apmsWAPP (version 1.0)

norm.inttable: Normalization of spectral count data

Description

Normalization of spectral counts in bait and control samples based on an AP-MS experiment.

Usage

norm.inttable(inttab.mat, baittab,   norm = c("sumtotal", "upperquartile",  "DESeq", "TMM", "quantile"))

Arguments

inttab.mat

matrix of spectral counts, proteins in rows and samples in columns.

baittab

a data.frame. The baittable as required for SAINT, classifying control and bait samples.

norm

method to normalize the data.

Value

A list containing the following components:

1

normalized spectral count matrix

2

scaling factors (if available)

Details

The baittable corresponds to a format as required for SAINT, consisting of three columns: IP name, bait or control name, indicator for bait and control experiment (T=bait purification, C=control). Note that the IP names in the baittable must be in agreement with the sample names.

Five different normalization methods, adapted from microarray and RNA-seq analysis to AP-MS data, are available: In the ‘sumtotal’ normalization counts are divided by the total number of counts in the sample. The ‘upperquartile’ normalization corrects counts by dividing each count by the 75% quantile of its sample counts. The ‘quantile’ method equalizes the distributions of protein counts across all samples. In the ‘DESeq’ approach by Anders and Huber (2010), counts are divided by the the median of the ratio of its count over its geometric mean across all samples. In the ‘TMM’ approach by Robinson and Oshlack (2010), a scaling factor is computed as the weighted mean of log ratios between chosen test and reference samples.

References

Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biology 2010.

Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 2010.

Bolstad BM, Irizarry RA, Astrand M, et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003.

Dillies M-A, Rau A, Aubert J, et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in Bioinformatics 2012.

Examples

#input data
intfile <- system.file("extdata", "inttable.txt", package="apmsWAPP")
counts <- int2mat(read.table(intfile))
baitfile <- system.file("extdata", "baittab.txt", package="apmsWAPP")
baittab <- read.table(baitfile)
# Normalization:
norm.counts <- norm.inttable(counts, baittab, norm = "upperquartile")
summary(norm.counts[[1]])
norm.counts[[2]]