calJSI {MesKit} | R Documentation

## compareJSI

### 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)

[Package MesKit version 1.4.0 Index]