runAbsoluteCN: Run PureCN implementation of ABSOLUTE

In PureCN: Copy number calling and SNV classification using targeted short read sequencing

Description

This function takes as input tumor and normal control coverage data and a VCF containing allelic fractions of germline variants and somatic mutations. Normal control does not need to be from the same patient. In case VCF does not contain somatic status, it should contain dbSNP and optionally COSMIC annotation. Returns purity and ploidy combinations, sorted by likelihood score. Provides copy number and LOH data, by both gene and genomic region.

Usage

runAbsoluteCN(
  normal.coverage.file = NULL,
  tumor.coverage.file = NULL,
  log.ratio = NULL,
  seg.file = NULL,
  seg.file.sdev = 0.4,
  vcf.file = NULL,
  normalDB = NULL,
  genome,
  centromeres = NULL,
  sex = c("?", "F", "M", "diploid"),
  fun.filterVcf = filterVcfMuTect,
  args.filterVcf = list(),
  fun.setPriorVcf = setPriorVcf,
  args.setPriorVcf = list(),
  fun.setMappingBiasVcf = setMappingBiasVcf,
  args.setMappingBiasVcf = list(),
  fun.filterIntervals = filterIntervals,
  args.filterIntervals = list(),
  fun.segmentation = segmentationCBS,
  args.segmentation = list(),
  fun.focal = findFocal,
  args.focal = list(),
  sampleid = NULL,
  min.ploidy = 1.4,
  max.ploidy = 6,
  test.num.copy = 0:7,
  test.purity = seq(0.15, 0.95, by = 0.01),
  prior.purity = NULL,
  prior.K = 0.999,
  prior.contamination = 0.01,
  max.candidate.solutions = 20,
  candidates = NULL,
  min.coverage = 15,
  max.coverage.vcf = 300,
  max.non.clonal = 0.2,
  max.homozygous.loss = c(0.05, 1e+07),
  non.clonal.M = 1/3,
  max.mapping.bias = 0.8,
  max.pon = 3,
  iterations = 30,
  min.variants.segment = 5,
  log.ratio.calibration = 0.1,
  smooth.log.ratio = TRUE,
  model.homozygous = FALSE,
  error = 0.001,
  interval.file = NULL,
  max.dropout = c(0.95, 1.1),
  min.logr.sdev = 0.15,
  max.logr.sdev = 0.6,
  max.segments = 300,
  min.gof = 0.8,
  plot.cnv = TRUE,
  vcf.field.prefix = "",
  cosmic.vcf.file = NULL,
  DB.info.flag = "DB",
  POPAF.info.field = "POP_AF",
  min.pop.af = 0.001,
  model = c("beta", "betabin"),
  post.optimize = FALSE,
  speedup.heuristics = 2,
  BPPARAM = NULL,
  log.file = NULL,
  verbose = TRUE
)

Arguments

normal.coverage.file	Coverage file of normal control (optional if log.ratio is provided - then it will be only used to filter low coverage exons). Should be already GC-normalized with correctCoverageBias. Needs to be either a file name or data read with the readCoverageFile function.
tumor.coverage.file	Coverage file of tumor. If NULL, requires seg.file and an interval file via interval.file. Should be already GC-normalized with correctCoverageBias. Needs to be either a file name or data read with the readCoverageFile function.
log.ratio	Copy number log-ratios for all exons in the coverage files. If NULL, calculated based on coverage files.
seg.file	Segmented data. Optional, to support third-pary segmentation tools. If NULL, use coverage files or log.ratio to segment the data.
seg.file.sdev	If seg.file provided, the log-ratio standard deviation, used to model likelihood of sub-clonal copy number events.
vcf.file	VCF file. Optional, but typically needed to select between local optima of similar likelihood. Can also be a CollapsedVCF, read with the readVcf function. Requires a DB info flag for dbSNP membership. The default fun.setPriorVcf function will also look for a Cosmic.CNT slot (see cosmic.vcf.file), containing the hits in the COSMIC database. Again, do not expect very useful results without a VCF file.
normalDB	Normal database, created with createNormalDatabase. If provided, used to calculate gene-level p-values (requires Gene column in interval.file) and to filter targets with low coverage in the pool of normal samples.
genome	Genome version, for example hg19. See readVcf.
centromeres	A GRanges object with centromere positions. If NULL, use pre-stored positions for genome versions hg18, hg19 and hg38.
sex	Sex of sample. If ?, detect using getSexFromCoverage function and default parameters. Default parameters might not work well with every assay and might need to be tuned. If set to diploid, then PureCN will assume all chromosomes are diploid and will not try to detect sex.
fun.filterVcf	Function for filtering variants. Expected output is a list with elements vcf (CollapsedVCF), flag (logical(1)) and flag_comment (character(1)). The flags will be added to the output data and can be used to warn users, for example when samples look too noisy. Default filter will remove variants flagged by MuTect, but will keep germline variants. If ran in matched normal mode, it will by default use somatic status of variants and filter non-somatic calls with allelic fraction significantly different from 0.5 in normal. Defaults to filterVcfMuTect, which in turn also calls filterVcfBasic.
args.filterVcf	Arguments for variant filtering function. Arguments vcf, tumor.id.in.vcf, min.coverage, model.homozygous and error are required in the filter function and are automatically set.
fun.setPriorVcf	Function to set prior for somatic status for each variant in the VCF. Defaults to setPriorVcf.
args.setPriorVcf	Arguments for somatic prior function.
fun.setMappingBiasVcf	Function to set mapping bias for each variant in the VCF. Defaults to setMappingBiasVcf.
args.setMappingBiasVcf	Arguments for mapping bias function.
fun.filterIntervals	Function for filtering low-quality intervals in the coverage files. Needs to return a logical vector whether an interval should be used for segmentation. Defaults to filterIntervals.
args.filterIntervals	Arguments for target filtering function. Arguments normal, tumor, log.ratio, min.coverageseg.file and normalDB are required and automatically set.
fun.segmentation	Function for segmenting the copy number log-ratios. Expected return value is a data.frame representation of the segmentation. Defaults to segmentationCBS.
args.segmentation	Arguments for segmentation function. Arguments normal, tumor, log.ratio, plot.cnv, sampleid, vcf, tumor.id.in.vcf, centromeres are required in the segmentation function and automatically set.
fun.focal	Function for identifying focal amplifications. Defaults to findFocal.
args.focal	Arguments for focal amplification function.
sampleid	Sample id, provided in output files etc.
min.ploidy	Minimum ploidy to be considered.
max.ploidy	Maximum ploidy to be considered.
test.num.copy	Copy numbers tested in the grid search. Note that focal amplifications can have much higher copy numbers, but they will be labeled as subclonal (because they do not fit the integer copy numbers).
test.purity	Considered tumor purity values.
prior.purity	numeric(length(test.purity)) with priors for tested purity values. If NULL, use flat priors.
prior.K	This defines the prior probability that the multiplicity of a SNV corresponds to either the maternal or the paternal copy number (for somatic variants additionally to a multiplicity of 1). For perfect segmentations, this value would be 1; values smaller than 1 thus may provide some robustness against segmentation errors.
prior.contamination	The prior probability that a known SNP is from a different individual.
max.candidate.solutions	Number of local optima considered in optimization and variant fitting steps. If there are too many local optima, it will use specified number of top candidate solutions, but will also include all optima close to diploid, because silent genomes have often lots of local optima.
candidates	Candidates to optimize from a previous run (return.object$candidates). If NULL, do 2D grid search and find local optima.
min.coverage	Minimum coverage in both normal and tumor. Intervals and variants with lower coverage are ignored. This value is provided to the args.filterIntervals and args.filterVcf lists, but can be overwritten in these lists if different cutoffs for the coverage and variant filters are wanted. To increase the sensitivity of homozygous deletions in high purity samples, the coverage cutoff in tumor is automatically lowered by 50 percent if the normal coverage is high.
max.coverage.vcf	This will set the maximum number of reads in the SNV fitting. This is to avoid that small non-reference biases that come apparent only at high coverages have a dramatic influence on likelihood scores. Only relevant for model = "beta".
max.non.clonal	Maximum genomic fraction assigned to a subclonal copy number state.
max.homozygous.loss	double(2) with maximum chromosome fraction assigned to homozygous loss and maximum size of a homozygous loss segment.
non.clonal.M	Average expected cellular fraction of sub-clonal somatic mutations. This is to calculate expected allelic fractions of a single sub-clonal bin for variants. For all somatic variants, more accurate cellular fractions are calculated.
max.mapping.bias	Exclude variants with high mapping bias from the likelihood score calculation. Note that bias is reported on an inverse scale; a variant with mapping bias of 1 has no bias.
max.pon	Exclude variants found more than max.pon times in pool of normals and not in dbSNP. Requires mapping.bias.file in setMappingBiasVcf. Should be set to a value high enough to be much more likely an artifact and not a true germline variant not present in dbSNP.
iterations	Maximum number of iterations in the Simulated Annealing copy number fit optimization. Note that this an integer optimization problem that should converge quickly. Allowed range is 10 to 250.
min.variants.segment	Flag segments with fewer variants. The minor copy number estimation is not reliable with insufficient variants.
log.ratio.calibration	Re-calibrate log-ratios in the window purity*log.ratio.calibration.
smooth.log.ratio	Smooth log.ratio using the DNAcopy package.
model.homozygous	Homozygous germline SNPs are uninformative and by default removed. In 100 percent pure samples such as cell lines, however, heterozygous germline SNPs appear homozygous in case of LOH. Setting this parameter to TRUE will keep homozygous SNPs and include a homozygous SNP state in the likelihood model. Not necessary when matched normal samples are available.
error	Estimated sequencing error rate. Used to calculate minimum number of supporting reads for variants using calculatePowerDetectSomatic. Also used to calculate the probability of homozygous SNP allelic fractions (assuming reference reads are sequencing errors).
interval.file	A mapping file that assigns GC content and gene symbols to each exon in the coverage files. Used for generating gene-level calls. First column in format CHR:START-END. Second column GC content (0 to 1). Third column gene symbol. This file is generated with the preprocessIntervals function.
max.dropout	Measures GC bias as ratio of coverage in AT-rich (GC < 0.5) versus GC-rich on-target regions (GC >= 0.5). High drop-out might indicate that data was not GC-normalized or that the sample quality might be insufficient. Requires interval.file.
min.logr.sdev	Minimum log-ratio standard deviation used in the model. Useful to make fitting more robust to outliers in very clean data.
max.logr.sdev	Flag noisy samples with segment log-ratio standard deviation larger than this. Assay specific and needs to be calibrated.
max.segments	Flag noisy samples with a large number of segments. Assay specific and needs to be calibrated.
min.gof	Flag purity/ploidy solutions with poor fit.
plot.cnv	Generate segmentation plots.
vcf.field.prefix	Prefix all newly created VCF field names with this string.
cosmic.vcf.file	Add a Cosmic.CNT info field to the provided vcf.file using a VCF file containing the COSMIC database. The default fun.setPriorVcf function will give variants found in the COSMIC database a higher prior probability of being somatic. Not used in likelhood model when matched normal is available in vcf.file. Should be compressed and indexed with bgzip and tabix, respectively.
DB.info.flag	Flag in INFO of VCF that marks presence in common germline databases. Defaults to DB that may contain somatic variants if it is from an unfiltered dbSNP VCF.
POPAF.info.field	As alternative to a flag, use an info field that contains population allele frequencies. The DB info flag has priority over this field when both exist.
min.pop.af	Minimum population allele frequency in POPAF.info.field to set a high germline prior probability.
model	Use either a beta or a beta-binomial distribution for fitting observed to expected allelic fractions of alterations in vcf.file. The latter can be useful to account for significant overdispersion, for example due to mapping biases when no pool of normals is available or due to other unmodeled biases, e.g. amplification biases. The beta-binomial model is only recommended with a sufficiently sized pool of normal samples (more than 10 normals)
post.optimize	Optimize purity using final SCNA-fit and variants. This might take a long time when lots of variants need to be fitted, but will typically result in a slightly more accurate purity, especially for rather silent genomes or very low purities. Otherwise, it will just use the purity determined via the SCNA-fit.
speedup.heuristics	Tries to avoid spending computation time on local optima that are unlikely correct. Set to 0 to turn this off, to 1 to only apply heuristics that in worst case will decrease accuracy slightly or to 2 to turn on all heuristics.
BPPARAM	BiocParallelParam object. If NULL, does not use parallelization for fitting local optima.
log.file	If not NULL, store verbose output to file.
verbose	Verbose output.

Value

A list with elements

candidates	Results of the grid search.
results	All local optima, sorted by final rank.
input	The input data.

Author(s)

Markus Riester

References

Riester et al. (2016). PureCN: Copy number calling and SNV classification using targeted short read sequencing. Source Code for Biology and Medicine, 11, pp. 13.

Carter et al. (2012), Absolute quantification of somatic DNA alterations in human cancer. Nature Biotechnology.

See Also

correctCoverageBias segmentationCBS calculatePowerDetectSomatic

Examples

normal.coverage.file <- system.file('extdata', 'example_normal_tiny.txt', 
    package='PureCN')
tumor.coverage.file <- system.file('extdata', 'example_tumor_tiny.txt', 
    package='PureCN')
vcf.file <- system.file('extdata', 'example.vcf.gz', 
    package='PureCN')
interval.file <- system.file('extdata', 'example_intervals_tiny.txt', 
    package='PureCN')

# The max.candidate.solutions, max.ploidy and test.purity parameters are set to
# non-default values to speed-up this example.  This is not a good idea for real
# samples.
ret <-runAbsoluteCN(normal.coverage.file=normal.coverage.file, 
    tumor.coverage.file=tumor.coverage.file, genome='hg19', vcf.file=vcf.file,
    sampleid='Sample1', interval.file=interval.file,
    max.ploidy=4, test.purity=seq(0.3,0.7,by=0.05), max.candidate.solutions=1)


# If a high-quality segmentation was obtained with third-party tools:
seg.file <- system.file('extdata', 'example_seg.txt', 
    package = 'PureCN')

# By default, PureCN will re-segment the data, for example to identify
# regions of copy number neutral LOH. If this is not wanted, we can provide
# a minimal segmentation function which just returns the provided one:
funSeg <- function(seg, ...) return(seg)

res <- runAbsoluteCN(seg.file=seg.file, fun.segmentation=funSeg, max.ploidy = 4,
    test.purity = seq(0.3, 0.7, by = 0.05), max.candidate.solutions=1,
    genome='hg19', interval.file=interval.file)

PureCN documentation built on Nov. 8, 2020, 5:37 p.m.