Package 'MesKit'

Title: A tool kit for dissecting cancer evolution from multi-region derived tumor biopsies via somatic alterations
Description: MesKit provides commonly used analysis and visualization modules based on mutational data generated by multi-region sequencing (MRS). This package allows to depict mutational profiles, measure heterogeneity within or between tumors from the same patient, track evolutionary dynamics, as well as characterize mutational patterns on different levels. Shiny application was also developed for a need of GUI-based analysis. As a handy tool, MesKit can facilitate the interpretation of tumor heterogeneity and the understanding of evolutionary relationship between regions in MRS study.
Authors: Mengni Liu [aut, cre] , Jianyu Chen [aut, ctb] , Xin Wang [aut, ctb]
Maintainer: Mengni Liu <[email protected]>
License: GPL-3
Version: 1.17.0
Built: 2024-11-14 05:49:34 UTC
Source: https://github.com/bioc/MesKit

Help Index

calFst

Description

Genetic divergence between regions of subclonal sSNVs using the Weir and Cockerham method

Usage

calFst(
  maf,
  patient.id = NULL,
  min.vaf = 0,
  min.total.depth = 2,
  use.adjVAF = FALSE,
  plot = TRUE,
  withinTumor = FALSE,
  use.circle = TRUE,
  title = NULL,
  number.cex = 8,
  number.col = "#C77960",
  use.tumorSampleLabel = FALSE,
  ...
)

Arguments

maf

A Maf or MafList object generated by readMaf function.
patient.id

Select the specific patients. Default NULL, all patients are included.
min.vaf

Specify The minimum VAF to filter variants. Default 0.
min.total.depth

The minimum total allele depth for filtering variants. Default 2.
use.adjVAF

Use adjusted VAF in analysis when adjusted VAF or CCF is available. Default FALSE.
plot

Logical (Default: TRUE).
withinTumor

Logical (Default: FALSE). Whether calculate between-region heterogeneity within tumors.
use.circle

Logical (Default: TRUE). Whether use "circle" in the plot. as visualization method of correlation matrix
title

The title of the plot. Default "Nei's distance"
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

A list contains Fst value of MRS and Hudson estimator of each sample-pair, respectively.

References

Sun R, Hu Z, Sottoriva A, et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat Genet. 2017;49(7):1015-1024.

Bhatia G, Patterson N, Sankararaman S, Price AL. Estimating and interpreting FST: the impact of rare variants. Genomic Res. 2013;23(9):1514-1521.

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")
calFst(maf)

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)

calNeiDist

Description

Nei's distance of CCF for sample/tumor pair.

Usage

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

Arguments

maf

A Maf or MafList object generated by readMaf function.
patient.id

Select the specific patients. Default NULL, all patients are included.
withinTumor

Calculate between-region heterogeneity within tumor. (Default: FALSE).
min.ccf

Specify the minimum CCF. Default 0.
plot

Logical (Default: TRUE).
use.circle

Logical (Default: TRUE). Whether to use "circle" as visualization method of correlation matrix.
title

The title of the plot. Default "Nei's distance"
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

Nei's genetic distance matrix and heatmap of sample-pairs from the same patient

References

Lee JK, Wang J, Sa JK, et al. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nat Genet. 2017;49(4):594-599.

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")
calNeiDist(maf)

ccfAUC

Description

The tumor heterogeneity was estimated as the area under the curve (AUC) of the cumulative density function from all cancer cell fractions per tumor

Usage

ccfAUC(
  maf,
  patient.id = NULL,
  min.ccf = 0,
  withinTumor = FALSE,
  plot.density = TRUE,
  use.tumorSampleLabel = FALSE,
  ...
)

Arguments

maf

A Maf or MafList object generated by readMaf function.
patient.id

Select the specific patients. Default NULL, all patients are included.
min.ccf

The minimum value of CCF. Default 0.
withinTumor

Calculate between-region heterogeneity within tumor. Default FALSE.
plot.density

Whether to show the density plot. Default TRUE.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' by 'Tumor_Sample_Label'.
...

Other options passed to subMaf

Value

A list containing AUC of CCF and a graph

References

Charoentong P, Finotello F, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell reports 2017, 18:248-262.

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")
ccfAUC(maf)

classifyMut

Description

classifyMut

Usage

classifyMut(maf, patient.id = NULL, class = "SP", classByTumor = FALSE, ...)

Arguments

maf

Maf or MafList object generated by readMaf function. Classify SSNVs/Indels into Shared/P-shared/Private, Clonal/Subclonl or Shared-Clonal/P-shared-Clonal/Private-Clonal/Shared-Subclonal/P-shared-SubClonal/Private-SubClonal
patient.id

Select the specific patients. Default NULL, all patients are included
class

The class which would be represented. Default: "SP" (Shared pattern: Public/Shared/Private), other options: "CS" (Clonal status: Clonal/Subclonl) and "SPCS".
classByTumor

Logical (Default: FALSE). Classify mutations based on "Tumor_ID".
...

Other options passed to subMaf

Value

A data.frame with classification of mutations for each patient

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")
classifyMut(maf, class = "SP")

cna2gene

Description

cna2gene

Usage

cna2gene(seg, txdb, min.overlap.len = 50, geneList = NULL)

Arguments

seg

seg object generated by readSegment function.
txdb

A TxDb object. i.e., TxDb.Hsapiens.UCSC.hg19.knownGene. Default NULL.
min.overlap.len

The minimum insertion size of segment and gene. Default 50.
geneList

The list of genes used to limit the annotation.Default NULL.

Value

seg object

Examples

segFile <- system.file("extdata", "CRC_HZ.seg.txt", package = "MesKit")
gisticAmpGenesFile <- system.file("extdata", "COREAD_amp_genes.conf_99.txt", package = "MesKit")
gisticDelGenesFile <- system.file("extdata", "COREAD_del_genes.conf_99.txt", package = "MesKit")
gisticAllLesionsFile <- system.file("extdata", "COREAD_all_lesions.conf_99.txt", package = "MesKit")
seg <- readSegment(segFile = segFile,
                   gisticAmpGenesFile = gisticAmpGenesFile,
                    gisticDelGenesFile = gisticDelGenesFile, 
                   gisticAllLesionsFile = gisticAllLesionsFile)
                   
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
cna2gene(seg, txdb = TxDb.Hsapiens.UCSC.hg19.knownGene)

compareCCF

Description

Compare the CCF between samples/tumor pairs This function requires CCF for clustering

Usage

compareCCF(
  maf,
  patient.id = NULL,
  min.ccf = 0,
  pairByTumor = FALSE,
  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.
min.ccf

The minimum value of CCF. Default 0.
pairByTumor

Pair by tumor types in each patients. Default FALSE.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' by 'Tumor_Sample_Label'.
...

Other options passed to subMaf

Value

a result list of CCF comparing between samples/tumor pairs

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")
compareCCF(maf)

compareTree

Description

Compares two phylogenetic trees and returns a detailed report of several distance methods

Usage

compareTree(
  phyloTree1,
  phyloTree2,
  plot = FALSE,
  min.ratio = 1/20,
  show.bootstrap = FALSE,
  use.tumorSampleLabel = FALSE
)

Arguments

phyloTree1

A phyloTree object generated by getPhyloTree function.
phyloTree2

A phyloTree object generated by getPhyloTree function.
plot

Logical (Default: FALSE). If TRUE, two trees will be plotted on the same device and their similarities will be shown.
min.ratio

Double, Default 1/20. If min.ratio is not NULL, all edge length which are smaller than min.ratio*the longest edge length will be reset as min.ratio*longest edge length.
show.bootstrap

Logical (Default: FALSE). Whether to add bootstrap value on internal nodes.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' by 'Tumor_Sample_Label'.

Value

A vector containing the following tree distance methods by R package phangorn Symmetric.difference Robinson-Foulds distance KF-branch distance the branch score distance (Kuhner & Felsenstein 1994) Path.difference difference in the path length, counted as the number of branches Weighted.path.difference difference in the path length, counted using branches lengths

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")


phyloTree1 <- getPhyloTree(maf$V402, method = "NJ")
phyloTree2 <- getPhyloTree(maf$V402, method = "MP")
compareTree(phyloTree1, phyloTree2)
compareTree(phyloTree1, phyloTree2, plot = TRUE)

fitSignatures

Description

Find nonnegative linear combination of mutation signatures to reconstruct matrix and calculate cosine similarity based on somatic SNVs.

Usage

fitSignatures(
  tri_matrix = NULL,
  patient.id = NULL,
  signaturesRef = "cosmic_v2",
  associated = NULL,
  min.mut.count = 15,
  signature.cutoff = 0.1
)

Arguments

tri_matrix

A matrix or a list of matrix generated by triMatrix function.
patient.id

Select the specific patients. Default NULL, all patients are included
signaturesRef

Signature reference,Users can upload their own reference. Default "cosmic_v2". Option: "exome_cosmic_v3","nature2013".
associated

Associated Vector of associated signatures. If given, will narrow the signatures reference to only the ones listed. Default NULL.
min.mut.count

The threshold for the variants in a branch. Default 15.
signature.cutoff

Discard any signature relative contributions with a weight less than this amount. Default 0.1.

Value

A list of data frames, each one contains treeMSOutput, containing information about each set/branch's mutational signature.

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")

## Load a reference genome.
library(BSgenome.Hsapiens.UCSC.hg19)

phyloTree <- getPhyloTree(maf, patient.id = 'V402')
tri_matrix <- triMatrix(phyloTree)
fitSignatures(tri_matrix)

getBinaryMatrix

Description

getBinaryMatrix

Usage

getBinaryMatrix(object)

## S4 method for signature 'phyloTree'
getBinaryMatrix(object)

Arguments

object

An object of phyloTree

Value

Binary matrix of phyloTree

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")
phyloTree <- getPhyloTree(maf)
getBinaryMatrix(phyloTree$V402)

getBootstrapValue

Description

getBootstrapValue

Usage

getBootstrapValue(object)

## S4 method for signature 'phyloTree'
getBootstrapValue(object)

Arguments

object

An object of phyloTree

Value

Bootstrap value of phyloTree

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")
phyloTree <- getPhyloTree(maf)
getBootstrapValue(phyloTree$V402)

getBranchType

Description

getBranchType

Usage

getBranchType(object)

## S4 method for signature 'phyloTree'
getBranchType(object)

Arguments

object

An object of phyloTree

Value

Branch type of phyloTree

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")
phyloTree <- getPhyloTree(maf)
getBranchType(phyloTree$V402)

getCCFMatrix

Description

getCCFMatrix

Usage

getCCFMatrix(object)

## S4 method for signature 'phyloTree'
getCCFMatrix(object)

Arguments

object

An object of phyloTree

Value

CCF matrix of phyloTree

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")
phyloTree <- getPhyloTree(maf)
getCCFMatrix(phyloTree$V402)

getMafData

Description

getMafData

Usage

getMafData(object)

## S4 method for signature 'Maf'
getMafData(object)

Arguments

object

An object of Maf

Value

Maf data

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")
getMafData(maf$V402)

getMafPatient

Description

getMafPatient

Usage

getMafPatient(object)

## S4 method for signature 'Maf'
getMafPatient(object)

Arguments

object

An object of Maf

Value

Human reference genome versions of Maf

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")
getMafPatient(maf$V402)

getMafRef

Description

getMafRef

Usage

getMafRef(object)

## S4 method for signature 'Maf'
getMafRef(object)

Arguments

object

An object of Maf

Value

Human reference genome versions of Maf

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")
getMafRef(maf$V402)

getMutBranches

Description

getMutBranches

Usage

getMutBranches(object)

## S4 method for signature 'phyloTree'
getMutBranches(object)

Arguments

object

An object of phyloTree

Value

Branches mutation of phyloTree

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")
phyloTree <- getPhyloTree(maf)
getMutBranches(phyloTree$V402)

getNonSyn_vc

Description

getNonSyn_vc

Usage

getNonSyn_vc(object)

## S4 method for signature 'Maf'
getNonSyn_vc(object)

Arguments

object

An object of Maf

Value

A list of Variant classifications which are considered as non-silent.

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")
getNonSyn_vc(maf$V402)

getPhyloTree

Description

getPhyloTree

Usage

getPhyloTree(
  maf,
  patient.id = NULL,
  method = "NJ",
  min.vaf = 0,
  min.ccf = 0,
  bootstrap.rep.num = 100,
  ...
)

Arguments

maf

Maf or MafList object generated by readMaf function
patient.id

Select the specific patients. Default NULL, all patients are included.
method

Approach to construct phylogenetic trees. Choose one of "NJ"(Neibor-Joining), "MP"(maximum parsimony), "ML"(maximum likelihood), "FASTME.ols" or "FASTME.bal".
min.vaf

The minimum value of vaf. Default 0.
min.ccf

The minimum value of CCF. Default 0
bootstrap.rep.num

Bootstrap iterations. Default 100.
...

Other options passed to subMaf

Value

PhyloTree or phyloTreeList object

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")
phyloTree <- getPhyloTree(maf)

getPhyloTreePatient

Description

getPhyloTreePatient

Usage

getPhyloTreePatient(object)

## S4 method for signature 'phyloTree'
getPhyloTreePatient(object)

Arguments

object

An object of phyloTree

Value

patientID of phyloTree

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")
phyloTree <- getPhyloTree(maf)
getPhyloTreePatient(phyloTree$V402)

getPhyloTreeRef

Description

getPhyloTreeRef

Usage

getPhyloTreeRef(object)

## S4 method for signature 'phyloTree'
getPhyloTreeRef(object)

## S4 method for signature 'phyloTree'
getPhyloTreeTsbLabel(object)

Arguments

object

An object of phyloTree

Value

Reference genome versions of phyloTree

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")
phyloTree <- getPhyloTree(maf)
getPhyloTreeRef(phyloTree$V402)

getPhyloTreeRef

Description

getPhyloTreeRef

Usage

getPhyloTreeTsbLabel(object)

Arguments

object

An object of phyloTree

Value

relationship between Tumor_Sample_Barcode and Tumor_Sample_Label

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")
phyloTree <- getPhyloTree(maf)
getPhyloTreeTsbLabel(phyloTree$V402)

getSampleInfo

Description

getSampleInfo

Usage

getSampleInfo(object)

## S4 method for signature 'Maf'
getSampleInfo(object)

Arguments

object

An object of Maf

Value

Sample information

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")
getSampleInfo(maf$V402)

getTree

Description

getTree

Usage

getTree(object)

## S4 method for signature 'phyloTree'
getTree(object)

Arguments

object

An object of phyloTree

Value

Tree object of phyloTree

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")
phyloTree <- getPhyloTree(maf)
getTree(phyloTree$V402)

getTreeMethod

Description

getTreeMethod

Usage

getTreeMethod(object)

## S4 method for signature 'phyloTree'
getTreeMethod(object)

Arguments

object

An object of phyloTree

Value

Tree construction method of phyloTree

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")
phyloTree <- getPhyloTree(maf)
getTreeMethod(phyloTree$V402)

Maf class

Description

Maf class.

Slots

data

data.table of MAF file containing somatic mutations.

sample.info

data.frame of sample information per patient.

nonSyn.vc

list of variant classifications which are considered as non-silent. Default NULL, use Variant Classifications with "Frame_Shift_Del", "Frame_Shift_Ins", "Splice_Site", "Translation_Start_Site", "Nonsense_Mutation", "Nonstop_Mutation", "In_Frame_Del", "In_Frame_Ins", "Missense_Mutation"

ref.build

human reference genome version. Default 'hg19'. Optional: 'hg18' or 'hg38'.

MafList class

Description

S4 class for storing a list of Maf objects.

Slots

.Data

a list of Maf objects.

Constructor

MafList (...)

combine multiple Maf objects supplied in ... into a MafList object.

mathScore

Description

calculates MATH score of each tumor sample or based on Mutant-Allele Tumor Heterogeneity (MATH) approach.

Usage

mathScore(
  maf,
  patient.id = NULL,
  withinTumor = FALSE,
  min.vaf = 0,
  use.adjVAF = FALSE,
  segFile = NULL,
  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.
withinTumor

Calculate between-region heterogeneity within tumor. Default: FALSE.
min.vaf

Specify The minimum VAF to filter variants. Default: 0.
use.adjVAF

Use adjusted VAF in analysis when adjusted VAF or CCF is available. Default: FALSE.
segFile

The segment file.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' by 'Tumor_Sample_Label'.
...

Other options passed to subMaf

Value

A data.frame of MATH scores

References

Mroz, Edmund A. et al. Intra-Tumor Genetic Heterogeneity and Mortality in Head and Neck Cancer: Analysis of Data from The Cancer Genome Atlas. Ed. Andrew H. Beck. PLoS Medicine 12.2 (2015): e1001786.

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")
mathScore(maf)

mutCluster

Description

Cluster mutations based on variant allele frequencies (VAFs) or cancer cell fractions (CCFs).

Usage

mutCluster(
  maf,
  patient.id = NULL,
  use.ccf = FALSE,
  segFile = NULL,
  withinTumor = FALSE,
  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.
use.ccf

Cluster CCF. Default FALSE.
segFile

The segment file.
withinTumor

Cluster Tumor average CCF within tumors in each patients. Default FALSE.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' by 'Tumor_Sample_Label'.
...

Other options passed to subMaf

Value

clustering plots of vaf

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")
mutCluster(maf, patient.id = 'V402')

mutHeatmap

Description

plot binary or CCF heatmap of somatic mutations.

Usage

mutHeatmap(
  maf,
  patient.id = NULL,
  min.vaf = 0,
  min.ccf = 0,
  use.adjVAF = FALSE,
  use.ccf = FALSE,
  geneList = NULL,
  plot.geneList = FALSE,
  show.geneList = TRUE,
  mut.threshold = 50,
  sample.text.size = 9,
  legend.title.size = 10,
  gene.text.size = 9,
  sampleOrder = NULL,
  use.tumorSampleLabel = FALSE,
  classByTumor = FALSE,
  ...
)

Arguments

maf

Maf or MafList object generated by readMaf function.
patient.id

Select the specific patients. Default NULL, all patients are included.
min.vaf

The minimum value of VAF. Default 0. Option: on the scale of 0 to 1.
min.ccf

The minimum value of CCF. Default 0. Option: on the scale of 0 to 1.
use.adjVAF

Use adjusted VAF in analysis when adjusted VAF or CCF is available. Default FALSE.
use.ccf

Logical. If FALSE (Default: FALSE), print a binary heatmap of mutations. Otherwise, print a cancer cell frequency (CCF) heatmap.
geneList

List of genes to restrict the analysis. Default NULL.
plot.geneList

Logical (Default: FALSE). If TRUE, plot heatmap with genes on geneList when geneList is not NULL.
show.geneList

Show the names of gene on the geneList. Default TRUE.
mut.threshold

show.gene and show.geneList will be FALSE when patient have more mutations than threshold. Default 150.
sample.text.size

Size of sample name.Default 9.
legend.title.size

Size of legend title.Default 10.
gene.text.size

Size of gene text. Default 9.
sampleOrder

A named list which contains the sample order used in plotting the heatmap. Default NULL.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' by 'Tumor_Sample_Label'.
classByTumor

Logical Default: FALSE. Classify mutations based on "Tumor_ID".
...

Other options passed to subMaf

Value

heatmap of somatic mutations

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")
mutHeatmap(maf)

mutTrunkBranch

Description

Summarize and conduct paired Fisher test of mutations of trunk/branches in a phylogenetic tree.

Usage

mutTrunkBranch(
  phyloTree,
  patient.id = NULL,
  CT = FALSE,
  pvalue = 0.05,
  plot = TRUE
)

Arguments

phyloTree

phyloTree or phyloTreeList object generated by getPhyloTree function.
patient.id

Select the specific patients. Default NULL, all patients are included
CT

Distinction between C>T at CpG and C>T at other sites. (Default: FALSE).
pvalue

Confidence level of the interval for Fisher test. Default 0.05.
plot

Logical. (Default: TRUE).

Value

a list of box plots based on mutational categories

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")

## Load a reference genome.
library(BSgenome.Hsapiens.UCSC.hg19)

phyloTree <- getPhyloTree(maf, patient.id = 'V402')
mutTrunkBranch(phyloTree, plot = TRUE)

phyloTree class

Description

S4 class for storing informations about phylogenetic tree.

Slots

patientID

patient ID.

tree

a object of class "phylo".

bootstrap.value

a numeric vector of bootstrap values.

method

approach to construct a phylogenetic tree.

binary.matrix

a presense/absent binary matrix of mutations.

ccf.matrix

a ccf matrix of mutations.

mut.branches

a data.frame of mutations per trunk/branch.

branch.type

a data.frame of trunk/branch types based on shared pattern.

ref.build

human reference genome version. Default: 'hg19'. Optional: 'hg18' or 'hg38'.

tsb.label

store relationship between Tumor_Sample_Barcode and Tumor_Sample_Label if Tumor_Sample_Label is provided in clinical data.

phyloTreeList class

Description

S4 class for storing a list of phyloTree objects.

Slots

.Data

a list of phyloTree objects.

Constructor

phyloTreeList (...)

combine multiple phyloTree objects supplied in ... into a phyloTreeList object.

plotCNA

Description

plotCNA

Usage

plotCNA(
  seg,
  patient.id = NULL,
  sampleOrder = NULL,
  chrSilent = NULL,
  refBuild = "hg19",
  sample.text.size = 11,
  chrom.text.size = 3,
  legend.text.size = 9,
  legend.title.size = 11,
  annot.text.size = 3,
  sample.bar.height = 0.5,
  chrom.bar.height = 0.5,
  showRownames = TRUE,
  removeEmptyChr = TRUE,
  showCytoband = FALSE,
  showGene = FALSE,
  use.tumorSampleLabel = FALSE
)

Arguments

seg

Object generated by readSegment function.
patient.id

Select the specific patients. Default NULL, all patients are included.
sampleOrder

A named list which contains the sample order used in plotting the final profile. Default NULL.
chrSilent

Chromosomes excluded in the analysis. e.g, 1, 2, 3. Default NULL.
refBuild

Human reference genome versions of hg18, hg19 or hg38 by UCSC. Default "hg19".
sample.text.size

Fontsize of sample name. Default 11.
chrom.text.size

Fontsize of chromosome text. Default 3.
legend.text.size

Fontsize of legend text. Default 9.
legend.title.size

Fontsize of legend title. Default 11.
annot.text.size

Fontsize of cytoband or gene symbols. Default 3.
sample.bar.height

Bar height of each sample. Default 0.5.
chrom.bar.height

Bar height of each chromosome. Default 0.5.
showRownames

Logical (Default: TRUE). Show sample names of rows.
removeEmptyChr

Remove empty chromosomes that do not exist in all samples. Default TRUE.
showCytoband

Logical (Default: FALSE). Show cytobands on the plot. Only when the seg object is created with GISTIC results, this parameter can be TRUE.
showGene

Logical (Default: FALSE). Show gene symbols on the plot. Only when the seg object is created with txdb, this parameter can be TRUE.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' with 'Tumor_Sample_Label'.

Value

a heatmap plot of CNA profile

Examples

segFile <- system.file("extdata", "CRC_HZ.seg.txt", package = "MesKit")
seg <- readSegment(segFile = segFile)
plotCNA(seg)

## showCytoband
gisticAmpGenesFile <- system.file("extdata", "COREAD_amp_genes.conf_99.txt", package = "MesKit")
gisticDelGenesFile <- system.file("extdata", "COREAD_del_genes.conf_99.txt", package = "MesKit")
gisticAllLesionsFile <- system.file("extdata", "COREAD_all_lesions.conf_99.txt", package = "MesKit")
seg <- readSegment(segFile = segFile,
                   gisticAmpGenesFile = gisticAmpGenesFile,
                    gisticDelGenesFile = gisticDelGenesFile,
                   gisticAllLesionsFile = gisticAllLesionsFile)
plotCNA(seg, showCytoband = TRUE)

plotMutProfile

Description

plotMutProfile

Usage

plotMutProfile(
  maf,
  patient.id = NULL,
  class = "SP",
  classByTumor = FALSE,
  topGenesCount = 10,
  geneList = NULL,
  sample.text.size = 11,
  gene.text.size = 11,
  legend.text.size = 11,
  legend.title.size = 11,
  bgCol = "#f0f0f0",
  patientsCol = NULL,
  removeEmptyCols = TRUE,
  removeEmptyRows = TRUE,
  showColnames = TRUE,
  sampleOrder = NULL,
  use.tumorSampleLabel = FALSE,
  ...
)

Arguments

maf

Maf or MafList object generated by readMaf function.
patient.id

Select or reorder the patients. Default NULL, all patients are included. Classify SSNVs/Indels into Shared/P-shared/Private, Clonal/Subclonl or Shared-Clonal/P-shared-Clonal/Private-Clonal/Shared-Subclonal/P-shared-SubClonal/Private-SubClonal
class

The class which would be represented. Default "SP" (Shared pattern: Public/Shared/Private), other options: "CS" (Clonal status: Clonal/Subclonl) and "SPCS".
classByTumor

Logical (Default: FALSE). Define shared pattern of mutations based on tumor types (TRUE) or samples (FALSE)
topGenesCount

The number of genes print, Default 10.
geneList

A list of genes to restrict the analysis. Default NULL.
sample.text.size

Fontsize of sample name. Default 11.
gene.text.size

Fontsize of gene text. Default 11.
legend.text.size

Fontsize of legend text. Default 11.
legend.title.size

Fontsize of legend title. Default 11.
bgCol

Background grid color. Default "#f0f0f0".
patientsCol

A list containing customized colors for distinct patients. Default NULL.
removeEmptyCols

Logical (Default: TRUE). Whether remove the samples without alterations.
removeEmptyRows

Logical (Default: TRUE). Whether remove the genes without alterations.
showColnames

Logical (Default: TRUE). Show sample names of columns.
sampleOrder

A named list which contains the sample order used in plotting the final profile. Default NULL.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' with 'Tumor_Sample_Label'.
...

Other options passed to subMaf

Value

Mutational profile

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")
plotMutProfile(maf, class = "SP")

plotMutSigProfile

Description

plotMutSigProfile

Usage

plotMutSigProfile(
  sig_input,
  patient.id = NULL,
  mode = NULL,
  contribution.type = "relative",
  use.tumorSampleLabel = FALSE
)

Arguments

sig_input

Result generated by function fitSignatures or triMatrix.
patient.id

Select the specific patients. Default NULL, all patients are included.
mode

Type of mutation spectrum. Default NULL. Options:'Original','Reconstructed' or 'Difference'
contribution.type

Type of Signature contribution. Default 'realative'. Options:'realative' or 'absolute'.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' by 'Tumor_Sample_Label'.

Value

Mutational signature profile of patients

Examples

## input from fitSignatures
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")
phyloTree <- getPhyloTree(maf, patient.id = 'V402')

## Load a reference genome.
library(BSgenome.Hsapiens.UCSC.hg19)

tri_matrix <- triMatrix(phyloTree)
fit_out <- fitSignatures(tri_matrix)
plotMutSigProfile(fit_out)
## input from treeMatrix
plotMutSigProfile(tri_matrix)

plotPhyloTree

Description

plotPhyloTree

Usage

plotPhyloTree(
  phyloTree,
  patient.id = NULL,
  branchCol = "mutType",
  show.bootstrap = TRUE,
  min.ratio = 1/20,
  signaturesRef = "cosmic_v2",
  min.mut.count = 15,
  use.tumorSampleLabel = FALSE,
  show.scale.bar = FALSE,
  scale.bar.x = NULL,
  scale.bar.y = NULL
)

Arguments

phyloTree

phyloTree or phyloTreeList object generated by getPhyloTree function.
patient.id

Select the specific patients. Default NULL, all patients are included.
branchCol

Specify the colors of branches Default 'mutType'. Other options: "mutSig" for coloring branches by branch mutation signature;
show.bootstrap

Logical (Default: TRUE). Whether to add bootstrap value on internal nodes.
min.ratio

Double. Default 1/20. If min.ratio is not NULL, all edge length of a phylogenetic tree should be greater than min.ratio*the longest edge length. If not, the edge length will be reset as min.ratio*longest edge length.
signaturesRef

Signature reference,Users can upload their own reference. Default "cosmic_v2". Option:"exome_cosmic_v3","nature2013".
min.mut.count

The threshold for the variants in a branch. Default 15.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' with 'Tumor_Sample_Label'.
show.scale.bar

Logical (Default: FALSE). Whether to show scale bar.This function adds a horizontal bar giving the scale of the branch lengths to a plot on the current graphical device.
scale.bar.x

The x location of scale bar.
scale.bar.y

The y location of scale bar.

Value

return a list of phylotree graph .

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")

phyloTree <- getPhyloTree(maf, patient.id = 'V402')
plotPhyloTree(phyloTree)

readMaf

Description

Read tab delimited MAF (can be plain text or *.gz compressed) file along with sample information file.

Usage

readMaf(
  mafFile,
  clinicalFile,
  ccfFile = NULL,
  adjusted.VAF = FALSE,
  nonSyn.vc = NULL,
  use.indel.ccf = FALSE,
  ccf.conf.level = 0.95,
  refBuild = "hg19"
)

Arguments

mafFile

A tab delimited MAF file (plain text or *.gz compressed). Required.
clinicalFile

A clinical data file includes Tumor_Sample_Barcode, Tumor_ID, Patient_ID. Tumor_Sample_Label is optional. Default NULL.
ccfFile

A CCF file of somatic mutations. Default NULL.
adjusted.VAF

Whether adjusted VAF is included in mafFile. Default FALSE.
nonSyn.vc

List of Variant classifications which are considered as non-silent. Default NULL, use Variant Classifications with "Frame_Shift_Del","Frame_Shift_Ins","Splice_Site","Translation_Start_Site","Nonsense_Mutation","Nonstop_Mutation","In_Frame_Del","In_Frame_Ins","Missense_Mutation"
use.indel.ccf

Whether include indels in ccfFile. Default FALSE.
ccf.conf.level

The confidence level of CCF to identify clonal or subclonal. Only works when "CCF_std" or "CCF_CI_high" is provided in ccfFile. Default 0.95.
refBuild

Human reference genome version. Default 'hg19'. Optional: 'hg18' or 'hg38'.

Value

an object of Maf or MafList.

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, refBuild="hg19")
maf <- readMaf(mafFile=maf.File, clinicalFile = clin.File, ccfFile=ccf.File, refBuild="hg19")

readSegment

Description

readSegment

Usage

readSegment(
  segFile,
  gisticAmpGenesFile = NULL,
  gisticDelGenesFile = NULL,
  gisticAllLesionsFile = NULL,
  gistic.qval = 0.25,
  min.seg.size = 500,
  txdb = NULL,
  min.overlap.len = 50,
  verbose = TRUE,
  ...
)

Arguments

segFile

The segment file.
gisticAmpGenesFile

Amplification Genes file generated by GISTIC. Default NULL.
gisticDelGenesFile

Deletion Genes file generated by GISTIC. Default NULL.
gisticAllLesionsFile

Information of all lesions generated by GISTIC. Default NULL.
gistic.qval

The threshold of gistic Q value. Default 0.25.
min.seg.size

The smallest size of segments. Default 500.
txdb

A TxDb object. i.e., TxDb.Hsapiens.UCSC.hg19.knownGene. Default NULL.
min.overlap.len

The minimum insertion size of segment and gene. Default 50.
verbose

Whether to display details in the console. Default TRUE.
...

... Other options passed to cna2gene.

Value

a list of segmentation data frame

Examples

segFile <- system.file("extdata", "CRC_HZ.seg.txt", package = "MesKit")
gisticAmpGenesFile <- system.file("extdata", "COREAD_amp_genes.conf_99.txt", package = "MesKit")
gisticDelGenesFile <- system.file("extdata", "COREAD_del_genes.conf_99.txt", package = "MesKit")
gisticAllLesionsFile <- system.file("extdata", "COREAD_all_lesions.conf_99.txt", package = "MesKit")
seg <- readSegment(segFile = segFile,
                   gisticAmpGenesFile = gisticAmpGenesFile,
                   gisticDelGenesFile = gisticDelGenesFile,
                   gisticAllLesionsFile = gisticAllLesionsFile)

Run the default MesKit app for analysis locally

Description

runMesKit run MesKit locally

Usage

runMesKit()

Value

a shiny app window

Author(s)

Mengni Liu

Examples

runMesKit()

Subset Maf object

Description

Subset Maf object

Usage

subMaf(
  maf,
  mafObj = FALSE,
  patient.id = NULL,
  geneList = NULL,
  chrSilent = NULL,
  mutType = "All",
  use.indel = TRUE,
  min.vaf = 0,
  max.vaf = 1,
  min.average.vaf = 0,
  min.ccf = 0,
  min.ref.depth = 0,
  min.alt.depth = 0,
  min.total.depth = 0,
  clonalStatus = NULL,
  use.adjVAF = FALSE,
  use.tumorSampleLabel = FALSE
)

Arguments

maf

Maf or MafList object generated by readMaf function.
mafObj

return Maf class. (Default: FALSE).
patient.id

Select the specific patients. Default NULL, all patients are included.
geneList

A list of genes to restrict the analysis. Default NULL.
chrSilent

Chromosomes excluded in the analysis. e.g, 1, 2, X, Y. Default NULL.
mutType

Select Proper variant classification you need. Default "All". Option: "nonSyn".
use.indel

Logical value. Whether to use INDELs besides somatic SNVs. (Default: TRUE).
min.vaf

The minimum VAF for filtering variants. Default 0.
max.vaf

The maximum VAF for filtering variants. Default 1.
min.average.vaf

The minimum tumor average VAF for filtering variants. Default 0.
min.ccf

The minimum CCF for filtering variants. Default NULL.
min.ref.depth

The minimum reference allele depth for filtering variants. Default 0.
min.alt.depth

The minimum alteratation allele depth for filtering variants. Default 0.
min.total.depth

The minimum total allele depth for filtering variants. Default 0.
clonalStatus

Subset by clonal status. Default NULL. Option: "Clonal","Subclonal".
use.adjVAF

Use adjusted VAF in analysis when adjusted VAF or CCF is available. Default FALSE.
use.tumorSampleLabel

Logical (Default: FALSE). Rename the 'Tumor_Sample_Barcode' by 'Tumor_Sample_Label'.

Value

Maf object or Maf data.

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")
maf_data <- subMaf(maf)

testNeutral

Description

Evaluate whether a tumor follows neutral evolution or under strong selection during the growth based on variant frequency distribution (VAF) of subclonal mutations. The subclonal mutant allele frequencies of a follow a simple power-law distribution predicted by neutral growth.

Usage

testNeutral(
  maf,
  patient.id = NULL,
  withinTumor = FALSE,
  min.total.depth = 2,
  min.vaf = 0.1,
  max.vaf = 0.3,
  R2.threshold = 0.98,
  min.mut.count = 20,
  plot = TRUE,
  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.
withinTumor

Test neutral within tumros in each patients. Default FALSE.
min.total.depth

The minimun total depth of coverage. Defalut 2
min.vaf

The minimum value of adjusted VAF value. Default 0.1
max.vaf

The maximum value of adjusted VAF value. Default 0.3
R2.threshold

The threshod of R2 to decide whether a tumor follows neutral evolution. Default 0.98
min.mut.count

The minimun number of subclonal mutations used to fit model. Default 20
plot

Logical, whether to print model fitting plot of each sample. Default TRUE.
use.tumorSampleLabel

Let Tumor_Sample_Barcode replace Tumor_Sample_Label if Tumor Label is provided in clinical data. Default FALSE.
...

Other options passed to subMaf

Value

the neutrality metrics and model fitting plots

References

Williams, M., Werner, B. et al. Identification of neutral tumor evolution across cancer types. Nat Genet 48, 238-244 (2016)

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")
testNeutral(maf)

triMatrix

Description

Calculate the frequency of 96 trinucleotide mutation based on somatic SNVs.

Usage

triMatrix(phyloTree, patient.id = NULL, level = 2)

Arguments

phyloTree

phyloTree or phyloTreeList object generated by getPhyloTree function.
patient.id

Select the specific patients. Default NULL, all patients are included
level

Calculate the frequency of 96 trinucleotide mutatio on different levels. 1: patient-level, 2: tumor-level, 3: sample-level, 4: branch-level, 5: shared pattern (public/shared/private) of each tumor. 6: trunk/branch-level. Default 2.

Value

The frequency of 96 trinucleotide mutation.

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")

## Load a reference genome.
library(BSgenome.Hsapiens.UCSC.hg19)

phyloTree <- getPhyloTree(maf, patient.id = 'V402')
triMatrix(phyloTree)