sequenza (version 1.0.5)

sequenza: Use sequenza to estimate tumor purity and ploidy.

Description

The main interface of the package, to run several of the functions of sequenza in a standardized pipeline.

Usage

sequenza.extract(file, gz = TRUE, window = 1e6, overlap = 1, gamma = 80,
                   kmin = 10, mufreq.treshold = 0.10, min.reads = 40,
                   max.mut.types = 1, min.type.freq = 0.9)
  sequenza.fit(sequenza.extract, female = TRUE, segment.filter = 1e7,
               XY = c(X = "X", Y = "Y"), cellularity = seq(0.1,1,0.01),
               ploidy = seq(1, 7, 0.1), ratio.priority = FALSE,
               priors.table = data.frame(CN = 2, value = 2),
               chromosome.list = 1:24, mc.cores = getOption("mc.cores", 2L))
  sequenza.results(sequenza.extract, sequenza.fit = NULL, sample.id, out.dir = './',
                   cellularity = NULL, ploidy = NULL, female = TRUE, CNt.max = 20,
                   ratio.priority = FALSE, XY = c(X = "X", Y = "Y"),
                   chromosome.list = 1:24)

Arguments

file

an ABfreq file.

gz

logical. If TRUE (the default) the function expects a gzipped file.

window

size of windows used when plotting mean and quartile ranges of depth ratios and B-allele frequencies. Smaller windows will take more time to compute.

overlap

integer specifying the number of overlapping windows.

gamma, kmin

arguments passed to aspcf from the copynumber package.

mufreq.treshold

mutation frequency threshold.

min.reads

minimal number of reads above the quality threshold to accept the mutation call.

max.mut.types

maximal number of different base substitutions per position. Integer from 1 to 3 (since there are only 4 bases). Default is 3, to accept "noisy" mutation calls.

min.type.freq

minimal frequency of aberrant types.

sequenza.extract

a list of objects as output from the sequenza.extract function.

sequenza.fit

a list of objects as output from the sequenza.fit function.

female

logical, indicating whether the sample is male or female, to properly handle the X and Y chromosomes. Implementation only works for the human normal karyotype.

CNt.max

maximum copy number to consider in the model.

segment.filter

threshold segment length (in base pairs) to filter out short segments, that can cause noise when fitting the cellularity and ploidy parameters. The threshold will not affect the allele-specific segmentation.

XY

character vector of length 2 specifying the labels used for the X and Y chromosomes. Defaults to c(X = "X", Y = "Y").

cellularity

vector of values to test as cellularity parameter.

ploidy

vector of values to test as ploidy parameter.

priors.table

data frame with the columns CN and value, containing the copy numbers and the corresponding weights. To every copy number is assigned the value 1 as default, so every values different then 1 will change the corresponding weight.

ratio.priority

logical, if TRUE only the depth ratio will be used to determine the copy number state, while the Bf value will be used to determine the number of B-alleles.

chromosome.list

Vector containing the index or the names of the chromosome to include in the model fitting.

sample.id

identifier of the sample. It will be used as prefix of saved object.

out.dir

output directory where all the files and object will be stored.

mc.cores

number of cores to use, defined as in the parallel package.

Details

The function sequenza.extract utilizes a range of functions from the sequenza package to read the raw data, normalize the depth.ratio for GC-content bias, perform allele-specific segmentation, filter for noisy mutations and binning of the raw data for plotting. The computed objects are returned as a single list object. This object can be given to sequenza.fit, which uses baf.model.fit to calculate the log-likelihood for all pairs of the ploidy and cellularity parameters. The function sequenza.fit would save a number of object on a desired directory (default is the working directory). The object are the list of segments with resulting copy numbers and major and minor alleles; the candidate mutation list with variant allele frequency, and copy number and number of mutated allele, in relation of the clonal population (for sub-clonal population it needs to be processed with furthers methods); A plot of all the chromosomes in one image, representing the major and minor alleles and the absolute copy number changes (genome_view); multiple plots with one chromosome per image, representing copy-number, B-allele frequency and mutation in parallel (chromosome_view); results of the model fitting (CP_contours and confints); and a summary of the copy number state of the sample (CN_bars).

See Also

genome.view, baf.bayes, cp.plot, get.ci.

Examples

data.file <-  system.file("data", "abf.data.abfreq.txt.gz",
              package = "sequenza")
test <- sequenza.extract(data.file)
CP   <- sequenza.fit(test, mc.cores = 4)