runVarbin: Read BAM file and perform varbin analysis

In navinlabcode/copykit: CopyKit

Read BAM file and perform varbin analysis

Description

runVarbin performs the variable binning (VarBin) algorithm to a series of BAM files resulting from short-read sequencing.

Usage

runVarbin(
  dir,
  genome = c("hg38", "hg19"),
  resolution = c("220kb", "55kb", "110kb", "195kb", "280kb", "500kb", "1Mb", "2.8Mb"),
  remove_Y = FALSE,
  is_paired_end = FALSE,
  method = c("CBS", "multipcf"),
  vst = c("ft", "log"),
  seed = 17,
  min_bincount = 10,
  alpha = 1e-05,
  merge_levels_alpha = 1e-05,
  gamma = 40,
  name = "segment_ratios",
  BPPARAM = bpparam()
)

Arguments

dir	A path containing .BAM files from short-read sequencing.
genome	A character indicating the choice of genome assembly.
resolution	A character indicating the resolution for the scaffold of the VarBin method, i. e. the bin resulting bin size.
remove_Y	A boolean when set to TRUE, removes information from the chrY from the dataset.
is_paired_end	A boolean indicating if bam files are from single-read or pair end sequencing.
method	A character indicating the segmentation method.
vst	A character indicating the variance stabilization transformation to be performed. See runVst details.
seed	A numeric scalar that sets the seed for CBS segmentation permutation reproducibility.
min_bincount	A numerical indicating the minimum mean bin counts a cell should have to remain in the dataset.
alpha	A numeric with the. significance levels for the test to accept change-points for CBS segmentation. See segment.
merge_levels_alpha	A numeric with the significance levels for the merge levels test to accept two different segments.
gamma	A numeric passed on to 'multipcf' segmentation. Penalty for each discontinuity in the curve, default is 40. See multipcf.
name	A character with the name for the slot returned by runVarbin
BPPARAM	A BiocParallelParam specifying how the function should be parallelized.

Details

runVarbin is a convenient wrapper for CopyKit's pre-processing module. It runs runCountReads, runVst and, runSegmentation.

runCountReads takes as input duplicate marked BAM files from whole genome sequencing and runs the variable binning pipeline algorithm. Briefly, the genome is split into pre-determined bins. The bin size is controlled by the argument bin_size. By using VarBin, for a diploid cell, each bin will receive equal amount of reads, controlling for mappability. A lowess function is applied to perform GC correction across the bins. The argument genome can be set to 'hg38' or 'hg19' to select the scaffolds genome assembly. Information regarding the alignment of the reads to the bins and from the bam files are stored in the #' colData.

runVst performs variance stabilization to reduce the overdispersion from the negative binomial distribution nature of the bin counts and reduce technical bias. The argument vst controls the choice of the transformation allowing either the Freeman-Tukey transformation by using the option 'ft' (recommended) or a logarithmic transformation with the option 'log'. Using a 'log' transformation may result in long segmentation times for a few cells with large breakpoint counts.

runSegmentation Fits a piece-wise constant function to the transformed the smoothed bin counts. Bin counts are smoothed with smooth.CNA. Segmentation can be chosen to one of the following:

• CBS: runSegmentation Fits a piece-wise constant function to the transformed the smoothed bin counts. Bin counts are smoothed with smooth.CNA using the Circular Binary Segmentation (CBS) algorithm from segment with default it applies undo.prune with value of 0.05.

• multipcf: Performs the joint segmentation from the copynumber package using the multipcf function. By fitting piecewise constant curves with common breakpoints for all samples.

The resulting segment means are further refined with MergeLevels to join adjacent segments with non-significant differences in segmented means.

Value

An scCNA object containing the bin counts, the ratios and the segment ratios.

Author(s)

Darlan Conterno Minussi

References

Navin, N., Kendall, J., Troge, J. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011). https://doi.org/10.1038/nature09807

Baslan, T., Kendall, J., Ward, B., et al (2015). Optimizing sparse sequencing of single cells for highly multiplex copy number profiling. Genome research, 25(5), 714–724. https://doi.org/10.1101/gr.188060.114

Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics. 2004 Oct;5(4):557-72. doi: 10.1093/biostatistics/kxh008. PMID: 15475419.

Freeman, M. F.; Tukey, J. W. (1950), "Transformations related to the angular and the square root", The Annals of Mathematical Statistics, 21 (4), pp. 607–611, doi:10.1214/aoms/1177729756, JSTOR 2236611

Fryzlewicz, P. (2014). WILD BINARY SEGMENTATION FOR MULTIPLE CHANGE-POINT DETECTION. The Annals of Statistics, 42(6), 2243-2281. Retrieved July 30, 2021, from http://www.jstor.org/stable/43556493

Nilsen G, Liestol K, Van Loo P, Vollan H, Eide M, Rueda O, Chin S, Russell R, Baumbusch L, Caldas C, Borresen-Dale A, Lingjaerde O (2012). “Copynumber: Efficient algorithms for single- and multi-track copy number segmentation.” BMC Genomics, 13(1), 591.

Examples

## Not run: 
copykit_obj <- runVarbin("~/path/to/bam/files/", remove_Y = TRUE)

## End(Not run)

navinlabcode/copykit documentation built on Oct. 16, 2024, 2:55 p.m.