norm.fact {rCNV}

Calculate normalization factor for each sample

Description

This function calculates the normalization factor for each sample using different methods. See details.

Usage

norm.fact(
  df,
  method = c("TMM", "TMMex", "MedR", "QN"),
  logratioTrim = 0.3,
  sumTrim = 0.05,
  Weighting = TRUE,
  Acutoff = -1e+10
)

Arguments

df

a data frame or matrix of allele depth values (total depth per snp per sample)

method

character. method to be used (see details). Default TMM

logratioTrim

numeric. percentage value (0 - 1) of variation to be trimmed in log transformation

sumTrim

numeric. amount of trim to use on the combined absolute levels (“A” values) for method TMM

Weighting

logical, whether to compute (asymptotic binomial precision) weights

Acutoff

numeric, cutoff on “A” values to use before trimming

Details

Originally described for normalization of RNA sequences (Robinson & Oshlack 2010), this function computes normalization (scaling) factors to convert observed library sizes into effective library sizes. It uses the method trimmed means of M-values proposed by Robinson & Oshlack (2010). See the original publication and edgeR package for more information. The method MedR is median ratio normalization; QN - quantile normalization (see Maza, Elie, et al. 2013 for a comparison of methods).

Value

Returns a numerical vector of normalization factors for each sample

Author(s)

Piyal Karunarathne

References

• Robinson MD, and Oshlack A (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 11, R25

• Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26

Examples

vcf.file.path <- paste0(path.package("rCNV"), "/example.raw.vcf.gz")
vcf <- readVCF(vcf.file.path)
df<-hetTgen(vcf,"AD-tot",verbose=FALSE)
norm.fact(df)