rCNV (version 1.3.0)

cpm.normal: Calculate normalized depth for alleles

Description

This function outputs the normalized depth values separately for each allele, calculated using normalization factor with trimmed mean of M-values of sample libraries, median ratios normalization or quantile normalization, See details.

Usage

cpm.normal(
  het.table,
  method = c("MedR", "QN", "pca", "TMM", "TMMex"),
  logratioTrim = 0.3,
  sumTrim = 0.05,
  Weighting = TRUE,
  Acutoff = -1e+10,
  verbose = TRUE,
  plot = TRUE
)

Value

Returns a list with (AD), a data frame of normalized depth values similar to the output of hetTgen function and (outliers) a list of outlier sample names

Arguments

het.table

allele depth table generated from the function hetTgen

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 (only for TMM(ex))

verbose

logical. show progress

plot

logical. Plot the boxplot of sample library sizes showing outliers

Author

Piyal Karunarathne, Qiujie Zhou

Details

This function converts an observed depth value table to an effective depth value table using several normalization methods;

1. TMM normalization (See the original publication for more information). It is different from the function normz only in calculation of the counts per million is for separate alleles instead of the total depth. The TMMex method is an extension of the TMM method for large data sets containing SNPs exceeding 10000

2. The method MedR is median ratio normalization;

3. QN - quantile normalization (see Maza, Elie, et al. 2013 for a comparison of methods).

4. PCA - a modified Kaiser's Rule applied to depth values: Sample variation of eigen values smaller than 0.7 are removed (i.e., the first eigen value < 0.7) to eliminate the effect of the library size of samples

References

• Robinson MD, 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

• Maza, Elie, et al. "Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments: a matter of relative size of studied transcriptomes." Communicative & integrative biology 6.6 (2013): e25849

Examples

if (FALSE) data(ADtable)
ADnormalized<-cpm.normal(ADtable)