sciClone: sciClone: Identifies sub-clones within a sequenced sample

In genome/sciclone: Detect subclones from genomic sequence data

sciClone: Identifies sub-clones within a sequenced sample

Description

sciClone integrates the read depth and copy number information at single nucleotide variant locations and clusters the variants in copy neutral regions, to formalize description of the sub-clonal architecture of the sample.

Usage

    sciClone(vafs, copyNumberCalls=NULL, regionsToExclude=NULL,
             sampleNames, minimumDepth=100, clusterMethod="bmm",
             clusterParams="no.apply.overlapping.std.dev.condition",
             cnCallsAreLog2=FALSE, useSexChrs=TRUE, doClustering=TRUE,
             verbose=TRUE, 
             copyNumberMargins=0.25, maximumClusters=10,
             annotation=NULL, doClusteringAlongMargins=TRUE,
             plotIntermediateResults=0)

Arguments

vafs	a list of dataframes containing variant allele fraction data for single nucleotide variants in 5-column format: 1. chromosome 2. position 3. reference-supporting read counts 4. variant-supporting read counts 5. variant allele fraction (between 0-100)
copyNumberCalls	list of dataframes containing copy number segments in 4-column format: 1. chromosome 2. segment start position 3. segment stop position 4. copy number value for that segment. Unrepresented regions are assumed to have a copy number of 2.
regionsToExclude	Exclusion regions in 3-column format: 1. chromosome 2. window start position 3. window stop position; Single nucleotide variants falling into these windows will not be included in the analysis. Use this input for LOH regions, for example.
sampleNames	vector of names describing each sample ex: ("Primary Tumor", "Relapse")
minimumDepth	threshold used for excluding low-depth variants
maximumClusters	max number of clusters to consider when choosing the component fit to the data.
annotation	a list of positions in 3-column format 1) chromosome 2) position 3) gene name. These will be used to annotate the cluster table, if output.

cnCallsAreLog2	boolean argument specifying whether or not the copy number predictions are in log2 format (as opposed to being absolute copy number designations)
useSexChrs	boolean argument to specify preference of whether (TRUE) or not (FALSE) to use variants on sex chromosomes in the clustering steps of the tool.
doClustering	boolean argument - if (TRUE), the tool will attempt to use clustering to identify subclones. If (FALSE) this stage is skipped, and an object suitable for feeding into the plotting functions is produced.
clusterMethod	Use a different distribution for clustering. Currently available options are 'bmm' for beta, 'gaussian.bmm' for gaussian, and 'binomial.bmm' for binomial.
clusterParams	The framework is in place to drop in different clustering methods and provide them with additional parameters, but none of the currently available methods take any params - this should stay NULL.
verbose	if TRUE, prints lots of output to the screen that might be useful for debugging.
copyNumberMargins	In order to identify cleanly copy-number neutral regions, sciClone only considers sites with a copy number of 2.0 +/- this value. For example, if set to 0.25, regions at 2.20 will be considered cn-neutral, and regions at, 2.30 will not.
doClusteringAlongMargins	Perform 1d clustering of each sample to facilitate certain certain types of plotting (via sc.plot2dWithMargins())
plotIntermediateResults	output plots from intermediate steps of clustering (allows for vizualization of cluster convergence. Generally not useful, unless you're debugging the clustering code.

Value

returns a sciClone object containing merged vafs, clusters, and other information needed for visualization

Examples

  #sc = sciClone(vafs=list(v1,v2), copyNumberCalls=list(cn1,cn2), sampleNames=c("Tumor1","tumor2"))

genome/sciclone documentation built on Oct. 15, 2023, 5:09 a.m.