callVariantsSingle {h5vc} | R Documentation

## Single sample variant calling

### Description

A simple single sample variant calling function (calling SNVs and deletions)

### Usage

    callVariantsSingle( data, sampledata, samples = sampledata$Sample, errorRate = 0.001, minSupport = 2, minAF = 0.05, minStrandSupport = 1, mergeDels = TRUE, aggregator = mean)

### Arguments

`data` | A `list` with elements `Counts` (a 4d `integer` array of size [1:12, 1:2, 1:k, 1:n]), `Coverage` (a 3d `integer` array of size [1:2, 1:k, 1:n]), `Deletions` (a 3d `integer` array of size [1:2, 1:k, 1:n]), `Reference` (a 1d `integer` vector of size [1:n]) – see Details.
`sampledata` | A `data.frame` with `k` rows (one for each sample) and columns `Column` and (`Sample`. The tally file should contain this information as a group attribute, see `getSampleData` for an example.
`samples` | The samples on which variants should be called, by default all samples specified in sampledata are used
`errorRate` | The expected error rate of the sequencing technology that was used, for illumina this should be `1/1000`
`minSupport` | minimal support required for a position to be considered variant
`minAF` | minimal allelic frequency for an allele at a position to be cosidered a variant
`minStrandSupport` | minimal per-strand support for a position to be considered variant
`mergeDels` | Boolean flag to specify that adjacent deletion calls should be merged
`aggregator` | Aggregator function for merging statistics of adjacent deletion calls, defaults to `mean`, which means that a deletion larger than 1bp will be annotated with the means of the counts and coverages etc.

### Details

`data` is a list of datasets which has to at least contain the `Counts` and `Coverages` for variant calling respectively `Deletions` for deletion calling (if `Deletions` is not present no deletion calls will be made). This list will usually be generated by a call to the `h5dapply` function in which the tally file, chromosome, datasets and regions within the datasets would be specified. See `h5dapply` for specifics.

`callVariantsSingle` implements a simple single sample variant callign approach for SNVs and deletions (if `Deletions` is a dataset present in the `data` parameter. The function applies three essential filters to the provided data, requiring:

- `minSupport` total support for the variant at the position - `minStrandSupport` support for the variant on each strand - an allele freqeuncy of at least `minAF` (for pure diploid samples this can be set relatively high, e.g. 0.3, for calling potentially homozygous variants a value of 0.8 or higher might be used)

Calls are annotated with the p-Value of a `binom.test` of the present support and coverage given the error rate provided in the `errorRate` parameter, no filtering is done on this annotation.

Adjacent deletion calls are merged based in the value of the `mergeDels` parameter and their statistics are aggregated with the function supplied in the `aggregator` parameter.

### Value

This function returns a `data.frame` containing annotated calls with the following slots:

`Chrom` | The chromosome the potential variant / deletion is on
`Start` | The starting position of the variant / deletion
`End` | The end position of the variant / deletions (equal to Start for SNVs and single basepair deletions)
`Sample` | The sample in which the variant was called
`altAllele` | The alternate allele for SNVs (deletions will have a `"-"` in that slot)
`refAllele` | The reference allele for SNVs (deletions will have the deleted sequence here as extracted from the `Reference` dataset, if the tally file contains a sparse representation of the reference, i.e. only positions with mismatches show a reference value the missing values are substituted with `"N"`'s. It is strongly suggested to write the whole reference into the tally file prior to deletion calling - see `writeReference` for details)
`SupFwd` | Support for the variant in the sample on the forward strand
`SupRev` | Support for the variant in the sample on the reverse strand
`CovFwd` | Coverage of the variant position in the sample on the forward strand
`CovRev` | Coverage of the variant position in the sample on the reverse strand
`AF_Fwd` | Allele frequency of the variant in the sample on the forward strand
`AF_Rev` | Allele frequency of the variant in the sample on the reverse strand
`Support` | Total Support of the variant - i.e. `SupFwd + SupRev`
`Coverage` | Total Coverage of the variant position - i.e. `CovFwd + CovRev`
`AF` | Total allele frequency of the variant, i.e. `Support / Coverage`
`fBackground` | Background frequency of the variant in all samples but the one the variant is called in
`pErrorFwd` | Probablity of the observed support and coverage given the error rate on the forward strand
`pErrorRev` | Probablity of the observed support and coverage given the error rate on the reverse strand
`pError` | Probablity of the observed support and coverage given the error rate on both strands combined
`pError` | Coverage of the variant position in the `Control` sample on the forward strand
`pStrand` | p-Value of a `fisher.test` on the contingency matrix `matrix(c(CovFwd,CovRev,SupFwd,SupRev), nrow = 2)` at this position - low values could indicate strand bias

### Author(s)

Paul Pyl

### Examples

      library(h5vc) # loading library
      tallyFile <- system.file( "extdata", "example.tally.hfs5", package = "h5vcData" )
      sampleData <- getSampleData( tallyFile, "/ExampleStudy/16" )
      position <- 29979629
      windowsize <- 1000
      vars <- h5dapply( # Calling Variants
        filename = tallyFile,
        group = "/ExampleStudy/16",
        blocksize = 500,
        FUN = callVariantsSingle,
        sampledata = sampleData,
        names = c("Coverages", "Counts", "Reference", "Deletions"),
        range = c(position - windowsize, position + windowsize)
      )
      vars <- do.call( rbind, vars ) # merge the results from all blocks by row
      vars # We did find a variant

[Package h5vc version 2.20.0 Index]