calJSI: compareJSI

In MesKit: A tool kit for dissecting cancer evolution from multi-region derived tumor biopsies via somatic alterations

Description

The Jaccard similarity index (JSI) is applied to distinguish monoclonal versus polyclonal seeding in metastases.

Usage

calJSI(
  maf,
  patient.id = NULL,
  pairByTumor = FALSE,
  min.ccf = 0,
  plot = FALSE,
  use.circle = TRUE,
  title = NULL,
  number.cex = 8,
  number.col = "#C77960",
  use.tumorSampleLabel = FALSE,
  ...
)

Arguments

maf	Maf or MafList object generated by readMaf function.
patient.id	Select the specific patients. Default NULL, all patients are included.
pairByTumor	Compare JSI between different tumors. Default FALSE.
min.ccf	The minimum value of CCF. Default 0.
plot	Logical (Default: FALSE).
use.circle	Logical (Default: TRUE). Whether to use "circle" as visualization method of correlation matrix.
title	Title of the plot Default "Jaccard similarity".
number.cex	The size of text shown in correlation plot. Default 8.
number.col	The color of text shown in correlation plot. Default "#C77960".
use.tumorSampleLabel	Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' by 'Tumor_Sample_Label'.
...	Other options passed to subMaf

Value

Correlation matrix and heatmap via Jaccard similarity coefficient method

References

Hu, Z., Li, Z., Ma, Z. et al. Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases. Nat Genet (2020).

Examples

maf.File <- system.file("extdata/", "CRC_HZ.maf", package = "MesKit")
clin.File <- system.file("extdata/", "CRC_HZ.clin.txt", package = "MesKit")
ccf.File <- system.file("extdata/", "CRC_HZ.ccf.tsv", package = "MesKit")
maf <- readMaf(mafFile=maf.File, clinicalFile = clin.File, ccfFile=ccf.File, refBuild="hg19")
calJSI(maf)

MesKit documentation built on March 28, 2021, 6 p.m.