the plot. Dafault "sample_group".

annotationColor

Custom colors to use for sample annotation-"sample_group". Must be a named list containing a named vector of colors. Default "red" and "green".

sortByAnnotation

logical sort oncomatrix (samples) by provided 'clinicalFeatures'. Sorts based on first 'clinicalFeatures'. Defaults to TRUE. column-sort.

removeNonMutated

Logical. If TRUE removes samples with no mutations in the GenePathwayOncoplots for better visualization. Default FALSE.

drawRowBar

logical. Plots righ barplot for each gene. Default TRUE.

drawColBar

logical plots top barplot for each sample. Default TRUE.

leftBarData

Data for leftside barplot. Must be a data.frame with two columns containing gene names and values. Default 'NULL'.

leftBarLims

limits for 'leftBarData'. Default 'NULL'.

rightBarData

Data for rightside barplot. Must be a data.frame with two columns containing to gene names and values. Default 'NULL' which draws distibution by variant classification. This option is applicable when only 'drawRowBar' is TRUE.

rightBarLims

limits for 'rightBarData'. Default 'NULL'.

topBarData

Default 'NULL' which draws absolute number of mutation load for each sample. Can be overridden by choosing one clinical indicator(Numeric) or by providing a two column data.frame contaning sample names and values for each sample. This option is applicable when only 'drawColBar' is TRUE.

logColBar

Plot top bar plot on log10 scale. Default FALSE.

draw_titv

logical Includes TiTv plot. Default FALSE

showTumorSampleBarcodes

logical to include sample names.

fill

Logical. If TRUE draws genes and samples as blank grids even when they are not altered.

showTitle

Default TRUE.

titleText

Custom title. Default 'NULL'.

Value

No return value

Examples

#get the path of the mutation annotation file and samples' survival data
maf<-system.file("extdata","data_mutations_extended.txt",package = "pathwayTMB")
sur_path<-system.file("extdata","sur.csv",package = "pathwayTMB")
sur<-read.csv(sur_path,header=TRUE,row.names = 1)
#perform the function 'get_mut_matrix'
mut_matrix<-get_mut_matrix(maffile=maf,mut_fre = 0.01,is.TCGA=FALSE,sur=sur)
#perform the function `get_PTMB`
PTMB_matrix<-get_PTMB(freq_matrix=mut_matrix,genesmbol=genesmbol,gene_path=gene_path)
set.seed(1)
final_character<-get_final_signature(PTMB=PTMB_matrix,sur=sur)
#calculate the risksciore
riskscore<-plotKMcurves(t(PTMB_matrix[final_character,]),sur=sur,plots=FALSE)$risk_score
cut<-median(riskscore)
GenePathwayOncoplots(maf,gene_path,mut_matrix,riskscore,cut,final_character)