Package 'maftools'

Title: Summarize, Analyze and Visualize MAF Files
Description: Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort.
Authors: Anand Mayakonda [aut, cre]
Maintainer: Anand Mayakonda <[email protected]>
License: MIT + file LICENSE
Version: 2.23.0
Built: 2024-11-01 06:14:58 UTC
Source: https://github.com/bioc/maftools

Help Index

Converts annovar annotations into MAF.

Description

Converts variant annotations from Annovar into a basic MAF.

Usage

annovarToMaf(
  annovar,
  Center = NULL,
  refBuild = "hg19",
  tsbCol = NULL,
  table = "refGene",
  ens2hugo = TRUE,
  basename = NULL,
  sep = "\t",
  MAFobj = FALSE,
  sampleAnno = NULL
)

Arguments

annovar

input annovar annotation file. Can be vector of multiple files.
Center

Center field in MAF file will be filled with this value. Default NA.
refBuild

NCBI_Build field in MAF file will be filled with this value. Default hg19.
tsbCol

column name containing Tumor_Sample_Barcode or sample names in input file.
table

reference table used for gene-based annotations. Can be 'ensGene' or 'refGene'. Default 'refGene'
ens2hugo

If 'table' is 'ensGene', setting this argument to 'TRUE' converts all ensemble IDs to hugo symbols.
basename

If provided writes resulting MAF file to an output file.
sep

field seperator for input file. Default tab seperated.
MAFobj

If TRUE, returns results as an MAF object.
sampleAnno

annotations associated with each sample/Tumor_Sample_Barcode in input annovar file. If provided it will be included in MAF object. Could be a text file or a data.frame. Ideally annotation would contain clinical data, survival information and other necessary features associated with samples. Default NULL.

Details

Annovar is one of the most widely used Variant Annotation tools in Genomics. Annovar output is generally in a tabular format with various annotation columns. This function converts such annovar output files into MAF. This function requires that annovar was run with gene based annotation as a first operation, before including any filter or region based annotations. Please be aware that this function performs no transcript prioritization.

e.g, table_annovar.pl example/ex1.avinput humandb/ -buildver hg19 -out myanno -remove -protocol (refGene),cytoBand,dbnsfp30a -operation (g),r,f -nastring NA

This function mainly uses gene based annotations for processing, rest of the annotation columns from input file will be attached to the end of the resulting MAF.

Value

MAF table.

References

Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38, e164 (2010).

Examples

var.annovar <- system.file("extdata", "variants.hg19_multianno.txt", package = "maftools")
var.annovar.maf <- annovarToMaf(annovar = var.annovar, Center = 'CSI-NUS', refBuild = 'hg19',
tsbCol = 'Tumor_Sample_Barcode', table = 'ensGene')

extract nucleotide counts for targeted variants from the BAM file.

Description

Given a BAM file and target loci, 'bamreadcounts' fetches redcounts for A, T, G, C, Ins, and Del. Function name is an homage to https://github.com/genome/bam-readcount

Usage

bamreadcounts(
  bam = NULL,
  loci = NULL,
  zerobased = FALSE,
  mapq = 10,
  sam_flag = 1024,
  op = NULL,
  fa = NULL,
  nthreads = 4
)

Arguments

bam

Input bam file(s). Required.
loci

Loci file. Can be a tsv file or a data.frame. First two columns should contain chromosome and position (by default assumes coordinates are 1-based)
zerobased

are coordinates zero-based. Default FALSE.
mapq

Map quality. Default 10
sam_flag

SAM FLAG to filter reads. Default 1024
op

Output file basename. Default parses from BAM file
fa

Indexed fasta file. If provided, extracts and adds reference base to the output tsv.
nthreads

Number of threads to use. Each BAM file will be launched on a separate thread. Works only on Unix and macOS.

Genotype known cancer hotspots from the tumor BAM file

Description

'cancerhotspots' allows rapid genotyping of known somatic variants from the tumor BAM files. This facilitates to get a quick overlook of known somatic hot-spots in a matter of minutes, without spending hours on variant calling and annotation. In simple words, it fetches nucleotide frequencies of known somatic hotspots and prioritizes them based on allele frequency. Output includes a browsable/sharable HTML report of candidate variants. Known cancerhotspots for both GRCh37 and GRCh38 assemblies (3180 variants) are included. This should be sufficient and cover most of the known driver genes/events. See Reference for details.

Usage

cancerhotspots(
  bam = NULL,
  refbuild = "GRCh37",
  mapq = 10,
  sam_flag = 1024,
  vaf = 0.05,
  t_depth = 30,
  t_alt_count = 8,
  op = NULL,
  fa = NULL,
  browse = FALSE
)

Arguments

bam

Input bam file. Required.
refbuild

Default "GRCh37". Can be "GRCh37", "GRCh38", "hg19", "hg38"
mapq

Map quality. Default 10
sam_flag

SAM FLAG to filter reads. Default 1024
vaf

VAF threshold. Default 0.05 [Variant filter]
t_depth

Depth of coverage threshold. Default 30 [Variant filter]
t_alt_count

Min. number of reads supporting tumor allele . Default 8 [Variant filter]
op

Output file basename. Default parses from BAM file
fa

Indexed fasta file. If provided, extracts and adds reference base to the output tsv.
browse

If TRUE opens the html file in browser

References

Chang MT, Asthana S, Gao SP, et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat Biotechnol. 2016;34(2):155-163. doi:10.1038/nbt.3391

See Also

cancerhotspotsAggr

Aggregate cancerhotspots reports

Description

Takes tsv files generated by cancerhotspots and aggregates them into an MAF for downstream analysis

Usage

cancerhotspotsAggr(
  tsvs = NULL,
  minVaf = 0.02,
  minDepth = 15,
  sampleNames = NULL,
  maf = TRUE,
  ...
)

Arguments

tsvs

TSV files generated by cancerhotspots
minVaf

Min. VAF threshold. Default 0.02
minDepth

Min. depth of coverage. Default 15
sampleNames

samples for each tsv file. Default NULL. Parses from file names.
maf

Return as an MAF object. Default TRUE.
...

Additional argumnets passed to read.maf if 'maf' is TRUE.

Value

MAF object

See Also

cancerhotspots

Performs mutational enrichment analysis for a given clinical feature.

Description

Performs pairwise and groupwise fisher exact tests to find differentially enriched genes for every factor within a clinical feature.

Usage

clinicalEnrichment(
  maf,
  clinicalFeature = NULL,
  annotationDat = NULL,
  minMut = 5,
  useCNV = TRUE,
  pathways = FALSE
)

Arguments

maf

MAF object
clinicalFeature

columns names from 'clinical.data' slot of MAF to be analysed for.
annotationDat

If MAF file was read without clinical data, provide a custom data.frame or a tsv file with a column containing Tumor_Sample_Barcodes along with clinical features. Default NULL.
minMut

Consider only genes with minimum this number of samples mutated. Default 5.
useCNV

whether to include copy number events if available. Default TRUE. Not applicable when 'pathways = TRUE'
pathways

Summarize genes by pathways before comparing. Default 'FALSE'

Details

Performs fishers test on 2x2 contingency table for WT/Mutants in group of interest vs rest of the sample. Odds Ratio indicate the odds of observing mutant in the group of interest compared to wild-type

Value

result list containing p-values

See Also

plotEnrichmentResults

Examples

## Not run: 
laml.maf = system.file('extdata', 'tcga_laml.maf.gz', package = 'maftools')
laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools')
laml = read.maf(maf = laml.maf, clinicalData = laml.clin)
clinicalEnrichment(laml, 'FAB_classification')

## End(Not run)

Draw two barplots side by side for cohort comparision.

Description

Draw two barplots side by side for cohort comparision.

Usage

coBarplot(
  m1,
  m2,
  genes = NULL,
  orderBy = NULL,
  m1Name = NULL,
  m2Name = NULL,
  colors = NULL,
  normalize = TRUE,
  yLims = NULL,
  borderCol = "gray",
  titleSize = 1,
  geneSize = 0.8,
  showPct = TRUE,
  pctSize = 0.7,
  axisSize = 0.8,
  showLegend = TRUE,
  legendTxtSize = 1,
  geneMar = 4
)

Arguments

m1

first MAF object
m2

second MAF object
genes

genes to be drawn. Default takes top 5 mutated genes.
orderBy

Order genes by mutation rate in 'm1' or 'm2'. Default 'NULL', keeps the same order of 'genes'
m1Name

optional name for first cohort
m2Name

optional name for second cohort
colors

named vector of colors for each Variant_Classification.
normalize

Default TRUE.
yLims

Default NULL. Auto estimates. Maximum values for 'm1' and 'm2' respectively
borderCol

Default gray
titleSize

Default 1
geneSize

Default 0.8
showPct

Default TRUE
pctSize

Default 0.7
axisSize

Default 0.8
showLegend

Default TRUE.
legendTxtSize

Default 0.8
geneMar

Default 4

Details

Draws two barplots side by side to display difference between two cohorts.

Value

Returns nothing. Just draws plot.

Examples

#' ##Primary and Relapse APL
primary.apl <- system.file("extdata", "APL_primary.maf.gz", package = "maftools")
relapse.apl <- system.file("extdata", "APL_relapse.maf.gz", package = "maftools")
##Read mafs
primary.apl <- read.maf(maf = primary.apl)
relapse.apl <- read.maf(maf = relapse.apl)
##Plot
coBarplot(m1 = primary.apl, m2 = relapse.apl, m1Name = 'Primary APL', m2Name = 'Relapse APL')
dev.off()

Co-plot version of gisticChromPlot()

Description

Use two GISTIC object or/and two MAF objects to view a vertical arranged version of Gistic Chromosome plot results on the Amp or Del G-scores.

Usage

coGisticChromPlot(
  gistic1 = NULL,
  gistic2 = NULL,
  g1Name = "",
  g2Name = "",
  type = "Amp",
  markBands = TRUE,
  labelGenes = TRUE,
  gLims = NULL,
  maf1 = NULL,
  maf2 = NULL,
  mutGenes = NULL,
  mutGenes1 = NULL,
  mutGenes2 = NULL,
  fdrCutOff = 0.05,
  symmetric = TRUE,
  color = NULL,
  ref.build = "hg19",
  cytobandOffset = "auto",
  txtSize = 0.8,
  cytobandTxtSize = 1,
  mutGenesTxtSize = 0.6,
  rugTickSize = 0.1
)

Arguments

gistic1

first GISTIC object
gistic2

second GISTIC object
g1Name

the title of the left side
g2Name

the title of the right side
type

default 'Amp', c('Amp',"Del"), choose one to plot, only focal events are shown, 'Amp' only shows the Amplification events, and 'Del' only shows the Deletion events.
markBands

default TRUE, integer of length 1 or 2 or TRUE, mark cytoband names of the outer side of the plot
labelGenes

if you want to label some genes you are interested along the chromosome, set it to TRUE
gLims

Controls the G-score's axis limits. Default NULL.
maf1, maf2

if labelGenes==TRUE, you need to provide MAF object, the genes mutation info collected from the maf1 is shown on the left side, while maf2 on the right side. the genes selected are controled by the mutGenes or mutGenes1 or mutGenes1 parameter, see following.
mutGenes, mutGenes1, mutGenes2

default NULL, could be NULL, number, or character vector of gene symbols which match the corresponding MAF object's Hugo_Symbol column values. mutGenes controls both sides of the annotation, mutGenes1 controls only left side and corresponding data is extracted from to maf1, and mutGenes2 controls only right side annotation and corresponding to maf2. If 'NULL', extract the top 50 mutated genes from maf1 and maf2 seperatedly then annotate them on the left side (maf1 genes) and right side (maf2 genes). if integer, say N, only top N genes will be extracted seperately from maf1 and maf2. These two condition leads to different genes annotated on both sides. If character vector, then the genes have mutated in maf1 and maf2 will be annotated on both side of the figure which mean the two sides have the same list of genes. if mutGenes is not NULL and both mutGenes1 and mutGenes1 are NULL, then the auto set mutGenes1 = mutGenes2 = mutGenes.
fdrCutOff

default 0.05,only items with FDR < fdrCutOff will be colored as Amp or Del ( colored 'Red' or 'Blue'), others will be seen as non-significant events (colored gray)
symmetric

default TRUE, If False, when the gistic1 and gistic2 have different max values of G-scores, the Chrom (0 point of x axis) will not be in the center of the whole plot, if you set symmetric==TRUE, then the one with smaller max(G-score) will be stretched larger to make the 0 of the x axis in the middle which eventually make the plot more symmetric.
color

NULL or a named vector. the color of the G-score lines, default NULL which will set the color c(Amp = "red", Del = "blue", neutral = 'gray70')
ref.build

default "hg19", c('hg18','hg19','hg38') supported at current.
cytobandOffset

default 'auto', the width of the chromosome rects (Y axis at 0 point of X axis). by default will be 0.015 of the width of the whole x axis length.
txtSize

the zoom value of most of the texts
cytobandTxtSize

textsize of the cytoband annotation
mutGenesTxtSize

textsize of the mutGenes annotation
rugTickSize

the rug line width of the cytoband annotation

Author(s)

bio_sun - https://github.com/biosunsci

Examples

## Not run: 
gistic_res_folder = system.file("extdata",package = "maftools")
laml.gistic = readGistic(gistic_res_folder)
laml.gistic2 = readGistic(gistic_res_folder)


laml.maf = system.file('extdata', 'tcga_laml.maf.gz', package = 'maftools')
laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools')
laml = read.maf(maf = laml.maf, clinicalData = laml.clin)
laml2 = laml

# --- plot ---
gisticChromPlot2v(gistic1 = laml.gistic, gistic2 = laml.gistic2, type='Del',
                   symmetric = TRUE, g1Name = 'TCGA1',
                   g2Name = 'TCGA2', maf1 = laml, maf2 = laml2, mutGenes = 30)

## End(Not run)

Compares identified denovo mutational signatures to known COSMIC signatures

Description

Takes results from extractSignatures and compares them known COSMIC signatures. Two COSMIC databases are used for comparisons - "legacy" which includes 30 signaures, and "SBS" - which includes updated/refined 65 signatures

Usage

compareSignatures(nmfRes, sig_db = "SBS_v34", verbose = TRUE)

Arguments

nmfRes

results from extractSignatures
sig_db

can be legacy, SBS, SBS_v34. Default SBS_v34
verbose

Default TRUE

Details

SBS signature database was obtained from https://www.synapse.org/#!Synapse:syn11738319.7

Value

list containing cosine smilarities, aetiologies if available, and best match.

See Also

trinucleotideMatrix extractSignatures plotSignatures

Draw two oncoplots side by side for cohort comparision.

Description

Draw two oncoplots side by side for cohort comparision.

Usage

coOncoplot(
  m1,
  m2,
  genes = NULL,
  m1Name = NULL,
  m2Name = NULL,
  clinicalFeatures1 = NULL,
  clinicalFeatures2 = NULL,
  annotationColor1 = NULL,
  annotationColor2 = NULL,
  annotationFontSize = 1.2,
  sortByM1 = FALSE,
  sortByM2 = FALSE,
  sortByAnnotation1 = FALSE,
  annotationOrder1 = NULL,
  sortByAnnotation2 = FALSE,
  annotationOrder2 = NULL,
  sampleOrder1 = NULL,
  sampleOrder2 = NULL,
  additionalFeature1 = NULL,
  additionalFeaturePch1 = 20,
  additionalFeatureCol1 = "white",
  additionalFeatureCex1 = 0.9,
  additionalFeature2 = NULL,
  additionalFeaturePch2 = 20,
  additionalFeatureCol2 = "white",
  additionalFeatureCex2 = 0.9,
  sepwd_genes1 = 0.5,
  sepwd_samples1 = 0.5,
  sepwd_genes2 = 0.5,
  sepwd_samples2 = 0.5,
  colors = NULL,
  removeNonMutated = TRUE,
  anno_height = 2,
  legend_height = 4,
  geneNamefont = 0.8,
  showSampleNames = FALSE,
  SampleNamefont = 0.5,
  barcode_mar = 1,
  outer_mar = 3,
  gene_mar = 1,
  legendFontSize = 1.2,
  titleFontSize = 1.5,
  keepGeneOrder = FALSE,
  bgCol = "#ecf0f1",
  borderCol = "white"
)

Arguments

m1

first MAF object
m2

second MAF object
genes

draw these genes. Default plots top 5 mutated genes from two cohorts.
m1Name

optional name for first cohort
m2Name

optional name for second cohort
clinicalFeatures1

columns names from 'clinical.data' slot of m1 MAF to be drawn in the plot. Dafault NULL.
clinicalFeatures2

columns names from 'clinical.data' slot of m2 MAF to be drawn in the plot. Dafault NULL.
annotationColor1

list of colors to use for 'clinicalFeatures1' Default NULL.
annotationColor2

list of colors to use for 'clinicalFeatures2' Default NULL.
annotationFontSize

font size for annotations Default 1.2
sortByM1

sort by mutation frequency in 'm1'
sortByM2

sort by mutation frequency in 'm2'
sortByAnnotation1

logical sort oncomatrix (samples) by provided 'clinicalFeatures1'. Sorts based on first 'clinicalFeatures1'. Defaults to FALSE. column-sort
annotationOrder1

Manually specify order for annotations for 'clinicalFeatures1'. Works only for first value. Default NULL.
sortByAnnotation2

same as above but for m2
annotationOrder2

Manually specify order for annotations for 'clinicalFeatures2'. Works only for first value. Default NULL.
sampleOrder1

Manually specify sample names in m1 for oncolplot ordering. Default NULL.
sampleOrder2

Manually specify sample names in m2 for oncolplot ordering. Default NULL.
additionalFeature1

a vector of length two indicating column name in the MAF and the factor level to be highlighted.
additionalFeaturePch1

Default 20
additionalFeatureCol1

Default "white"
additionalFeatureCex1

Default 0.9
additionalFeature2

a vector of length two indicating column name in the MAF and the factor level to be highlighted.
additionalFeaturePch2

Default 20
additionalFeatureCol2

Default "white"
additionalFeatureCex2

Default 0.9
sepwd_genes1

Default 0.5
sepwd_samples1

Default 0.5
sepwd_genes2

Default 0.5
sepwd_samples2

Default 0.5
colors

named vector of colors for each Variant_Classification.
removeNonMutated

Logical. If TRUE removes samples with no mutations in the oncoplot for better visualization. Default TRUE.
anno_height

Height of clinical margin. Default 2
legend_height

Height of legend margin. Default 4
geneNamefont

font size for gene names. Default 1
showSampleNames

whether to show sample names. Defult FALSE.
SampleNamefont

font size for sample names. Default 0.5
barcode_mar

Margin width for sample names. Default 1
outer_mar

Margin width for outer. Default 3
gene_mar

Margin width for gene names. Default 1
legendFontSize

font size for legend. Default 1.2
titleFontSize

font size for title. Default 1.5
keepGeneOrder

force the resulting plot to use the order of the genes as specified. Default FALSE
bgCol

Background grid color for wild-type (not-mutated) samples. Default gray - "#CCCCCC"
borderCol

border grid color for wild-type (not-mutated) samples. Default 'white'

Details

Draws two oncoplots side by side to display difference between two cohorts.

Value

Invisibly returns a list of sample names in their order of occurrences in M1 and M2 respectively.

Examples

#' ##Primary and Relapse APL
primary.apl <- system.file("extdata", "APL_primary.maf.gz", package = "maftools")
relapse.apl <- system.file("extdata", "APL_relapse.maf.gz", package = "maftools")
##Read mafs
primary.apl <- read.maf(maf = primary.apl)
relapse.apl <- read.maf(maf = relapse.apl)
##Plot
coOncoplot(m1 = primary.apl, m2 = relapse.apl, m1Name = 'Primary APL', m2Name = 'Relapse APL')
dev.off()

Drug-Gene Interactions

Description

Checks for drug-gene interactions and druggable categories

Usage

drugInteractions(
  maf,
  top = 20,
  genes = NULL,
  plotType = "bar",
  drugs = FALSE,
  fontSize = 0.8
)

Arguments

maf

an MAF object generated by read.maf
top

Top number genes to check for. Default 20
genes

Manually specify gene list
plotType

Can be bar, pie Default bar plot.
drugs

Check for known/reported drugs. Default FALSE
fontSize

Default 0.8

Details

This function takes a list of genes and checks for known/reported drug-gene interactions or Druggable categories. All gene-drug interactions and drug claims are compiled from Drug Gene Interaction Databse. See reference for details and cite it if you use this function.

References

Griffith, M., Griffith, O. L., Coffman, A. C., Weible, J. V., McMichael, J. F., Spies, N. C., et. al,. 2013. DGIdb - Mining the druggable genome. Nature Methods.

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
drugInteractions(maf = laml)

Estimate number of signatures based on cophenetic correlation metric

Description

Estimate number of signatures based on cophenetic correlation metric

Usage

estimateSignatures(
  mat,
  nMin = 2,
  nTry = 6,
  nrun = 10,
  parallel = 4,
  pConstant = NULL,
  verbose = TRUE,
  plotBestFitRes = FALSE
)

Arguments

mat

Input matrix of diemnsion nx96 generated by trinucleotideMatrix
nMin

Minimum number of signatures to try. Default 2.
nTry

Maximum number of signatures to try. Default 6.
nrun

numeric giving the number of run to perform for each value in range. Default 5
parallel

Default 4. Number of cores to use.
pConstant

A small positive value to add to the matrix. Use it ONLY if the functions throws an non-conformable arrays error
verbose

Default TRUE
plotBestFitRes

plots consensus heatmap for range of values tried. Default FALSE

Details

This function decomposes a non-negative matrix into n signatures. Extracted signatures are compared against 30 experimentally validated signatures by calculating cosine similarity. See http://cancer.sanger.ac.uk/cosmic/signatures for details.

Value

a list with NMF.rank object and summary stats.

See Also

plotCophenetic extractSignatures trinucleotideMatrix

Examples

## Not run: 
laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.tnm <- trinucleotideMatrix(maf = laml, ref_genome = 'BSgenome.Hsapiens.UCSC.hg19', prefix = 'chr',
add = TRUE, useSyn = TRUE)
library("NMF")
laml.sign <- estimateSignatures(mat = laml.tnm, plotBestFitRes = FALSE, nMin = 2, nTry = 3, nrun = 2, pConstant = 0.01)

## End(Not run)

Extract mutational signatures from trinucleotide context.

Description

Decompose a matrix of 96 substitution classes into n signatures.

Usage

extractSignatures(
  mat,
  n = NULL,
  plotBestFitRes = FALSE,
  parallel = 4,
  pConstant = NULL
)

Arguments

mat

Input matrix of diemnsion nx96 generated by trinucleotideMatrix
n

decompose matrix into n signatures. Default NULL. Tries to predict best value for n by running NMF on a range of values and chooses based on cophenetic correlation coefficient.
plotBestFitRes

plots consensus heatmap for range of values tried. Default FALSE
parallel

Default 4. Number of cores to use.
pConstant

A small positive value to add to the matrix. Use it ONLY if the functions throws an non-conformable arrays error

Details

This function decomposes a non-negative matrix into n signatures.

Value

a list with decomposed scaled signatures, signature contributions in each sample and NMF object.

See Also

trinucleotideMatrix plotSignatures compareSignatures

Examples

## Not run: 
laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.tnm <- trinucleotideMatrix(maf = laml, ref_genome = 'BSgenome.Hsapiens.UCSC.hg19', prefix = 'chr',
add = TRUE, useSyn = TRUE)
library("NMF")
laml.sign <- extractSignatures(mat = laml.tnm, plotBestFitRes = FALSE, n = 2, pConstant = 0.01)

## End(Not run)

Filter MAF objects

Description

Filter MAF by genes or samples

Usage

filterMaf(maf, genes = NULL, tsb = NULL, isTCGA = FALSE)

Arguments

maf

an MAF object generated by read.maf
genes

remove these genes
tsb

remove these samples (Tumor Sample Barcodes)
isTCGA

FALSE

Value

Filtered object of class MAF-class

See Also

subsetMaf

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
#get rid of samples of interest
filterMaf(maf = laml, tsb = c("TCGA-AB-2830", "TCGA-AB-2804"))
#remove genes of intrest
filterMaf(maf = laml, genes =c("TTN", "AHNAK2"))

Draw forest plot for differences betweeen cohorts.

Description

Draw forest plot for differences betweeen cohorts.

Usage

forestPlot(
  mafCompareRes,
  pVal = 0.05,
  fdr = NULL,
  color = c("maroon", "royalblue"),
  geneFontSize = 0.8,
  titleSize = 1.2,
  lineWidth = 1
)

Arguments

mafCompareRes

results from mafCompare
pVal

p-value threshold. Default 0.05.
fdr

fdr threshold. Default NULL. If provided uses adjusted pvalues (fdr).
color

vector of two colors for the lines. Default 'maroon' and 'royalblue'
geneFontSize

Font size for gene symbols. Default 0.8
titleSize

font size for titles. Default 1.2
lineWidth

line width for CI bars. Default 1

Details

Plots results from link{mafCompare} as a forest plot with x-axis as log10 converted odds ratio and differentially mutated genes on y-axis.

Value

Nothing

See Also

mafCompare

Examples

##Primary and Relapse APL
primary.apl <- system.file("extdata", "APL_primary.maf.gz", package = "maftools")
relapse.apl <- system.file("extdata", "APL_relapse.maf.gz", package = "maftools")
##Read mafs
primary.apl <- read.maf(maf = primary.apl)
relapse.apl <- read.maf(maf = relapse.apl)
##Perform analysis and draw forest plot.
pt.vs.rt <- mafCompare(m1 = primary.apl, m2 = relapse.apl, m1Name = 'Primary',
m2Name = 'Relapse', minMut = 5)
forestPlot(mafCompareRes = pt.vs.rt)

Extracts Tumor Sample Barcodes where the given genes are mutated.

Description

Extracts Tumor Sample Barcodes where the given genes are mutated.

Usage

genesToBarcodes(maf, genes = NULL, justNames = FALSE, verbose = TRUE)

Arguments

maf

an MAF object generated by read.maf
genes

Hogo_Symbol for which sample names to be extracted.
justNames

if TRUE, just returns samples names instead of summarized tables.
verbose

Default TRUE

Value

list of data.tables with samples in which given genes are mutated.

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
genesToBarcodes(maf = laml, genes = 'DNMT3A')

Creates a Genotype Matrix for every variant

Description

Creates a Genotype matrix using allele frequcies or by muatation status.

Usage

genotypeMatrix(
  maf,
  genes = NULL,
  tsb = NULL,
  includeSyn = FALSE,
  vafCol = NULL,
  vafCutoff = c(0.1, 0.75)
)

Arguments

maf

an MAF object generated by read.maf
genes

create matrix for only these genes. Define NULL
tsb

create matrix for only these tumor sample barcodes/samples. Define NULL
includeSyn

whether to include silent mutations. Default FALSE
vafCol

specify column name for vaf's. Default NULL. If not provided simply assumes all mutations are heterozygous.
vafCutoff

specify minimum and maximum vaf to define mutations as heterozygous. Default range 0.1 to 0.75. Mutations above maximum vafs are defined as homozygous.

Value

matrix

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
genotypeMatrix(maf = laml, genes = "RUNX1")

extract annotations from MAF object

Description

extract annotations from MAF object

Usage

getClinicalData(x)

## S4 method for signature 'MAF'
getClinicalData(x)

Arguments

x

An object of class MAF

Value

annotations associated with samples in MAF

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
getClinicalData(x = laml)

extract cytoband summary from GISTIC object

Description

extract cytoband summary from GISTIC object

Usage

getCytobandSummary(x)

## S4 method for signature 'GISTIC'
getCytobandSummary(x)

Arguments

x

An object of class GISTIC

Value

summarizied gistic results by altered cytobands.

Examples

all.lesions <- system.file("extdata", "all_lesions.conf_99.txt", package = "maftools")
amp.genes <- system.file("extdata", "amp_genes.conf_99.txt", package = "maftools")
del.genes <- system.file("extdata", "del_genes.conf_99.txt", package = "maftools")
scores.gistic <- system.file("extdata", "scores.gistic", package = "maftools")
laml.gistic = readGistic(gisticAllLesionsFile = all.lesions, gisticAmpGenesFile = amp.genes, gisticDelGenesFile = del.genes, gisticScoresFile = scores.gistic)
getCytobandSummary(laml.gistic)

extract available fields from MAF object

Description

extract available fields from MAF object

Usage

getFields(x)

## S4 method for signature 'MAF'
getFields(x)

Arguments

x

An object of class MAF

Value

Field names in MAF file

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
getFields(x = laml)

extract gene summary from MAF or GISTIC object

Description

extract gene summary from MAF or GISTIC object

Usage

getGeneSummary(x)

## S4 method for signature 'MAF'
getGeneSummary(x)

## S4 method for signature 'GISTIC'
getGeneSummary(x)

Arguments

x

An object of class MAF or GISTIC

Value

gene summary table

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
getGeneSummary(laml)

extract sample summary from MAF or GISTIC object

Description

extract sample summary from MAF or GISTIC object

Usage

getSampleSummary(x)

## S4 method for signature 'MAF'
getSampleSummary(x)

## S4 method for signature 'GISTIC'
getSampleSummary(x)

Arguments

x

An object of class MAF or GISTIC

Value

sample summary table

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
getSampleSummary(x = laml)

Class GISTIC

Description

S4 class for storing summarized MAF.

Slots

data

data.table of summarized GISTIC file.

cnv.summary

table containing alterations per sample

cytoband.summary

table containing alterations per cytoband

gene.summary

table containing alterations per gene

cnMatrix

character matrix of dimension n*m where n is number of genes and m is number of samples

numericMatrix

numeric matrix of dimension n*m where n is number of genes and m is number of samples

gis.scores

gistic.scores

summary

table with basic GISTIC summary stats

classCode

mapping between numeric values in numericMatrix and copy number events.

See Also

getGeneSummary getSampleSummary getCytobandSummary

Plot gistic results as a bubble plot

Description

Plots significantly altered cytobands as a function of number samples in which it is altered and number genes it contains. Size of each bubble is according to -log10 transformed q values.

Usage

gisticBubblePlot(
  gistic = NULL,
  color = NULL,
  markBands = NULL,
  fdrCutOff = 0.1,
  log_y = TRUE,
  txtSize = 3
)

Arguments

gistic

an object of class GISTIC generated by readGistic
color

colors for Amp and Del events.
markBands

any cytobands to label. Can be cytoband labels, or number of top bands to highlight. Default top 5 lowest q values.
fdrCutOff

fdr cutoff to use. Default 0.1
log_y

log10 scale y-axis (# genes affected). Default TRUE
txtSize

label size for bubbles.

Value

Nothing

Examples

all.lesions <- system.file("extdata", "all_lesions.conf_99.txt", package = "maftools")
amp.genes <- system.file("extdata", "amp_genes.conf_99.txt", package = "maftools")
del.genes <- system.file("extdata", "del_genes.conf_99.txt", package = "maftools")
scores.gistic <- system.file("extdata", "scores.gistic", package = "maftools")
laml.gistic = readGistic(gisticAllLesionsFile = all.lesions, gisticAmpGenesFile = amp.genes, gisticDelGenesFile = del.genes, gisticScoresFile = scores.gistic)
gisticBubblePlot(gistic = laml.gistic, markBands = "")

Plot gistic results along linearized chromosome

Description

A genomic plot with segments highlighting signififcant Amplifications and Deletion regions.

Usage

gisticChromPlot(
  gistic = NULL,
  fdrCutOff = 0.1,
  markBands = NULL,
  color = NULL,
  ref.build = "hg19",
  cytobandOffset = 0.01,
  txtSize = 0.8,
  cytobandTxtSize = 0.6,
  maf = NULL,
  mutGenes = NULL,
  y_lims = NULL,
  mutGenesTxtSize = 0.6
)

Arguments

gistic

an object of class GISTIC generated by readGistic
fdrCutOff

fdr cutoff to use. Default 0.1
markBands

any cytobands to label. Default top 5 lowest q values.
color

colors for Amp and Del events.
ref.build

reference build. Could be hg18, hg19 or hg38.
cytobandOffset

if scores.gistic file is given use this to adjust cytoband size.
txtSize

label size for lables
cytobandTxtSize

label size for cytoband
maf

an optional maf object
mutGenes

mutated genes from maf object to be highlighted
y_lims

Deafult NULL. A vector upper and lower y-axis limits
mutGenesTxtSize

Default 0.6

Value

nothing

Examples

all.lesions <- system.file("extdata", "all_lesions.conf_99.txt", package = "maftools")
amp.genes <- system.file("extdata", "amp_genes.conf_99.txt", package = "maftools")
del.genes <- system.file("extdata", "del_genes.conf_99.txt", package = "maftools")
scores.gistic <- system.file("extdata", "scores.gistic", package = "maftools")
laml.gistic = readGistic(gisticAllLesionsFile = all.lesions, gisticAmpGenesFile = amp.genes, gisticDelGenesFile = del.genes, gisticScoresFile = scores.gistic)
gisticChromPlot(laml.gistic)

compare two GISTIC objects

Description

compare two GISTIC objects

Usage

gisticCompare(
  g1,
  g2,
  g1Name = NULL,
  g2Name = NULL,
  minEvent = 5,
  pseudoCount = FALSE
)

Arguments

g1

first GISTIC object
g2

second GISTIC object
g1Name

optional name for first cohort
g2Name

optional name for second cohort
minEvent

Consider only cytobands with minimum this number of samples altered in at least one of the cohort for analysis. Helpful to ignore single mutated genes. Default 5.
pseudoCount

If TRUE, adds 1 to the contingency table with 0's to avoid 'Inf' values in the estimated odds-ratio.

Details

Performs fisher test on 2x2 contingency table generated from two GISTIC objects

Value

result list

See Also

forestPlot

lollipopPlot2

Plot gistic results.

Description

takes output generated by readGistic and draws a plot similar to oncoplot.

Usage

gisticOncoPlot(
  gistic = NULL,
  top = NULL,
  bands = NULL,
  showTumorSampleBarcodes = FALSE,
  gene_mar = 5,
  barcode_mar = 6,
  right_mar = 2.5,
  sepwd_genes = 0.5,
  sepwd_samples = 0.25,
  clinicalData = NULL,
  clinicalFeatures = NULL,
  sortByAnnotation = FALSE,
  sampleOrder = NULL,
  annotationColor = NULL,
  bandsToIgnore = NULL,
  removeNonAltered = TRUE,
  colors = NULL,
  SampleNamefontSize = 0.6,
  fontSize = 0.8,
  legendFontSize = 1.2,
  annotationFontSize = 1.2,
  borderCol = "white",
  bgCol = "#CCCCCC"
)

Arguments

gistic

an GISTIC object generated by readGistic
top

how many top cytobands to be drawn. defaults to all.
bands

draw oncoplot for these bands. Default NULL.
showTumorSampleBarcodes

logical to include sample names.
gene_mar

Default 5
barcode_mar

Default 6
right_mar

Default 2.5
sepwd_genes

Default 0.5
sepwd_samples

Default 0.25
clinicalData

data.frame with columns containing Tumor_Sample_Barcodes and rest of columns with annotations.
clinicalFeatures

columns names from 'clinicalData' to be drawn in the plot. Dafault NULL.
sortByAnnotation

logical sort oncomatrix (samples) by provided 'clinicalFeatures'. Defaults to FALSE. column-sort
sampleOrder

Manually speify sample names for oncolplot ordering. Default NULL.
annotationColor

list of colors to use for clinicalFeatures. Default NULL.
bandsToIgnore

do not show these bands in the plot Default NULL.
removeNonAltered

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

named vector of colors Amp and Del events.
SampleNamefontSize

font size for sample names. Default 0.6
fontSize

font size for cytoband names. Default 0.8
legendFontSize

font size for legend. Default 1.2
annotationFontSize

font size for annotations. Default 1.2
borderCol

Default "white"
bgCol

Default "#CCCCCC"

Details

Takes gistic file as input and plots it as a matrix. Any desired annotations can be added at the bottom of the oncoplot by providing annotation

Value

None.

See Also

oncostrip

Examples

all.lesions <- system.file("extdata", "all_lesions.conf_99.txt", package = "maftools")
amp.genes <- system.file("extdata", "amp_genes.conf_99.txt", package = "maftools")
del.genes <- system.file("extdata", "del_genes.conf_99.txt", package = "maftools")
scores.gistic <- system.file("extdata", "scores.gistic", package = "maftools")
laml.gistic = readGistic(gisticAllLesionsFile = all.lesions, gisticAmpGenesFile = amp.genes, gisticDelGenesFile = del.genes, gisticScoresFile = scores.gistic)
gisticOncoPlot(laml.gistic)

Extract read counts from genetic markers for ASCAT analysis

Description

The function will generate tsv files '<tumor/normal>_nucleotide_counts.tsv' that can be used for downstream analysis. Note that the function will process ~900K loci from Affymetrix Genome-Wide Human SNP 6.0 Array. The process can be sped up by increasing 'nthreads' which will launch each chromosome on a separate thread. Currently hg19 and hg38 are supported. Files need to be further processed with prepAscat for tumor-normal pair, or prepAscat_t for tumor only samples.

Usage

gtMarkers(
  t_bam = NULL,
  n_bam = NULL,
  build = "hg19",
  prefix = NULL,
  add = TRUE,
  mapq = 10,
  sam_flag = 1024,
  loci = NULL,
  fa = NULL,
  op = NULL,
  zerobased = FALSE,
  nthreads = 4,
  verbose = TRUE
)

Arguments

t_bam

Tumor BAM file. Required
n_bam

Normal BAM file. Recommended
build

Default hg19. Mutually exclusive with 'loci'. Currently supported 'hg19' and 'hg38' and includes ca. 900K SNPs from Affymetrix Genome-Wide Human SNP 6.0 Array. SNP file has no 'chr' prefix.
prefix

Prefix to add or remove from contig names in loci file. For example, in case BAM files have ‘chr' prefix, set prefix = ’chr'
add

If prefix is used, default is to add prefix to contig names in loci file. If false prefix will be removed from contig names.
mapq

Minimum mapping quality. Default 10
sam_flag

SAM FLAG to filter reads. Default 1024
loci

A tab separated file with chr and position. If not available use 'build' argument.
fa

Indexed fasta file. If provided, extracts and adds reference base to the output tsv.
op

Output file basename. Default parses from BAM file
zerobased

are coordinates zero-based. Default FALSE. Use only if 'loci' is used.
nthreads

Number of threads to use. Default 4. Each chromosome will be launched on a separate thread. Works only on Unix and macOS.
verbose

Default TRUE

See Also

prepAscat prepAscat_t segmentLogR

Converts ICGC Simple Somatic Mutation format file to MAF

Description

Converts ICGC Simple Somatic Mutation format file to Mutation Annotation Format. Basic fields are converted as per MAF specififcations, rest of the fields are retained as in the input file. Ensemble gene IDs are converted to HGNC Symbols. Note that by default Simple Somatic Mutation format contains all affected transcripts of a variant resuting in multiple entries of the same variant in same sample. It is hard to choose a single affected transcript based on annotations alone and by default this program removes repeated variants as duplicated entries. If you wish to keep all of them, set removeDuplicatedVariants to FALSE.

Usage

icgcSimpleMutationToMAF(
  icgc,
  basename = NA,
  MAFobj = FALSE,
  clinicalData = NULL,
  removeDuplicatedVariants = TRUE,
  addHugoSymbol = FALSE
)

Arguments

icgc

Input data in ICGC Simple Somatic Mutation format. Can be gz compressed.
basename

If given writes to output file with basename.
MAFobj

If TRUE returns results as an MAF object.
clinicalData

Clinical data associated with each sample/Tumor_Sample_Barcode in MAF. Could be a text file or a data.frame. Default NULL.
removeDuplicatedVariants

removes repeated variants in a particuar sample, mapped to multiple transcripts of same Gene. See Description. Default TRUE.
addHugoSymbol

If TRUE replaces ensemble gene IDs with Hugo_Symbols. Default FALSE.

Details

ICGC Simple Somatic Mutattion format specififcation can be found here: http://docs.icgc.org/submission/guide/icgc-simple-somatic-mutation-format/

Value

tab delimited MAF file.

Examples

esca.icgc <- system.file("extdata", "simple_somatic_mutation.open.ESCA-CN.sample.tsv.gz", package = "maftools")
esca.maf <- icgcSimpleMutationToMAF(icgc = esca.icgc)

Clusters variants based on Variant Allele Frequencies (VAF).

Description

takes output generated by read.maf and clusters variants to infer tumor heterogeneity. This function requires VAF for clustering and density estimation. VAF can be on the scale 0-1 or 0-100. Optionally if copy number information is available, it can be provided as a segmented file (e.g, from Circular Binary Segmentation). Those variants in copy number altered regions will be ignored.

Usage

inferHeterogeneity(
  maf,
  tsb = NULL,
  top = 5,
  vafCol = NULL,
  segFile = NULL,
  ignChr = NULL,
  minVaf = 0,
  maxVaf = 1,
  useSyn = FALSE,
  dirichlet = FALSE
)

Arguments

maf

an MAF object generated by read.maf
tsb

specify sample names (Tumor_Sample_Barcodes) for which clustering has to be done.
top

if tsb is NULL, uses top n number of most mutated samples. Defaults to 5.
vafCol

manually specify column name for vafs. Default looks for column 't_vaf'
segFile

path to CBS segmented copy number file. Column names should be Sample, Chromosome, Start, End, Num_Probes and Segment_Mean (log2 scale).
ignChr

ignore these chromosomes from analysis. e.g, sex chromsomes chrX, chrY. Default NULL.
minVaf

filter low frequency variants. Low vaf variants maybe due to sequencing error. Default 0. (on the scale of 0 to 1)
maxVaf

filter high frequency variants. High vaf variants maybe due to copy number alterations or impure tumor. Default 1. (on the scale of 0 to 1)
useSyn

Use synonymous variants. Default FALSE.
dirichlet

Deprecated! No longer supported. uses nonparametric dirichlet process for clustering. Default FALSE - uses finite mixture models.

Details

This function clusters variants based on VAF to estimate univariate density and cluster classification. There are two methods available for clustering. Default using parametric finite mixture models and another method using nonparametric inifinite mixture models (Dirichlet process).

Value

list of clustering tables.

References

Chris Fraley and Adrian E. Raftery (2002) Model-based Clustering, Discriminant Analysis and Density Estimation Journal of the American Statistical Association 97:611-631

Jara A, Hanson TE, Quintana FA, Muller P, Rosner GL. DPpackage: Bayesian Semi- and Nonparametric Modeling in R. Journal of statistical software. 2011;40(5):1-30.

Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics. 2004;5(4):557-72.

See Also

plotClusters

Examples

## Not run: 
laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
TCGA.AB.2972.clust <- inferHeterogeneity(maf = laml, tsb = 'TCGA-AB-2972', vafCol = 'i_TumorVAF_WU')

## End(Not run)

Draws lollipop plot of amino acid changes on to Protein structure.

Description

Draws lollipop plot of amino acid changes. Protein domains are derived from PFAM database.

Usage

lollipopPlot(
  maf,
  data = NULL,
  gene = NULL,
  AACol = NULL,
  labelPos = NULL,
  labPosSize = 0.9,
  showMutationRate = TRUE,
  showDomainLabel = TRUE,
  cBioPortal = FALSE,
  refSeqID = NULL,
  proteinID = NULL,
  roundedRect = TRUE,
  repel = FALSE,
  collapsePosLabel = TRUE,
  showLegend = TRUE,
  legendTxtSize = 0.8,
  labPosAngle = 0,
  domainLabelSize = 0.8,
  axisTextSize = c(1, 1),
  printCount = FALSE,
  colors = NULL,
  domainAlpha = 1,
  domainBorderCol = "black",
  bgBorderCol = "black",
  labelOnlyUniqueDoamins = TRUE,
  defaultYaxis = FALSE,
  titleSize = c(1.2, 1),
  pointSize = 1.5
)

Arguments

maf

an MAF object generated by read.maf
data

Provide a custom two column data frame with pos and counts instead of an MAF. Input data can also contain an additional column 'Variant_Classification' used for color coding the dots.
gene

HGNC symbol for which protein structure to be drawn.
AACol

manually specify column name for amino acid changes. Default looks for fields 'HGVSp_Short', 'AAChange' or 'Protein_Change'. Changes can be of any format i.e, can be a numeric value or HGVSp annotations (e.g; p.P459L, p.L2195Pfs*30 or p.Leu2195ProfsTer30)
labelPos

Amino acid positions to label. If 'all', labels all variants.
labPosSize

Text size for labels. Default 0.9
showMutationRate

Whether to show the somatic mutation rate on the title. Default TRUE
showDomainLabel

Label domains within the plot. Default TRUE. If 'FALSE“ domains are annotated in legend.
cBioPortal

Adds annotations similar to cBioPortals MutationMapper and collapse Variants into Truncating and rest.
refSeqID

RefSeq transcript identifier for gene if known.
proteinID

RefSeq protein identifier for gene if known.
roundedRect

Default TRUE. If 'TRUE' domains are drawn with rounded corners. Requires berryFunctions
repel

If points are too close to each other, use this option to repel them. Default FALSE. Warning: naive method, might make plot ugly in case of too many variants!
collapsePosLabel

Collapses overlapping labels at same position. Default TRUE
showLegend

Default TRUE
legendTxtSize

Text size for legend. Default 0.8
labPosAngle

angle for labels. Defaults to horizonal 0 degree labels. Set to 90 for vertical; 45 for diagonal labels.
domainLabelSize

text size for domain labels. Default 0.8
axisTextSize

text size x and y tick labels. Default c(1,1).
printCount

If TRUE, prints number of summarized variants for the given protein.
colors

named vector of colors for each Variant_Classification. Default NULL.
domainAlpha

Default 1
domainBorderCol

Default "black". Set to NA to remove.
bgBorderCol

Default "black". Set to NA to remove.
labelOnlyUniqueDoamins

Default TRUE only labels unique doamins.
defaultYaxis

If FALSE, just labels min and maximum y values on y axis.
titleSize

font size for title and subtitle. Default c(1.2, 1)
pointSize

size of lollipop heads. Default 1.5

Details

This function by default looks for fields 'HGVSp_Short', 'AAChange' or 'Protein_Change' in maf file. One can also manually specify field name containing amino acid changes.

Value

Nothing

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
lollipopPlot(maf = laml, gene = 'KIT', AACol = 'Protein_Change')

Compare two lollipop plots

Description

Compare two lollipop plots

Usage

lollipopPlot2(
  m1,
  m2,
  gene = NULL,
  AACol1 = NULL,
  AACol2 = NULL,
  m1_name = NULL,
  m2_name = NULL,
  m1_label = NULL,
  m2_label = NULL,
  refSeqID = NULL,
  proteinID = NULL,
  labPosAngle = 0,
  labPosSize = 0.9,
  colors = NULL,
  alpha = 1,
  axisTextSize = c(1, 1),
  pointSize = 1.2,
  roundedRect = TRUE,
  showDomainLabel = TRUE,
  domainBorderCol = "black",
  domainLabelSize = 1,
  legendTxtSize = 1,
  verbose = TRUE
)

Arguments

m1

first MAF object
m2

second MAF object
gene

HGNC symbol for which protein structure to be drawn.
AACol1

manually specify column name for amino acid changes in m1. Default looks for fields 'HGVSp_Short', 'AAChange' or 'Protein_Change'.
AACol2

manually specify column name for amino acid changes in m2. Default looks for fields 'HGVSp_Short', 'AAChange' or 'Protein_Change'.
m1_name

name for m1 cohort. optional.
m2_name

name for m2 cohort. optional.
m1_label

Amino acid positions to label for m1 cohort. If 'all', labels all variants.
m2_label

Amino acid positions to label for m2 cohort. If 'all', labels all variants.
refSeqID

RefSeq transcript identifier for gene if known.
proteinID

RefSeq protein identifier for gene if known.
labPosAngle

angle for labels. Defaults to horizonal 0 degree labels. Set to 90 for vertical; 45 for diagonal labels.
labPosSize

Text size for labels. Default 3
colors

named vector of colors for each Variant_Classification. Default NULL.
alpha

color adjustment. Default 1
axisTextSize

text size for axis labels. Default 1.
pointSize

size of lollipop heads. Default 1.2
roundedRect

Default FALSE. If 'TRUE' domains are drawn with rounded corners. Requires berryFunctions
showDomainLabel

Label domains within the plot. Default TRUE. If FALSE domains are annotated in legend.
domainBorderCol

Default "black". Set to NA to remove.
domainLabelSize

text size for domain labels. Default 1.
legendTxtSize

Default 1.
verbose

Default TRUE

Details

Draws lollipop plot for a gene from two cohorts

Value

invisible list of domain overlaps

See Also

lollipopPlot

mafCompare

Examples

primary.apl <- system.file("extdata", "APL_primary.maf.gz", package = "maftools")
relapse.apl <- system.file("extdata", "APL_relapse.maf.gz", package = "maftools")
primary.apl <- read.maf(maf = primary.apl)
relapse.apl <- read.maf(maf = relapse.apl)
lollipopPlot2(m1 = primary.apl, m2 = relapse.apl, gene = "FLT3",AACol1 = "amino_acid_change", AACol2 = "amino_acid_change", m1_name = "Primary", m2_name = "Relapse")

Construct an MAF object

Description

Constructor function which takes non-synonymous, and synonymous variants along with an optional clinical information and generates an MAF object

Usage

MAF(nonSyn = NULL, syn = NULL, clinicalData = NULL, verbose = TRUE)

Arguments

nonSyn

non-synonymous variants as a data.table or any object that can be coerced into a data.table (e.g: data.frame, GRanges)
syn

synonymous variants as a data.table or any object that can be coerced into a data.table (e.g: data.frame, GRanges)
clinicalData

Clinical data associated with each sample/Tumor_Sample_Barcode in MAF. Could be a text file or a data.frame. Requires at least a column with the name 'Tumor_Sample_Barcode' Default NULL.
verbose

Default TRUE

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml_dt = data.table::fread(input = laml.maf)
laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools') #Clinical data
# Just for demonstration
nsyn_vars = laml_dt[Variant_Classification %in% "Missense_Mutation"]
syn_vars = laml_dt[Variant_Classification %in% "Silent"]
maftools::MAF(nonSyn = nsyn_vars, syn = syn_vars, clinicalData = laml.clin)

Class MAF

Description

S4 class for storing summarized MAF.

Slots

data

data.table of MAF file containing all non-synonymous variants.

variants.per.sample

table containing variants per sample

variant.type.summary

table containing variant types per sample

variant.classification.summary

table containing variant classification per sample

gene.summary

table containing variant classification per gene

summary

table with basic MAF summary stats

maf.silent

subset of main MAF containing only silent variants

clinical.data

clinical data associated with each sample/Tumor_Sample_Barcode in MAF.

See Also

getGeneSummary getSampleSummary getFields

Convert MAF to MultiAssayExperiment object

Description

Generates an object of class MultiAssayExperiment from MAF object

Usage

maf2mae(m = NULL)

Arguments

m

an MAF object

Examples

laml.maf = system.file('extdata', 'tcga_laml.maf.gz', package = 'maftools')
laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools')
laml = read.maf(maf = laml.maf, clinicalData = laml.clin)
maf2mae(laml)

Creates a bar plot

Description

Takes an MAF object and generates a barplot of mutated genes color coded for variant classification

Usage

mafbarplot(
  maf,
  n = 20,
  genes = NULL,
  color = NULL,
  fontSize = 0.7,
  includeCN = FALSE,
  legendfontSize = 0.7,
  borderCol = "#34495e",
  showPct = TRUE
)

Arguments

maf

an MAF object
n

Number of genes to include. Default 20.
genes

Manually provide names of genes. Default NULL.
color

named vector of colors for each Variant_Classification. Default NULL.
fontSize

Default 0.7
includeCN

Include copy number events if available? Default FALSE
legendfontSize

Default 0.7
borderCol

Default "#34495e". Set to 'NA' for no border color.
showPct

Default TRUE. Show percent altered samples.

Examples

laml.maf = system.file("extdata", "tcga_laml.maf.gz", package = "maftools") #MAF file
laml = read.maf(maf = laml.maf)
mafbarplot(maf = laml)

compare two cohorts (MAF).

Description

compare two cohorts (MAF).

Usage

mafCompare(
  m1,
  m2,
  m1Name = NULL,
  m2Name = NULL,
  minMut = 5,
  useCNV = TRUE,
  pathways = NULL,
  custom_pw = NULL,
  pseudoCount = FALSE
)

Arguments

m1

first MAF object
m2

second MAF object
m1Name

optional name for first cohort
m2Name

optional name for second cohort
minMut

Consider only genes with minimum this number of samples mutated in atleast one of the cohort for analysis. Helful to ignore single mutated genes. Default 5.
useCNV

whether to include copy number events. Default TRUE if available.. Not applicable when 'pathways = TRUE'
pathways

Summarize genes by pathways before comparing. Can be either 'sigpw' or 'smgbp', 'sigpw' uses known oncogenic signalling pathways (Sanchez/Vega et al) whereas 'smgbp' uses pan cancer significantly mutated genes classified according to biological process (Bailey et al). Default NULL
custom_pw

Optional. Can be a two column data.frame/tsv-file with pathway-name and genes involved in them. Default 'NULL'. This argument is mutually exclusive with pathdb
pseudoCount

If TRUE, adds 1 to the contingency table with 0's to avoid 'Inf' values in the estimated odds-ratio.

Details

Performs fisher test on 2x2 contigency table generated from two cohorts to find differentially mutated genes.

Value

result list

See Also

forestPlot

lollipopPlot2

Examples

primary.apl <- system.file("extdata", "APL_primary.maf.gz", package = "maftools")
relapse.apl <- system.file("extdata", "APL_relapse.maf.gz", package = "maftools")
primary.apl <- read.maf(maf = primary.apl)
relapse.apl <- read.maf(maf = relapse.apl)
pt.vs.rt <- mafCompare(m1 = primary.apl, m2 = relapse.apl, m1Name = 'Primary',
m2Name = 'Relapse', minMut = 5)

Summary statistics of MAF

Description

Summarizes genes and samples irrespective of the type of alteration. This is different from getSampleSummary and getGeneSummary which returns summaries of only non-synonymous variants.

Usage

mafSummary(maf)

Arguments

maf

an MAF object generated by read.maf

Details

This function takes MAF object as input and returns summary table.

Value

Returns a list of summarized tables

See Also

getGeneSummary getSampleSummary

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
mafSummary(maf = laml)

Performs survival analysis for a geneset

Description

Similar to mafSurvival but for a geneset

Usage

mafSurvGroup(
  maf,
  geneSet = NULL,
  minMut = NA,
  clinicalData = NULL,
  time = "Time",
  Status = "Status"
)

Arguments

maf

an MAF object generated by read.maf
geneSet

gene names for which survival analysis needs to be performed.
minMut

minimum number of mutated genes in the 'geneSet' to consider a sample as a mutant. Default, 'NA', samples with all the genes mutated are treated as mutant group.
clinicalData

dataframe containing events and time to events. Default looks for clinical data in annotation slot of MAF.
time

column name containing time in clinicalData
Status

column name containing status of patients in clinicalData. must be logical or numeric. e.g, TRUE or FALSE, 1 or 0.

Value

Survival plot

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml.clin <- system.file("extdata", "tcga_laml_annot.tsv", package = "maftools")
laml <- read.maf(maf = laml.maf,  clinicalData = laml.clin)
mafSurvGroup(maf = laml, geneSet = c('DNMT3A', 'FLT3'), time = 'days_to_last_followup', Status = 'Overall_Survival_Status')

Performs survival analysis

Description

Performs survival analysis by grouping samples from maf based on mutation status of given gene(s) or manual grouping of samples.

Usage

mafSurvival(
  maf,
  genes = NULL,
  samples = NULL,
  clinicalData = NULL,
  time = "Time",
  Status = "Status",
  groupNames = c("Mutant", "WT"),
  showConfInt = TRUE,
  addInfo = TRUE,
  col = c("maroon", "royalblue"),
  isTCGA = FALSE,
  textSize = 12
)

Arguments

maf

an MAF object generated by read.maf
genes

gene names for which survival analysis needs to be performed. Samples with mutations in any one of the genes provided are used as mutants.
samples

samples to group by. Genes and samples are mutually exclusive.
clinicalData

dataframe containing events and time to events. Default looks for clinical data in annotation slot of MAF.
time

column name contining time in clinicalData
Status

column name containing status of patients in clinicalData. must be logical or numeric. e.g, TRUE or FALSE, 1 or 0.
groupNames

names for groups. Should be of length two. Default c("Mutant", "WT")
showConfInt

TRUE. Whether to show confidence interval in KM plot.
addInfo

TRUE. Whether to show survival info in the plot.
col

colors for plotting.
isTCGA

FALSE. Is data is from TCGA.
textSize

Text size for surv table. Default 7.

Details

This function takes MAF file and groups them based on mutation status associated with given gene(s) and performs survival analysis. Requires dataframe containing survival status and time to event. Make sure sample names match to Tumor Sample Barcodes from MAF file.

Value

Survival plot

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml.clin <- system.file("extdata", "tcga_laml_annot.tsv", package = "maftools")
laml <- read.maf(maf = laml.maf,  clinicalData = laml.clin)
mafSurvival(maf = laml, genes = 'DNMT3A', time = 'days_to_last_followup', Status = 'Overall_Survival_Status', isTCGA = TRUE)

calculates MATH (Mutant-Allele Tumor Heterogeneity) score.

Description

calcuates MATH scores from variant allele frequencies. Mutant-Allele Tumor Heterogeneity (MATH) score is a measure of intra-tumor genetic heterogeneity. High MATH scores are related to lower survival rates. This function requies vafs.

Usage

math.score(maf, vafCol = NULL, sampleName = NULL, vafCutOff = 0.075)

Arguments

maf

an MAF object generated by read.maf
vafCol

manually specify column name for vafs. Default looks for column 't_vaf'
sampleName

sample name for which MATH score to be calculated. If NULL, calculates for all samples.
vafCutOff

minimum vaf for a variant to be considered for score calculation. Default 0.075

Value

data.table with MATH score for every Tumor_Sample_Barcode

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

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.math <- math.score(maf = laml, vafCol = 'i_TumorVAF_WU',
sampleName = c('TCGA-AB-3009', 'TCGA-AB-2849', 'TCGA-AB-3002', 'TCGA-AB-2972'))

Merge multiple mafs into single MAF

Description

Merges multiple maf files/objects/data.frames into a single MAF.

Usage

merge_mafs(mafs, verbose = TRUE, ...)

Arguments

mafs

a list of MAF objects or data.frames or paths to MAF files.
verbose

Default TRUE
...

additional arguments passed read.maf

Value

MAF object

Generates count matrix of mutations.

Description

Generates a count matrix of mutations. i.e, number of mutations per gene per sample.

Usage

mutCountMatrix(
  maf,
  includeSyn = FALSE,
  countOnly = NULL,
  removeNonMutated = TRUE
)

Arguments

maf

an MAF object generated by read.maf
includeSyn

whether to include sysnonymous variants in ouput matrix. Default FALSE
countOnly

Default NULL - counts all variants. You can specify type of 'Variant_Classification' to count. For e.g, countOnly = 'Splice_Site' will generates matrix for only Splice_Site variants.
removeNonMutated

Logical Default TRUE, removes samples with no mutations from the matrix.

Value

Integer Matrix

See Also

getFields getGeneSummary getSampleSummary

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
##Generate matrix
mutCountMatrix(maf = laml)
##Generate count matrix of Splice_Site mutations
mutCountMatrix(maf = laml, countOnly = 'Splice_Site')

Detect cancer driver genes based on positional clustering of variants.

Description

Clusters variants based on their position to detect disease causing genes.

Usage

oncodrive(
  maf,
  AACol = NULL,
  minMut = 5,
  pvalMethod = "zscore",
  nBgGenes = 100,
  bgEstimate = TRUE,
  ignoreGenes = NULL
)

Arguments

maf

an MAF object generated by read.maf
AACol

manually specify column name for amino acid changes. Default looks for field 'AAChange'
minMut

minimum number of mutations required for a gene to be included in analysis. Default 5.
pvalMethod

either zscore (default method for oncodriveCLUST), poisson or combined (uses lowest of the two pvalues).
nBgGenes

minimum number of genes required to estimate background score. Default 100. Do not change this unless its necessary.
bgEstimate

If FALSE skips background estimation from synonymous variants and uses predifined values estimated from COSMIC synonymous variants.
ignoreGenes

Ignore these genes from analysis. Default NULL. Helpful in case data contains large number of variants belonging to polymorphic genes such as mucins and TTN.

Details

This is the re-implimentation of algorithm defined in OncodriveCLUST article. Concept is based on the fact that most of the variants in cancer causing genes are enriched at few specific loci (aka hotspots). This method takes advantage of such positions to identify cancer genes. Cluster score of 1 means, a single hotspot hosts all observed variants. If you use this function, please cite OncodriveCLUST article.

Value

data table of genes ordered according to p-values.

References

Tamborero D, Gonzalez-Perez A and Lopez-Bigas N. OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics. 2013; doi: 10.1093/bioinformatics/btt395s

See Also

plotOncodrive

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.sig <- oncodrive(maf = laml, AACol = 'Protein_Change', minMut = 5)

draw an oncoplot

Description

takes output generated by read.maf and draws an oncoplot

Usage

oncoplot(
  maf,
  top = 20,
  minMut = NULL,
  genes = NULL,
  altered = FALSE,
  drawRowBar = TRUE,
  drawColBar = TRUE,
  leftBarData = NULL,
  leftBarLims = NULL,
  leftBarVline = NULL,
  leftBarVlineCol = "gray70",
  rightBarData = NULL,
  rightBarLims = NULL,
  rightBarVline = NULL,
  rightBarVlineCol = "gray70",
  topBarData = NULL,
  topBarLims = NULL,
  topBarHline = NULL,
  topBarHlineCol = "gray70",
  logColBar = FALSE,
  includeColBarCN = TRUE,
  clinicalFeatures = NULL,
  annotationColor = NULL,
  annotationDat = NULL,
  pathways = NULL,
  topPathways = 3,
  path_order = NULL,
  selectedPathways = NULL,
  collapsePathway = FALSE,
  pwLineCol = "#535c68",
  pwLineWd = 1,
  draw_titv = FALSE,
  titv_col = NULL,
  showTumorSampleBarcodes = FALSE,
  tsbToPIDs = NULL,
  barcode_mar = 4,
  barcodeSrt = 90,
  gene_mar = 5,
  anno_height = 1,
  legend_height = 4,
  sortByAnnotation = FALSE,
  groupAnnotationBySize = TRUE,
  annotationOrder = NULL,
  sortByMutation = FALSE,
  keepGeneOrder = FALSE,
  GeneOrderSort = TRUE,
  sampleOrder = NULL,
  additionalFeature = NULL,
  additionalFeaturePch = 20,
  additionalFeatureCol = "gray70",
  additionalFeatureCex = 0.9,
  genesToIgnore = NULL,
  removeNonMutated = FALSE,
  fill = TRUE,
  cohortSize = NULL,
  colors = NULL,
  cBioPortal = FALSE,
  bgCol = "#ecf0f1",
  borderCol = "white",
  annoBorderCol = NA,
  numericAnnoCol = NULL,
  drawBox = FALSE,
  fontSize = 0.8,
  SampleNamefontSize = 1,
  titleFontSize = 1.5,
  legendFontSize = 1.2,
  annotationFontSize = 1.2,
  sepwd_genes = 0.5,
  sepwd_samples = 0.25,
  writeMatrix = FALSE,
  colbar_pathway = FALSE,
  showTitle = TRUE,
  titleText = NULL,
  showPct = TRUE
)

Arguments

maf

an MAF object generated by read.maf
top

how many top genes to be drawn. defaults to 20.
minMut

draw all genes with 'min' number of mutations. Can be an integer or fraction (of samples mutated), Default NULL
genes

Just draw oncoplot for these genes. Default NULL.
altered

Default FALSE. Chooses top genes based on muatation status. If TRUE chooses top genes based alterations (CNV or mutation).
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'.
leftBarVline

Draw vertical lines at these values. Default 'NULL'.
leftBarVlineCol

Line color for 'leftBarVline' Default gray70
rightBarData

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

limits for 'rightBarData'. Default 'NULL'.
rightBarVline

Draw vertical lines at these values. Default 'NULL'.
rightBarVlineCol

Line color for 'rightBarVline' Default gray70
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 containing sample names and values for each sample. This option is applicable when only 'drawColBar' is TRUE.
topBarLims

limits for 'topBarData'. Default 'NULL'.
topBarHline

Draw horizontal lines at these values. Default 'NULL'.
topBarHlineCol

Line color for 'topBarHline.' Default gray70
logColBar

Plot top bar plot on log10 scale. Default FALSE.
includeColBarCN

Whether to include CN in column bar plot. Default TRUE
clinicalFeatures

columns names from 'clinical.data' slot of MAF to be drawn in the plot. Default NULL.
annotationColor

Custom colors to use for 'clinicalFeatures'. Must be a named list containing a named vector of colors. Default NULL. See example for more info.
annotationDat

If MAF file was read without clinical data, provide a custom data.frame with a column Tumor_Sample_Barcode containing sample names along with rest of columns with annotations. You can specify which columns to be drawn using 'clinicalFeatures' argument.
pathways

Default 'NULL'. Can be 'sigpw', 'smgbp', or a two column data.frame/tsv-file with genes and corresponding pathway mappings.'
topPathways

Top most altered pathways to draw. Default 3. Mutually exclusive with 'selectedPathways'
path_order

Default 'NULL' Manually specify the order of pathways
selectedPathways

Manually provide the subset of pathway names to be selected from 'pathways'. Default NULL. In case 'pathways' is 'auto' draws top 3 altered pathways.
collapsePathway

Shows only rows corresponding to the pathways. Default FALSE.
pwLineCol

Color for the box around the pathways Default #535c68
pwLineWd

Line width for the box around the pathways Default Default 1
draw_titv

logical Includes TiTv plot. FALSE
titv_col

named vector of colors for each transition and transversion classes. Should be of length six with the names "C>T" "C>G" "C>A" "T>A" "T>C" "T>G". Default NULL.
showTumorSampleBarcodes

logical to include sample names.
tsbToPIDs

Custom names for Tumor_Sample_Barcodes. Can be a column name in clinicaldata or a 2 column data.frame of Tumor_Sample_Barcodes to patient ID mappings. Applicable only when 'showTumorSampleBarcodes = TRUE'. Default NULL.
barcode_mar

Margin width for sample names. Default 4
barcodeSrt

Rotate sample labels. Default 90.
gene_mar

Margin width for gene names. Default 5
anno_height

Height of plotting area for sample annotations. Default 1
legend_height

Height of plotting area for legend. Default 4
sortByAnnotation

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

Further group 'sortByAnnotation' orders by their size. Defaults to TRUE. Largest groups comes first.
annotationOrder

Manually specify order for annotations. Works only for first 'clinicalFeatures'. Default NULL.
sortByMutation

Force sort matrix according mutations. Helpful in case of MAF was read along with copy number data. Default FALSE.
keepGeneOrder

logical whether to keep order of given genes. Default FALSE, order according to mutation frequency
GeneOrderSort

logical this is applicable when 'keepGeneOrder' is TRUE. Default TRUE
sampleOrder

Manually speify sample names for oncolplot ordering. Default NULL.
additionalFeature

a vector of length two indicating column name in the MAF and the factor level to be highlighted. Provide a list of values for highlighting more than one features
additionalFeaturePch

Default 20
additionalFeatureCol

Default "gray70"
additionalFeatureCex

Default 0.9
genesToIgnore

do not show these genes in Oncoplot. Default NULL.
removeNonMutated

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

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

Number of sequenced samples in the cohort. Default all samples from Cohort. You can manually specify the cohort size. Default NULL
colors

named vector of colors for each Variant_Classification.
cBioPortal

Adds annotations similar to cBioPortals MutationMapper and collapse Variants into Truncating and rest.
bgCol

Background grid color for wild-type (not-mutated) samples. Default "#ecf0f1"
borderCol

border grid color (not-mutated) samples. Default 'white'.
annoBorderCol

border grid color for annotations. Default NA.
numericAnnoCol

color palette used for numeric annotations. Default 'YlOrBr' from RColorBrewer
drawBox

logical whether to draw a box around main matrix. Default FALSE
fontSize

font size for gene names. Default 0.8.
SampleNamefontSize

font size for sample names. Default 1
titleFontSize

font size for title. Default 1.5
legendFontSize

font size for legend. Default 1.2
annotationFontSize

font size for annotations. Default 1.2
sepwd_genes

size of lines seperating genes. Default 0.5
sepwd_samples

size of lines seperating samples. Default 0.25
writeMatrix

writes character coded matrix used to generate the plot to an output file.
colbar_pathway

Draw top column bar with respect to diplayed pathway. Default FALSE.
showTitle

Default TRUE
titleText

Custom title. Default 'NULL'
showPct

Default TRUE. Shows percent altered to the right side of the plot.

Details

Takes an MAF object as an input and plots it as a matrix. Any desired clincal features can be added at the bottom of the oncoplot by providing clinicalFeatures. Oncoplot can be sorted either by mutations or by clinicalFeatures using arguments sortByMutation and sortByAnnotation respectively.

By setting 'pathways' argument either 'sigpw' or 'smgbp' - cohort can be summarized by altered pathways. pathways argument also accepts a custom pathway list in the form of a two column tsv file or a data.frame containing gene names and their corresponding pathway.

Value

Invisibly returns a list with components 1. 'oncomatrix' A matrix used for drawing the oncoplot. Values are numeric coded for each variant classification 2. 'vc_legend' A mapping of variant classification to numeric values in the oncomatrix 3. 'vc_color' Color coding used for each variant classification

References

Bailey, Matthew H et al. “Comprehensive Characterization of Cancer Driver Genes and Mutations.” Cell vol. 173,2 (2018): 371-385.e18. doi:10.1016/j.cell.2018.02.060 Sanchez-Vega, Francisco et al. “Oncogenic Signaling Pathways in The Cancer Genome Atlas.” Cell vol. 173,2 (2018): 321-337.e10. doi:10.1016/j.cell.2018.03.035

See Also

pathways

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools')
laml <- read.maf(maf = laml.maf, clinicalData = laml.clin)
#Basic onocplot
oncoplot(maf = laml, top = 3)
#Changing colors for variant classifications (You can use any colors, here in this example we will use a color palette from RColorBrewer)
col = RColorBrewer::brewer.pal(n = 8, name = 'Paired')
names(col) = c('Frame_Shift_Del','Missense_Mutation', 'Nonsense_Mutation', 'Multi_Hit', 'Frame_Shift_Ins',
               'In_Frame_Ins', 'Splice_Site', 'In_Frame_Del')
#Color coding for FAB classification; try getAnnotations(x = laml) to see available annotations.
fabcolors = RColorBrewer::brewer.pal(n = 8,name = 'Spectral')
names(fabcolors) = c("M0", "M1", "M2", "M3", "M4", "M5", "M6", "M7")
fabcolors = list(FAB_classification = fabcolors)
oncoplot(maf = laml, colors = col, clinicalFeatures = 'FAB_classification', sortByAnnotation = TRUE, annotationColor = fabcolors)

draw an oncostrip similar to cBioportal oncoprinter output.

Description

draw an oncostrip similar to cBioportal oncoprinter output.

Usage

oncostrip(maf = NULL, ...)

Arguments

maf

an MAF object generated by read.maf
...

arguments passed oncoplot

Details

This is just a wrapper around oncoplot with drawRowBar and drawColBar set to FALSE

Value

None.

See Also

oncoplot

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
dev.new()
oncostrip(maf = laml, genes = c('NPM1', 'RUNX1'))

Enrichment of known oncogenic or custom pathways

Description

Checks for enrichment of known or custom pathways

Usage

pathways(
  maf,
  pathdb = "sigpw",
  pathways = NULL,
  fontSize = 1,
  panelWidths = c(2, 4, 4),
  plotType = NA,
  col = "#f39c12"
)

Arguments

maf

an MAF object generated by read.maf
pathdb

Either 'sigpw' or 'smgbp', 'sigpw' uses known oncogenic signalling pathways (Sanchez/Vega et al) whereas 'smgbp' uses pan cancer significantly mutated genes classified according to biological process (Bailey et al). Default smgbp
pathways

Can be a two column data.frame/tsv-file with gene names and pathway-name involved in them. Default 'NULL'. This argument is mutually exclusive with pathdb
fontSize

Default 1
panelWidths

Default c(2, 4, 4)
plotType

Can be 'treemap' or 'bar'. Set NA to suppress plotting. Default NA
col

Default #f39c12

Details

Oncogenic signalling and SMG pathways are derived from TCGA cohorts. See references for details.

Value

fraction of altered pathway. attr genes contain pathway contents

References

Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, Dimitriadoy S, Liu DL, Kantheti HS, Saghafinia S et al. 2018. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell 173: 321-337 e310 Bailey, Matthew H et al. “Comprehensive Characterization of Cancer Driver Genes and Mutations.” Cell vol. 173,2 (2018): 371-385.e18.

See Also

plotPathways

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
pathways(maf = laml)

pfam domain annotation and summarization.

Description

Summarizes amino acid positions and annotates them with pfam domain information.

Usage

pfamDomains(
  maf = NULL,
  AACol = NULL,
  summarizeBy = "AAPos",
  top = 5,
  domainsToLabel = NULL,
  baseName = NULL,
  varClass = "nonSyn",
  width = 5,
  height = 5,
  labelSize = 1
)

Arguments

maf

an MAF object generated by read.maf
AACol

manually specify column name for amino acid changes. Default looks for field 'AAChange'
summarizeBy

Summarize domains by amino acid position or conversions. Can be "AAPos" or "AAChange"
top

How many top mutated domains to label in the scatter plot. Defaults to 5.
domainsToLabel

Default NULL. Exclusive with top argument.
baseName

If given writes the results to output file. Default NULL.
varClass

which variants to consider for summarization. Can be nonSyn, Syn or all. Default nonSyn.
width

width of the file to be saved.
height

height of the file to be saved.
labelSize

font size for labels. Default 1.

Value

returns a list two tables summarized by amino acid positions and domains respectively. Also plots top 5 most mutated domains as scatter plot.

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
pfamDomains(maf = laml, AACol = 'Protein_Change')

Plot differences between APOBEC enriched and non-APOBEC enriched samples.

Description

Plots differences between APOBEC enriched and non-APOBEC enriched samples

Usage

plotApobecDiff(
  tnm,
  maf,
  pVal = 0.05,
  title_size = 1,
  axis_lwd = 1,
  font_size = 1.2
)

Arguments

tnm

output generated by trinucleotideMatrix
maf

an MAF object used to generate the matrix
pVal

p-value threshold for fisher's test. Default 0.05.
title_size

size of title. Default 1.3
axis_lwd

axis width. Default 1
font_size

font size. Default 1.2

Details

Plots differences between APOBEC enriched and non-APOBEC enriched samples (TCW). Plot includes differences in mutations load, tCw motif distribution and top genes altered.

Value

list of table containing differenatially altered genes. This can be passed to forestPlot to plot results.

See Also

trinucleotideMatrix plotSignatures

Examples

## Not run: 
laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.tnm <- trinucleotideMatrix(maf = laml, ref_genome = 'BSgenome.Hsapiens.UCSC.hg19', prefix = 'chr',
add = TRUE, useSyn = TRUE)
plotApobecDiff(laml.tnm)

## End(Not run)

Plots segmented copy number data.

Description

Plots segmented copy number data.

Usage

plotCBSsegments(
  cbsFile = NULL,
  maf = NULL,
  tsb = NULL,
  savePlot = FALSE,
  ylims = NULL,
  seg_size = 0.1,
  width = 6,
  height = 3,
  genes = NULL,
  ref.build = "hg19",
  writeTable = FALSE,
  removeXY = FALSE,
  color = NULL
)

Arguments

cbsFile

CBS segmented copy number file. Column names should be Sample, Chromosome, Start, End, Num_Probes and Segment_Mean (log2 scale).
maf

optional MAF
tsb

If segmentation file contains many samples (as in gistic input), specify sample name here. Defualt plots head 1 sample. Set 'ALL' for plotting all samples. If you are maping maf, make sure sample names in Sample column of segmentation file matches to those Tumor_Sample_Barcodes in MAF.
savePlot

If true plot is saved as pdf.
ylims

Default NULL
seg_size

Default 0.1
width

width of plot
height

height of plot
genes

If given and maf object is specified, maps all mutataions from maf onto segments. Default NULL
ref.build

Reference build for chromosome sizes. Can be hg18, hg19 or hg38. Default hg19.
writeTable

If true and if maf object is specified, writes plot data with each variant and its corresponding copynumber to an output file.
removeXY

don not plot sex chromosomes.
color

Manually specify color scheme for chromosomes. Default NULL. i.e, aletrnating Gray70 and midnightblue

Details

this function takes segmented copy number data and plots it. If MAF object is specified, all mutations are highlighted on the plot.

Value

Draws plot

Examples

tcga.ab.009.seg <- system.file("extdata", "TCGA.AB.3009.hg19.seg.txt", package = "maftools")
plotCBSsegments(cbsFile = tcga.ab.009.seg)

Plot density plots from clutering results.

Description

Plots results from inferHeterogeneity.

Usage

plotClusters(
  clusters,
  tsb = NULL,
  genes = NULL,
  showCNvars = FALSE,
  colors = NULL
)

Arguments

clusters

clustering results from inferHeterogeneity
tsb

sample to plot from clustering results. Default plots all samples from results.
genes

genes to highlight on the plot. Can be a vector of gene names, CN_altered to label copy number altered varinats. or all to label all genes. Default NULL.
showCNvars

show copy numbered altered variants on the plot. Default FALSE.
colors

manual colors for clusters. Default NULL.

Value

returns nothing.

See Also

inferHeterogeneity

Examples

## Not run: 
laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
seg = system.file('extdata', 'TCGA.AB.3009.hg19.seg.txt', package = 'maftools')
TCGA.AB.3009.clust <- inferHeterogeneity(maf = laml, tsb = 'TCGA-AB-3009',
segFile = seg, vafCol = 'i_TumorVAF_WU')
plotClusters(TCGA.AB.3009.clust, genes = c('NF1', 'SUZ12'), showCNvars = TRUE)

## End(Not run)

Draw an elbow plot of cophenetic correlation metric.

Description

Draw an elbow plot of cophenetic correlation metric.

Usage

plotCophenetic(res = NULL, bestFit = NULL)

Arguments

res

output from estimateSignatures
bestFit

rank to highlight. Default NULL

Details

This function draws an elbow plot of cophenetic correlation metric.

See Also

estimateSignatures plotCophenetic

Plots results from clinicalEnrichment analysis

Description

Plots results from clinicalEnrichment analysis

Usage

plotEnrichmentResults(
  enrich_res,
  pVal = 0.05,
  ORthr = 1,
  featureLvls = NULL,
  cols = NULL,
  annoFontSize = 0.8,
  geneFontSize = 0.8,
  legendFontSize = 0.8,
  showTitle = TRUE,
  ylims = c(-1, 1)
)

Arguments

enrich_res

results from clinicalEnrichment or signatureEnrichment
pVal

Default 0.05
ORthr

Default 1. Odds ratio threshold. >1 indicates positive enrichment in the group of interest.
featureLvls

Plot results from the selected levels. Default NULL, plots all.
cols

named vector of colors for factor in a clinical feature. Default NULL
annoFontSize

cex for annotation font size. Default 0.8
geneFontSize

cex for gene font size. Default 0.8
legendFontSize

cex for legend font size. Default 0.8
showTitle

Default TRUE
ylims

Default c(-1, 1)

Value

returns nothing.

See Also

clinicalEnrichment signatureEnrichment

Plots maf summary.

Description

Plots maf summary.

Usage

plotmafSummary(
  maf,
  rmOutlier = TRUE,
  dashboard = TRUE,
  titvRaw = TRUE,
  log_scale = FALSE,
  addStat = NULL,
  showBarcodes = FALSE,
  fs = 1,
  textSize = 0.8,
  color = NULL,
  titleSize = c(1, 0.8),
  titvColor = NULL,
  top = 10
)

Arguments

maf

an MAF object generated by read.maf
rmOutlier

If TRUE removes outlier from boxplot.
dashboard

If FALSE plots simple summary instead of dashboard style.
titvRaw

TRUE. If false instead of raw counts, plots fraction.
log_scale

FALSE. If TRUE log10 transforms Variant Classification, Variant Type and Variants per sample sub-plots.
addStat

Can be either mean or median. Default NULL.
showBarcodes

include sample names in the top bar plot.
fs

base size for text. Default 1
textSize

font size if showBarcodes is TRUE. Default 0.8
color

named vector of colors for each Variant_Classification.
titleSize

font size for title and subtitle. Default c(10, 8)
titvColor

colors for SNV classifications.
top

include top n genes dashboard plot. Default 10.

Value

Prints plot.

See Also

read.maf MAF

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf, useAll = FALSE)
plotmafSummary(maf = laml, addStat = 'median')

Plot results from mosdepth output for Tumor/Normal pair

Description

Plot results from mosdepth output for Tumor/Normal pair

Usage

plotMosdepth(
  t_bed = NULL,
  n_bed = NULL,
  segment = TRUE,
  sample_name = NULL,
  col = c("#95a5a6", "#7f8c8d")
)

Arguments

t_bed

mosdepth output from tumor
n_bed

mosdepth output from matched normal
segment

Performs CBS segmentation. Default TRUE
sample_name

sample name. Default parses from 't_bed'
col

Colors. Default c("#95a5a6", "#7f8c8d")

Value

Invisibly returns DNAcopy object if 'segment' is 'TRUE'

References

Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics. 2018;34(5):867-868. doi:10.1093/bioinformatics/btx699

Plot results from mosdepth output

Description

Plot results from mosdepth output

Usage

plotMosdepth_t(
  bed = NULL,
  col = c("#95a5a6", "#7f8c8d"),
  sample_name = NULL,
  segment = FALSE
)

Arguments

bed

mosdepth output
col

Colors. Default c("#95a5a6", "#7f8c8d")
sample_name

sample name. Default parses from 'bed'
segment

Performs CBS segmentation. Default FALSE

Value

Invisibly returns DNAcopy object if 'segment' is 'TRUE'

References

Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics. 2018;34(5):867-868. doi:10.1093/bioinformatics/btx699

Plots results from oncodrive

Description

Takes results from oncodrive and plots them as a scatter plot. Size of the gene shows number of clusters (hotspots), x-axis can either be an absolute number of variants accumulated in these clusters or a fraction of total variants found in these clusters. y-axis is fdr values transformed into -log10 for better representation. Labels indicate Gene name with number clusters observed.

Usage

plotOncodrive(
  res = NULL,
  fdrCutOff = 0.05,
  useFraction = FALSE,
  colCode = NULL,
  bubbleSize = 1,
  labelSize = 1
)

Arguments

res

results from oncodrive
fdrCutOff

fdr cutoff to call a gene as a driver.
useFraction

if TRUE uses a fraction of total variants as X-axis scale instead of absolute counts.
colCode

Colors to use for indicating significant and non-signififcant genes. Default NULL
bubbleSize

Size for bubbles. Default 2.
labelSize

font size for labelling genes. Default 1.

Value

Nothing

See Also

oncodrive

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.sig <- oncodrive(maf = laml, AACol = 'Protein_Change', minMut = 5)
plotOncodrive(res = laml.sig, fdrCutOff = 0.1)

Plot oncogenic pathways

Description

Plot oncogenic pathways

Usage

plotPathways(
  maf = NULL,
  pathlist = NULL,
  pathnames = NULL,
  removeNonMutated = FALSE,
  fontSize = 1,
  showTumorSampleBarcodes = FALSE,
  sampleOrder = NULL,
  SampleNamefontSize = 0.6,
  mar = c(4, 6, 2, 3)
)

Arguments

maf

an MAF object
pathlist

Output from pathways
pathnames

Names of the pathways to be drawn. Default NULL, plots everything from input 'pathlist'
removeNonMutated

Default FALSE
fontSize

Default 1
showTumorSampleBarcodes

logical to include sample names.
sampleOrder

Manually speify sample names for oncolplot ordering. Default NULL.
SampleNamefontSize

font size for sample names. Default 0.6
mar

margins Default c(4, 6, 2, 3). Margins to bottom, left, top and right respectively

Details

Draws pathway burden123

References

Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, Dimitriadoy S, Liu DL, Kantheti HS, Saghafinia S et al. 2018. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell 173: 321-337 e310

See Also

pathways

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
p <- pathways(maf = laml)
plotPathways(maf = laml, pathlist = p)

Display protein domains

Description

Display protein domains

Usage

plotProtein(
  gene,
  refSeqID = NULL,
  proteinID = NULL,
  domainAlpha = 0.9,
  showLegend = FALSE,
  bgBorderCol = "black",
  axisTextSize = c(1, 1),
  roundedRect = TRUE,
  domainBorderCol = "black",
  showDomainLabel = TRUE,
  domainLabelSize = 0.8,
  titleSize = c(1.2, 1),
  legendTxtSize = 1,
  legendNcol = 1
)

Arguments

gene

HGNC symbol for which protein structure to be drawn.
refSeqID

RefSeq transcript identifier for gene if known.
proteinID

RefSeq protein identifier for gene if known.
domainAlpha

Default 1
showLegend

Default TRUE
bgBorderCol

Default "black". Set to NA to remove.
axisTextSize

text size x and y tick labels. Default c(1,1).
roundedRect

Default TRUE. If 'TRUE' domains are drawn with rounded corners. Requires berryFunctions
domainBorderCol

Default "black". Set to NA to remove.
showDomainLabel

Default TRUE
domainLabelSize

text size for domain labels. Default 0.8
titleSize

font size for title and subtitle. Default c(1.2, 1)
legendTxtSize

Text size for legend. Default 0.8
legendNcol

Default 1

Examples

par(mfrow = c(2, 1))
plotProtein(gene = "KIT")
plotProtein(gene = "DNMT3A")

Plots decomposed mutational signatures

Description

Takes results from extractSignatures and plots decomposed mutational signatures as a barplot.

Usage

plotSignatures(
  nmfRes = NULL,
  contributions = FALSE,
  absolute = FALSE,
  color = NULL,
  patient_order = NULL,
  font_size = 0.6,
  show_title = TRUE,
  sig_db = "SBS_v34",
  axis_lwd = 1,
  title_size = 0.9,
  show_barcodes = FALSE,
  yaxisLim = NA,
  ...
)

Arguments

nmfRes

results from extractSignatures
contributions

If TRUE plots contribution of signatures in each sample.
absolute

Whether to plot absolute contributions. Default FALSE.
color

colors for each Ti/Tv conversion class. Default NULL
patient_order

User defined ordering of samples. Default NULL.
font_size

font size. Default 0.6
show_title

If TRUE compares signatures to COSMIC signatures and prints them as title
sig_db

Only applicable if show_title is TRUE. can be legacy, SBS, SBS_v34. Default SBS_v34
axis_lwd

axis width. Default 1.
title_size

size of title. Default 1.3
show_barcodes

Default FALSE
yaxisLim

Default NA.
...

further plot options passed to barplot

Value

Nothing

See Also

trinucleotideMatrix plotSignatures

Plot Transition and Trasnversion ratios.

Description

Takes results generated from titv and plots the Ti/Tv ratios and contributions of 6 mutational conversion classes in each sample.

Usage

plotTiTv(
  res = NULL,
  plotType = "both",
  sampleOrder = NULL,
  color = NULL,
  showBarcodes = FALSE,
  textSize = 0.8,
  baseFontSize = 1,
  axisTextSize = c(1, 1),
  plotNotch = FALSE
)

Arguments

res

results generated by titv
plotType

Can be 'bar', 'box' or 'both'. Defaults to 'both'
sampleOrder

Sample names in which the barplot should be ordered. Default NULL
color

named vector of colors for each coversion class.
showBarcodes

Whether to include sample names for barplot
textSize

fontsize if showBarcodes is TRUE. Deafult 2.
baseFontSize

font size. Deafult 1.
axisTextSize

text size x and y tick labels. Default c(1,1).
plotNotch

logical. Include notch in boxplot.

Value

None.

See Also

titv

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.titv = titv(maf = laml, useSyn = TRUE)
plotTiTv(laml.titv)

Plots vaf distribution of genes

Description

Plots vaf distribution of genes as a boxplot. Each dot in the jitter is a variant.

Usage

plotVaf(
  maf,
  vafCol = NULL,
  genes = NULL,
  top = 10,
  orderByMedian = TRUE,
  keepGeneOrder = FALSE,
  flip = FALSE,
  fn = NULL,
  gene_fs = 0.8,
  axis_fs = 0.8,
  height = 5,
  width = 5,
  showN = TRUE,
  color = NULL
)

Arguments

maf

an MAF object generated by read.maf
vafCol

manually specify column name for vafs. Default looks for column 't_vaf'
genes

specify genes for which plots has to be generated
top

if genes is NULL plots top n number of genes. Defaults to 5.
orderByMedian

Orders genes by decreasing median VAF. Default TRUE
keepGeneOrder

keep gene order. Default FALSE
flip

if TRUE, flips axes. Default FALSE
fn

Filename. If given saves plot as a output pdf. Default NULL.
gene_fs

font size for gene names. Default 0.8
axis_fs

font size for axis. Default 0.8
height

Height of plot to be saved. Default 5
width

Width of plot to be saved. Default 4
showN

if TRUE, includes number of observations
color

manual colors. Default NULL.

Value

Nothing.

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
plotVaf(maf = laml, vafCol = 'i_TumorVAF_WU')

Prepares MAF file for MutSig analysis.

Description

Corrects gene names for MutSig compatibility.

Usage

prepareMutSig(maf, fn = NULL)

Arguments

maf

an MAF object generated by read.maf
fn

basename for output file. If provided writes MAF to an output file with the given basename.

Details

MutSig/MutSigCV is most widely used program for detecting driver genes. However, we have observed that covariates files (gene.covariates.txt and exome_full192.coverage.txt) which are bundled with MutSig have non-standard gene names (non Hugo_Symbols). This discrepancy between Hugo_Symbols in MAF and non-Hugo_symbols in covariates file causes MutSig program to ignore such genes. For example, KMT2D - a well known driver gene in Esophageal Carcinoma is represented as MLL2 in MutSig covariates. This causes KMT2D to be ignored from analysis and is represented as an insignificant gene in MutSig results. This function attempts to correct such gene symbols with a manually curated list of gene names compatible with MutSig covariates list.

Value

returns a MAF with gene symbols corrected.

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
prepareMutSig(maf = laml)

Prepare input files for ASCAT

Description

Function takes the output from gtMarkers and generates 'logR' and 'BAF' files required for ASCAT analysis.

Usage

prepAscat(
  t_counts = NULL,
  n_counts = NULL,
  sample_name = NA,
  min_depth = 15,
  normalize = TRUE
)

Arguments

t_counts

read counts from tumor generated by gtMarkers
n_counts

read counts from normal generated by gtMarkers
sample_name

Sample name. Used as a basename for output files. Default 'NA', parses from 't_counts' file.
min_depth

Min read depth required to consider a marker. Default 15
normalize

If TRUE, normalizes for library size. Default TRUE

Details

The function will filter SNPs with low coverage (default <15), estimate BAF, logR, and generates the input files for ASCAT. Alternatively, logR file can be segmented with segmentLogR

References

Van Loo P, Nordgard SH, Lingjærde OC, et al. Allele-specific copy number analysis of tumors. Proc Natl Acad Sci U S A. 2010;107(39):16910-16915. doi:10.1073/pnas.1009843107

See Also

gtMarkers prepAscat_t segmentLogR

Prepare input files for ASCAT tumor only samples

Description

Function takes the output from gtMarkers and generates 'logR' and 'BAF' files required for ASCAT analysis.

Usage

prepAscat_t(t_counts = NULL, sample_name = NA, min_depth = 15)

Arguments

t_counts

read counts from tumor generated by gtMarkers
sample_name

Sample name. Used as a basename for output files. Default NA, parses from 't_counts' file.
min_depth

Min read depth required to consider a marker. Default 15

Details

The function will filter SNPs with low coverage (default <15), estimate BAF, logR, and generates the input files for ASCAT. Tumor 'logR' file will be normalized for median depth of coverage. Alternatively, logR file can be segmented with segmentLogR

Value

Generates logR and BAF files required by ASCAT

References

Van Loo P, Nordgard SH, Lingjærde OC, et al. Allele-specific copy number analysis of tumors. Proc Natl Acad Sci U S A. 2010;107(39):16910-16915. doi:10.1073/pnas.1009843107

See Also

gtMarkers prepAscat segmentLogR

Rainfall plot to display hyper mutated genomic regions.

Description

Plots inter variant distance as a function of genomic locus.

Usage

rainfallPlot(
  maf,
  tsb = NULL,
  detectChangePoints = FALSE,
  ref.build = "hg19",
  color = NULL,
  savePlot = FALSE,
  width = 6,
  height = 3,
  fontSize = 1.2,
  pointSize = 0.4
)

Arguments

maf

an MAF object generated by read.maf. Required.
tsb

specify sample names (Tumor_Sample_Barcodes) for which plotting has to be done. If NULL, draws plot for most mutated sample.
detectChangePoints

If TRUE, detectes genomic change points where potential kataegis are formed. Results are written to an output tab delimted file.
ref.build

Reference build for chromosome sizes. Can be hg18, hg19 or hg38. Default hg19.
color

named vector of colors for each coversion class.
savePlot

If TRUE plot is saved to output pdf. Default FALSE.
width

width of plot to be saved.
height

height of plot to be saved.
fontSize

Default 12.
pointSize

Default 0.8.

Details

If 'detectChangePoints“ is set to TRUE, this function will identify Kataegis loci. Kategis detection algorithm by Moritz Goretzky at WWU Munster, which exploits the definition of Kategis (six consecutive mutations with an avg. distance of 1000bp ) to idetify hyper mutated genomic loci. Algorithm starts with a double-ended queue to which six consecutive mutations are added and their average intermutation distance is calculated. If the average intermutation distance is larger than 1000, one element is added at the back of the queue and one is removed from the front. If the average intermutation distance is less or equal to 1000, further mutations are added until the average intermutation distance is larger than 1000. After that all mutations in the double-ended queue are written into output as one kataegis and the double-ended queue is reinitialized with six mutations.

Value

Results are written to an output file with suffix changePoints.tsv

Read MAF files.

Description

Takes tab delimited MAF (can be plain text or gz compressed) file as an input and summarizes it in various ways. Also creates oncomatrix - helpful for visualization.

Usage

read.maf(
  maf,
  clinicalData = NULL,
  rmFlags = FALSE,
  removeDuplicatedVariants = TRUE,
  useAll = TRUE,
  gisticAllLesionsFile = NULL,
  gisticAmpGenesFile = NULL,
  gisticDelGenesFile = NULL,
  gisticScoresFile = NULL,
  cnLevel = "all",
  cnTable = NULL,
  isTCGA = FALSE,
  vc_nonSyn = NULL,
  verbose = TRUE
)

Arguments

maf

tab delimited MAF file. File can also be gz compressed. Required. Alternatively, you can also provide already read MAF file as a dataframe.
clinicalData

Clinical data associated with each sample/Tumor_Sample_Barcode in MAF. Could be a text file or a data.frame. Default NULL.
rmFlags

Default FALSE. Can be TRUE or an integer. If TRUE removes all the top 20 FLAG genes. If integer, remove top n FLAG genes.
removeDuplicatedVariants

removes repeated variants in a particuar sample, mapped to multiple transcripts of same Gene. See Description. Default TRUE.
useAll

logical. Whether to use all variants irrespective of values in Mutation_Status. Defaults to TRUE. If FALSE, only uses with values Somatic.
gisticAllLesionsFile

All Lesions file generated by gistic. e.g; all_lesions.conf_XX.txt, where XX is the confidence level. Default NULL.
gisticAmpGenesFile

Amplification Genes file generated by gistic. e.g; amp_genes.conf_XX.txt, where XX is the confidence level. Default NULL.
gisticDelGenesFile

Deletion Genes file generated by gistic. e.g; del_genes.conf_XX.txt, where XX is the confidence level. Default NULL.
gisticScoresFile

scores.gistic file generated by gistic. Default NULL
cnLevel

level of CN changes to use. Can be 'all', 'deep' or 'shallow'. Default uses all i.e, genes with both 'shallow' or 'deep' CN changes
cnTable

Custom copynumber data if gistic results are not available. Input file or a data.frame should contain three columns in aforementioned order with gene name, Sample name and copy number status (either 'Amp' or 'Del'). Default NULL. Recommended to include additional columns 'Chromosome' 'Start_Position' 'End_Position'
isTCGA

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

NULL. Provide manual list of variant classifications to be considered as non-synonymous. Rest will be considered as silent variants. Default uses Variant Classifications with High/Moderate variant consequences. https://m.ensembl.org/info/genome/variation/prediction/predicted_data.html: "Frame_Shift_Del", "Frame_Shift_Ins", "Splice_Site", "Translation_Start_Site","Nonsense_Mutation", "Nonstop_Mutation", "In_Frame_Del","In_Frame_Ins", "Missense_Mutation"
verbose

TRUE logical. Default to be talkative and prints summary.

Details

This function takes MAF file as input and summarizes them. If copy number data is available, e.g from GISTIC, it can be provided too via arguments gisticAllLesionsFile, gisticAmpGenesFile, and gisticDelGenesFile. Copy number data can also be provided as a custom table containing Gene name, Sample name and Copy Number status.

Note that if input MAF file contains multiple affected transcripts of a variant, this function by default removes them as duplicates, while keeping single unique entry per variant per sample. If you wish to keep all of them, set removeDuplicatedVariants to FALSE.

FLAGS - If you get a note on possible FLAGS while reading MAF, it means some of the top mutated genes are fishy. These genes are often non-pathogenic and passengers, but are frequently mutated in most of the public exome studies. Examples of such genes include TTN, MUC16, etc. This note can be ignored without any harm, it's only generated as to make user aware of such genes. See references for details on FLAGS.

Value

An object of class MAF.

References

Shyr C, Tarailo-Graovac M, Gottlieb M, Lee JJ, van Karnebeek C, Wasserman WW. FLAGS, frequently mutated genes in public exomes. BMC Med Genomics 2014; 7: 64.

See Also

plotmafSummary write.mafSummary

Examples

laml.maf = system.file("extdata", "tcga_laml.maf.gz", package = "maftools") #MAF file
laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools') #clinical data
laml = read.maf(maf = laml.maf, clinicalData = laml.clin)

Read and summarize gistic output.

Description

A little function to summarize gistic output files. Summarized output is returned as a list of tables.

Usage

readGistic(
  gisticDir = NULL,
  gisticAllLesionsFile = NULL,
  gisticAmpGenesFile = NULL,
  gisticDelGenesFile = NULL,
  gisticScoresFile = NULL,
  cnLevel = "all",
  isTCGA = FALSE,
  verbose = TRUE
)

Arguments

gisticDir

Directory containing GISTIC results. Default NULL. If provided all relevent files will be imported. Alternatively, below arguments can be used to import required files.
gisticAllLesionsFile

All Lesions file generated by gistic. e.g; all_lesions.conf_XX.txt, where XX is the confidence level. Required. Default NULL.
gisticAmpGenesFile

Amplification Genes file generated by gistic. e.g; amp_genes.conf_XX.txt, where XX is the confidence level. Default NULL.
gisticDelGenesFile

Deletion Genes file generated by gistic. e.g; del_genes.conf_XX.txt, where XX is the confidence level. Default NULL.
gisticScoresFile

scores.gistic file generated by gistic.
cnLevel

level of CN changes to use. Can be 'all', 'deep' or 'shallow'. Default uses all i.e, genes with both 'shallow' or 'deep' CN changes
isTCGA

Is the data from TCGA. Default FALSE.
verbose

Default TRUE

Details

Requires output files generated from GISTIC. Gistic documentation can be found here ftp://ftp.broadinstitute.org/pub/GISTIC2.0/GISTICDocumentation_standalone.htm

Value

A list of summarized data.

Examples

all.lesions <- system.file("extdata", "all_lesions.conf_99.txt", package = "maftools")
amp.genes <- system.file("extdata", "amp_genes.conf_99.txt", package = "maftools")
del.genes <- system.file("extdata", "del_genes.conf_99.txt", package = "maftools")
scores.gistic <- system.file("extdata", "scores.gistic", package = "maftools")
laml.gistic = readGistic(gisticAllLesionsFile = all.lesions, gisticAmpGenesFile = amp.genes, gisticDelGenesFile = del.genes, gisticScoresFile = scores.gistic, isTCGA = TRUE)

Identify sample swaps and similarities

Description

Given a list BAM files, the function genotypes known SNPs and identifies potentially related samples. For the source of SNPs, see reference

Usage

sampleSwaps(
  bams = NULL,
  build = "hg19",
  prefix = NULL,
  add = TRUE,
  min_depth = 30,
  ncores = 4,
  ...
)

Arguments

bams

Input bam files. Required.
build

reference genome build. Default "hg19". Can be hg19 or hg38
prefix

Prefix to add or remove from contig names in SNP file. If BAM files are aligned GRCh37/38 genome, use prefix 'chr' to 'add'
add

If prefix is used, default is to add prefix to contig names in SNP file. If FALSE prefix will be removed from contig names.
min_depth

Minimum read depth for a SNP to be considered. Default 30.
ncores

Default 4. Each BAM file will be launched on a separate thread. Works only on Unix and macOS.
...

Additional arguments passed to bamreadcounts

Value

a list with results summarized

References

Westphal, M., Frankhouser, D., Sonzone, C. et al. SMaSH: Sample matching using SNPs in humans. BMC Genomics 20, 1001 (2019). https://doi.org/10.1186/s12864-019-6332-7

Segment and plot log ratio values with DNACopy

Description

The function takes logR file generated by prepAscat or prepAscat_t and performs segmentation with DNAcopy

Usage

segmentLogR(tumor_logR = NULL, sample_name = NULL, build = "hg19")

Arguments

tumor_logR

logR.txt file generated by prepAscat or prepAscat_t
sample_name

Default NULL. Parses from 'tumor_logR' file
build

Reference genome. Default hg19. Can be hg18, hg19, or hg38

Value

Invisibly returns DNAcopy object

See Also

gtMarkers prepAscat

Summarize CBS segmentation results

Description

Summarize CBS segmentation results

Usage

segSummarize(
  seg = NULL,
  build = "hg19",
  cytoband = NULL,
  thr = 0.3,
  verbose = TRUE,
  maf = NULL,
  genes = NULL,
  topanno = NULL,
  topannocols = NA
)

Arguments

seg

segmentation results generated from DNAcopy package segment. Input should be a multi-sample segmentation file or a data.frame. First six columns should correspond to sample name, chromosome, start, end, Num_Probes, Segment_Mean in log2 scale. (default output format from DNAcopy)
build

genome build. Default hg19. Can be hg19, hg38. If other than these, use 'cytoband' argument
cytoband

cytoband data from UCSC genome browser. Only needed if 'build' is other than 'hg19' or 'hg38'
thr

threshold to call amplification and deletion. Any cytobands or chromosomal arms with median logR above or below this will be called. Default 0.3
verbose

Default TRUE
maf

optional MAF
genes

Add mutation status of these genes as an annotation to the heatmap
topanno

annotation for each sample. This is passed as an input to 'annotation_col' of 'pheatmap'
topannocols

annotation cols for 'topanno'. This is passed as an input to 'annotation_colors' of 'pheatmap'

Details

A handy function to summarize CBS segmentation results. Takes segmentation results generated by DNAcopy package segment and summarizes the CN for each cytoband and chromosomal arms.

Value

List of median CN values for each cytoband and chromosomal arm along with the plotting matrix

Examples

laml.seg <- system.file("extdata", "LAML_CBS_segments.tsv.gz", package = "maftools")
segSummarize(seg = laml.seg)

#Heighlight some genes as annotation
laml.maf = system.file("extdata", "tcga_laml.maf.gz", package = "maftools") #MAF file
laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools') #clinical data
laml = read.maf(maf = laml.maf, clinicalData = laml.clin)

segSummarize(seg = laml.seg, maf = laml, genes = c("FLT3", "DNMT3A"))

Set Operations for MAF objects

Description

Set Operations for MAF objects

Usage

setdiffMAF(x, y, mafObj = TRUE, refAltMatch = TRUE, ...)

intersectMAF(x, y, refAltMatch = TRUE, mafObj = TRUE, ...)

Arguments

x

the first 'MAF' object.
y

the second 'MAF' object.
mafObj

Return output as an 'MAF' object. Default 'TRUE'
refAltMatch

Set operations are done by matching ref and alt alleles in addition to loci (Default). If FALSE only loci (chr, start, end positions) are matched.
...

other parameters passing to 'subsetMaf' for subsetting operations.

Value

subset table or an object of class MAF-class. If no overlaps found returns 'NULL'

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
x <- subsetMaf(maf = laml, tsb = c('TCGA-AB-3009'))
y <- subsetMaf(maf = laml, tsb = c('TCGA-AB-2933'))
setdiffMAF(x, y)
intersectMAF(x, y) #Should return NULL due to no common variants

Performs sample stratification based on signature contribution and enrichment analysis.

Description

Performs k-means clustering to assign signature to samples and performs enrichment analysis. Note - Do not use this function. This will be removed in future updates.

Usage

signatureEnrichment(maf, sig_res, minMut = 5, useCNV = FALSE, fn = NULL)

Arguments

maf

an MAF object used for signature analysis.
sig_res

Signature results from extractSignatures
minMut

Consider only genes with minimum this number of samples mutated. Default 5.
useCNV

whether to include copy number events. Only applicable when MAF is read along with copy number data. Default TRUE if available.
fn

basename for output file. Default NULL.

Value

result list containing p-values

See Also

plotEnrichmentResults

Exact tests to detect mutually exclusive, co-occuring and altered genesets.

Description

Performs Pair-wise Fisher's Exact test to detect mutually exclusive or co-occuring events.

Usage

somaticInteractions(
  maf,
  top = 25,
  genes = NULL,
  pvalue = c(0.05, 0.01),
  returnAll = TRUE,
  geneOrder = NULL,
  fontSize = 0.8,
  leftMar = 4,
  topMar = 4,
  showSigSymbols = TRUE,
  showCounts = FALSE,
  countStats = "all",
  countType = "all",
  countsFontSize = 0.8,
  countsFontColor = "black",
  colPal = "BrBG",
  revPal = FALSE,
  showSum = TRUE,
  plotPadj = FALSE,
  colNC = 9,
  nShiftSymbols = 5,
  sigSymbolsSize = 2,
  sigSymbolsFontSize = 0.9,
  pvSymbols = c(46, 42),
  limitColorBreaks = TRUE
)

Arguments

maf

an MAF object generated by read.maf
top

check for interactions among top 'n' number of genes. Defaults to top 25. genes
genes

List of genes among which interactions should be tested. If not provided, test will be performed between top 25 genes.
pvalue

Default c(0.05, 0.01) p-value threshold. You can provide two values for upper and lower threshold.
returnAll

If TRUE returns test statistics for all pair of tested genes. Default FALSE, returns for only genes below pvalue threshold.
geneOrder

Plot the results in given order. Default NULL.
fontSize

cex for gene names. Default 0.8
leftMar

Left margin. Default 4
topMar

Top margin. Default 4
showSigSymbols

Default TRUE. Heighlight significant pairs
showCounts

Default TRUE. Include number of events in the plot
countStats

Default 'all'. Can be 'all' or 'sig'
countType

Default 'cooccur'. Can be 'all', 'cooccur', 'mutexcl'
countsFontSize

Default 0.8
countsFontColor

Default 'black'
colPal

colPalBrewer palettes. See RColorBrewer::display.brewer.all() for details
revPal

Reverse the color palette. Default FALSE
showSum

show [sum] with gene names in plot, Default TRUE
plotPadj

Plot adj. p-values instead
colNC

Number of different colors in the palette, minimum 3, default 9
nShiftSymbols

shift if positive shift SigSymbols by n to the left, default = 5
sigSymbolsSize

size of symbols in the matrix and in legend
sigSymbolsFontSize

size of font in legends
pvSymbols

vector of pch numbers for symbols of p-value for upper and lower thresholds c(upper, lower)
limitColorBreaks

limit color to extreme values. Default TRUE

Details

This function and plotting is inspired from genetic interaction analysis performed in the published study combining gene expression and mutation data in MDS. See reference for details.

Value

list of data.tables

References

Gerstung M, Pellagatti A, Malcovati L, et al. Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nature Communications. 2015;6:5901. doi:10.1038/ncomms6901.

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
somaticInteractions(maf = laml, top = 5)

Subset MAF objects

Description

Subsets MAF based on given conditions.

Usage

subsetMaf(
  maf,
  tsb = NULL,
  genes = NULL,
  query = NULL,
  clinQuery = NULL,
  ranges = NULL,
  keepNA = FALSE,
  mult = "first",
  fields = NULL,
  mafObj = TRUE,
  includeSyn = TRUE,
  isTCGA = FALSE,
  dropLevels = TRUE,
  restrictTo = "all",
  verbose = TRUE
)

Arguments

maf

an MAF object generated by read.maf
tsb

subset by these samples (Tumor Sample Barcodes)
genes

subset by these genes
query

query string. e.g, "Variant_Classification == 'Missense_Mutation'" returns only Missense variants.
clinQuery

query by clinical variable.
ranges

subset by ranges. data.frame with 3 column (chr, start, end). Overlaps are identified by foverlaps function with arguments 'type = within', 'mult = all', 'nomatch = NULL'
keepNA

Keep NAs while sub-setting for ranges. Default 'FALSE' - removes rows with missing loci prior to overlapping. Set to TRUE to keep them as is.
mult

When multiple loci in 'ranges' match to the variants maf, mult=. controls which values are returned - "all" , "first" (default) or "last". This value is passed to 'mult' argument of foverlaps
fields

include only these fields along with necessary fields in the output
mafObj

returns output as MAF class MAF-class. Default TRUE
includeSyn

Default TRUE, only applicable when mafObj = FALSE. If mafObj = TRUE, synonymous variants will be stored in a seperate slot of MAF object.
isTCGA

Is input MAF file from TCGA source.
dropLevels

Default TRUE.
restrictTo

restrict subset operations to these. Can be 'all', 'cnv', or 'mutations'. Default 'all'. If 'cnv' or 'mutations', subset operations will only be applied on copy-number or mutation data respectively, while retaining other parts as is.
verbose

Default TRUE

Value

subset table or an object of class MAF-class

See Also

getFields

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
##Select all Splice_Site mutations from DNMT3A and NPM1
subsetMaf(maf = laml, genes = c('DNMT3A', 'NPM1'),
query = "Variant_Classification == 'Splice_Site'")
##Select all variants with VAF above 30%
subsetMaf(maf = laml, query = "i_TumorVAF_WU > 30")
##Extract data for samples 'TCGA.AB.3009' and 'TCGA.AB.2933' but only include vaf filed.
subsetMaf(maf = laml, tsb = c('TCGA-AB-3009', 'TCGA-AB-2933'), fields = 'i_TumorVAF_WU')
##Subset by ranges
ranges = data.frame(chr = c("2", "17"), start = c(25457000, 7571720), end = c(25458000, 7590868))
subsetMaf(laml, ranges = ranges)

Predict genesets associated with survival

Description

Predict genesets associated with survival

Usage

survGroup(
  maf,
  top = 20,
  genes = NULL,
  geneSetSize = 2,
  minSamples = 5,
  clinicalData = NULL,
  time = "Time",
  Status = "Status",
  verbose = TRUE,
  plot = FALSE
)

Arguments

maf

an MAF object generated by read.maf
top

If genes is NULL by default used top 20 genes
genes

Manual set of genes
geneSetSize

Default 2
minSamples

minimum number of samples to be mutated to be considered for analysis. Default 5
clinicalData

dataframe containing events and time to events. Default looks for clinical data in annotation slot of MAF.
time

column name contining time in clinicalData
Status

column name containing status of patients in clinicalData. must be logical or numeric. e.g, TRUE or FALSE, 1 or 0.
verbose

Default TRUE
plot

Default FALSE If TRUE, generate KM plots of the genesets combinations.

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml.clin <- system.file("extdata", "tcga_laml_annot.tsv", package = "maftools")
laml <- read.maf(maf = laml.maf,  clinicalData = laml.clin)
survGroup(maf = laml, top = 20, geneSetSize = 1, time = "days_to_last_followup", Status = "Overall_Survival_Status", plot = FALSE)

Prints available TCGA datasets

Description

Prints available TCGA cohorts

Usage

tcgaAvailable(repo = c("github", "gitee"))

Arguments

repo

can be "github" (default) or "gitee". If 'github' fails to fetch, switch to 'gitee'

See Also

tcgaLoad

Examples

tcgaAvailable()

Compare mutation load against TCGA cohorts

Description

Compares mutation load in input MAF against all of 33 TCGA cohorts derived from MC3 project.

Usage

tcgaCompare(
  maf,
  capture_size = NULL,
  tcga_capture_size = 35.8,
  cohortName = NULL,
  tcga_cohorts = NULL,
  primarySite = FALSE,
  col = c("gray70", "black"),
  bg_col = c("#EDF8B1", "#2C7FB8"),
  medianCol = "red",
  decreasing = FALSE,
  logscale = TRUE,
  rm_hyper = FALSE,
  rm_zero = TRUE,
  cohortFontSize = 0.8,
  axisFontSize = 0.8
)

Arguments

maf

MAF object(s) generated by read.maf
capture_size

capture size for input MAF in MBs. Default NULL. If provided plot will be scaled to mutations per mb. TCGA capture size is assumed to be 35.8 mb.
tcga_capture_size

capture size for TCGA cohort in MB. Default 35.8. Do NOT change. See details for more information.
cohortName

name for the input MAF cohort. Default "Input"
tcga_cohorts

restrict tcga data to these cohorts.
primarySite

If TRUE uses primary site of cancer as labels instead of TCGA project IDs. Default FALSE.
col

color vector for length 2 TCGA cohorts and input MAF cohort. Default gray70 and black.
bg_col

background color. Default'#EDF8B1', '#2C7FB8'
medianCol

color for median line. Default red.
decreasing

Default FALSE. Cohorts are arranged in increasing mutation burden.
logscale

Default TRUE
rm_hyper

Remove hyper mutated samples (outliers)? Default FALSE
rm_zero

Remove samples with zero mutations? Default TRUE
cohortFontSize

Default 0.8
axisFontSize

Default 0.8

Details

Tumor mutation burden for TCGA cohorts is obtained from TCGA MC3 study. For consistency TMB is estimated by restricting variants within Agilent Sureselect capture kit of size 35.8 MB.

Value

data.table with median mutations per cohort

Source

TCGA MC3 file was obtained from https://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc. See TCGAmutations R package for more details. Further downstream script to estimate TMB for each sample can be found in ‘inst/scripts/estimate_tcga_tmb.R’

References

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines Kyle Ellrott, Matthew H. Bailey, Gordon Saksena, et. al. Cell Syst. 2018 Mar 28; 6(3): 271–281.e7. https://doi.org/10.1016/j.cels.2018.03.002

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
tcgaCompare(maf = laml, cohortName = "AML")

Compare genes to known TCGA drivers and their biological pathways

Description

A small function which uses known cancer driver genes and their associatd pathways from TCGA cohorts. See reference for details

Usage

tcgaDriverBP(m, genes = NULL, top = 20, fontSize = 0.7)

Arguments

m

an MAF object
genes

genes to compare. Default 'NULL'.
top

Top number of genes to use. Mutually exclusive with 'genes' argument. Default 20
fontSize

Default 0.7

References

Bailey MH, Tokheim C, Porta-Pardo E, et al. Comprehensive Characterization of Cancer Driver Genes and Mutations . Cell. 2018;173(2):371–385.e18. doi:10.1016/j.cell.2018.02.060

Loads a TCGA cohort

Description

Loads the user mentioned TCGA cohorts

Usage

tcgaLoad(
  study = NULL,
  source = c("MC3", "Firehose"),
  repo = c("github", "gitee")
)

Arguments

study

Study names to load. Use tcgaAvailable to see available options.
source

Source for MAF files. Can be MC3 or Firehose. Default MC3. Argument may be abbreviated (M or F)
repo

one of "github" (default) and "gitee".

Details

The function loads curated and pre-compiled MAF objects from TCGA cohorts. TCGA data are obtained from two sources namely, Broad Firehose repository, and MC3 project.

Value

An object of class MAF.

References

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines Kyle Ellrott, Matthew H. Bailey, Gordon Saksena, et. al. Cell Syst. 2018 Mar 28; 6(3): 271–281.e7.

See Also

tcgaAvailable

Examples

# Loads TCGA LAML cohort (default from MC3 project)
tcgaLoad(study = "LAML")
# Loads TCGA LAML cohort (from Borad Firehose)
tcgaLoad(study = "LAML", source = "Firehose")

Classifies SNPs into transitions and transversions

Description

takes output generated by read.maf and classifies Single Nucleotide Variants into Transitions and Transversions.

Usage

titv(maf, useSyn = FALSE, plot = TRUE, file = NULL)

Arguments

maf

an MAF object generated by read.maf
useSyn

Logical. Whether to include synonymous variants in analysis. Defaults to FALSE.
plot

plots a titv fractions. default TRUE.
file

basename for output file name. If given writes summaries to output file. Default NULL.

Value

list of data.frames with Transitions and Transversions summary.

See Also

plotTiTv

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.titv = titv(maf = laml, useSyn = TRUE)

Estimate Tumor Mutation Burden

Description

Estimates Tumor Mutation Burden in terms of per megabases

Usage

tmb(
  maf,
  captureRegions = NULL,
  captureSize = 50,
  logScale = TRUE,
  ignoreCNV = TRUE,
  plotType = "classic",
  pointcol = "#2c3e50",
  verbose = TRUE
)

Arguments

maf

maf MAF object
captureRegions

capture regions. Default NULL. If provided sub-sets variants within the capture regions for TMB estimation. Can be a data.frame or a tsv with first three columns containing chromosome, start and end position.
captureSize

capture size for input MAF in MBs. Default 50MB. Mutually exclusive with captureRegions
logScale

Default TRUE. For plotting purpose only.
ignoreCNV

Default TRUE. Ignores all the variants annotated as 'CNV' in the 'Variant_Type' column of MAF
plotType

Can be "classic" or "boxplot". Set to 'NA' for no plot.
pointcol

Default #2c3e50
verbose

Default TRUE

Value

data.table with TMB for every sample

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
tmb(maf = laml)

Extract single 5' and 3' bases flanking the mutated site for de-novo signature analysis. Also estimates APOBEC enrichment scores.

Description

Extract single 5' and 3' bases flanking the mutated site for de-novo signature analysis. Also estimates APOBEC enrichment scores.

Usage

trinucleotideMatrix(
  maf,
  ref_genome = NULL,
  prefix = NULL,
  add = TRUE,
  ignoreChr = NULL,
  useSyn = TRUE,
  fn = NULL
)

Arguments

maf

an MAF object generated by read.maf
ref_genome

BSgenome object or name of the installed BSgenome package. Example: BSgenome.Hsapiens.UCSC.hg19 Default NULL, tries to auto-detect from installed genomes.
prefix

Prefix to add or remove from contig names in MAF file.
add

If prefix is used, default is to add prefix to contig names in MAF file. If false prefix will be removed from contig names.
ignoreChr

Chromsomes to ignore from analysis. e.g. chrM
useSyn

Logical. Whether to include synonymous variants in analysis. Defaults to TRUE
fn

If given writes APOBEC results to an output file with basename fn. Default NULL.

Details

Extracts immediate 5' and 3' bases flanking the mutated site and classifies them into 96 substitution classes. Requires BSgenome data packages for sequence extraction.

APOBEC Enrichment: Enrichment score is calculated using the same method described by Roberts et al.

E = (n_tcw * background_c) / (n_C * background_tcw)

where, n_tcw = number of mutations within T[C>T]W and T[C>G]W context. (W -> A or T)

n_C = number of mutated C and G

background_C and background_tcw motifs are number of C and TCW motifs occuring around +/- 20bp of each mutation.

One-sided Fisher's Exact test is performed to determine the enrichment of APOBEC tcw mutations over background.

Value

list of 2. A matrix of dimension nx96, where n is the number of samples in the MAF and a table describing APOBEC enrichment per sample.

References

Roberts SA, Lawrence MS, Klimczak LJ, et al. An APOBEC Cytidine Deaminase Mutagenesis Pattern is Widespread in Human Cancers. Nature genetics. 2013;45(9):970-976. doi:10.1038/ng.2702.

See Also

extractSignatures plotApobecDiff

Examples

## Not run: 
laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
laml.tnm <- trinucleotideMatrix(maf = laml, ref_genome = 'BSgenome.Hsapiens.UCSC.hg19',
prefix = 'chr', add = TRUE, useSyn = TRUE)

## End(Not run)

compare VAF across two cohorts

Description

Draw boxplot distibution of VAFs across two cohorts

Usage

vafCompare(
  m1,
  m2,
  genes = NULL,
  top = 5,
  vafCol1 = NULL,
  vafCol2 = NULL,
  m1Name = "M1",
  m2Name = "M2",
  cols = c("#2196F3", "#4CAF50"),
  sigvals = TRUE,
  nrows = NULL,
  ncols = NULL
)

Arguments

m1

first MAF object. Required.
m2

second MAF object. Required.
genes

specify genes for which plot has to be generated. Default NULL.
top

if genes is NULL plots top n number of genes. Defaults to 5.
vafCol1

manually specify column name for vafs in m1. Default looks for column 't_vaf'
vafCol2

manually specify column name for vafs in m2. Default looks for column 't_vaf'
m1Name

optional name for first cohort
m2Name

optional name for second cohort
cols

vector of colors corresponding to m1 and m2 respectivelly.
sigvals

Estimate and add significance stars. Default TRUE.
nrows

Number of rows in the layout. Default NULL - estimated automatically
ncols

Number of genes drawn per row. Default 4

Writes GISTIC summaries to output tab-delimited text files.

Description

Writes GISTIC summaries to output tab-delimited text files.

Usage

write.GisticSummary(gistic, basename = NULL)

Arguments

gistic

an object of class GISTIC generated by readGistic
basename

basename for output file to be written.

Value

None. Writes output as tab delimited text files.

See Also

readGistic

Examples

all.lesions <- system.file("extdata", "all_lesions.conf_99.txt", package = "maftools")
amp.genes <- system.file("extdata", "amp_genes.conf_99.txt", package = "maftools")
del.genes <- system.file("extdata", "del_genes.conf_99.txt", package = "maftools")
scores.gistic <- system.file("extdata", "scores.gistic", package = "maftools")
laml.gistic = readGistic(gisticAllLesionsFile = all.lesions, gisticAmpGenesFile = amp.genes, gisticDelGenesFile = del.genes, gisticScoresFile = scores.gistic)
write.GisticSummary(gistic = laml.gistic, basename = 'laml')

Writes maf summaries to output tab-delimited text files.

Description

Writes maf summaries to output tab-delimited text files.

Usage

write.mafSummary(maf, basename = NULL, compress = FALSE)

Arguments

maf

an MAF object generated by read.maf
basename

basename for output file to be written.
compress

If 'TRUE' files will be gz compressed. Default 'FALSE'

Details

Writes MAF and related summaries to output files.

Value

None. Writes output as text files.

See Also

read.maf

Examples

laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
laml <- read.maf(maf = laml.maf)
write.mafSummary(maf = laml, basename = 'laml')