calJSI: compareJSI

Description Usage Arguments Value References Examples

View source: R/calJSI.R

Description

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

Usage

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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

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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.