calculate_CNA: Estimate copy number alterations in cytobands

In vallotlab/ChromSCape: Analysis of single-cell epigenomics datasets with a Shiny App

Estimate copy number alterations in cytobands

Description

Cytobands are considered large enough in order that a variation at the cytoband level is not considered as an epigenetic event but as a genetic event, e.g. Copy Number Alterations. The function successively :

• Calculates the fraction of reads in each cytoband (FrCyto). See calculate_cyto_mat

• Calculates the log2-ratio FrCyto of each cell by the average FrCyto in normal cells. See calculate_logRatio_CNA

• Estimates if there was a gain or a loss of copy in each cyto band. See calculate_gain_or_loss

The corresponding matrices are accessibles in the reducedDim slots "cytoBands", "logRatio_cytoBands" and "gainOrLoss_cytoBands" respectively.

Usage

calculate_CNA(
  scExp,
  control_samples = unique(scExp$sample_id)[1],
  ref_genome = c("hg38", "mm10", "ce11")[1],
  quantiles_to_define_gol = c(0.05, 0.95)
)

Arguments

scExp	A SingleCellExperiment with "logRatio_cytoBand" reducedDim slot filled. See calculate_logRatio_CNA
control_samples	Sample IDs of the normal sample to take as reference.
ref_genome	Reference genome ('hg38' or 'mm10')
quantiles_to_define_gol	Quantiles of normal log2-ratio distribution below/above which cytoband is considered to be a loss/gain. (c(0.05,0.95)). See calculate_gain_or_loss

Value

The SCE with the fraction of reads, log2-ratio and gain or loss in each cytobands in each cells (of dimension cell x cytoband) in the reducedDim slots.

Examples

data("scExp")
scExp = calculate_CNA(scExp,  control_samples = unique(scExp$sample_id)[1],
ref_genome="hg38", quantiles_to_define_gol = c(0.05,0.95))
SingleCellExperiment::reducedDim(scExp, "cytoBand")
SingleCellExperiment::reducedDim(scExp, "logRatio_cytoBand")
SingleCellExperiment::reducedDim(scExp, "gainOrLoss_cytoBand")

vallotlab/ChromSCape documentation built on Oct. 15, 2023, 1:47 p.m.