fcrosFCmat {fcros}

Calculation of a matrix of fold changes using pairwise comparisons

Description

This function is used internally by fcros2() to compute a matrix of fold changes using pairwise comparisons of a two biological conditions dataset.

Usage

fcrosFCmat(xdata, cont, test, log2.opt=0, trim.opt=0.25)

Arguments

xdata

A table containing a two biological conditions dataset to process for obtaining a matrix of fold changes that results from pairwise comparisons of samples. The rownames of xdata are used for the output idnames.

cont

A vector containing label names of the control samples: cont = c("cont01", "cont02", ...)

test

A vector containing label names of the test samples: test = c("test01", "test02", "test03", ...)

log2.opt

A scalar equals to 0 or 1. The value 0 (default) means that the data values in matrix "xdata" are expressed in a log2 scale: log2.opt = 0

trim.opt

A scalar between 0 and 0.5. The value 0.25 (default) means that 25% of the lower and the upper rank values of each gene are not used for computing the statistic "ri" or "u1", i.e. the interquartile rank values are averaged: trim.opt = 0.25

Details

The label names appearing in the parameters "cont" and "test" should match some label names of the columns in the table "xdata". The dataset "xdata" can contain other label names not used.

Value

This function returns a data frame containing 3 components

idnames

A vector containing the list of IDs or symbols associated with genes

fcMat

A matrix of fold changes associated with genes. This matrix is obtained using pairwise comparisons of samples in the dataset "xdata".

FC

The fold changes for genes in the dataset "xdata". These fold changes are calculated as a ratio of averages from the test and the control samples. Non log scale values are used in this calculation.

FC2

The robust fold changes for genes in the dataset "xdata". These fold changes are calculated as a trimmed mean of the fold changes obtained using pairs of samples. Non log scale values are used in this calculation.

Author(s)

Doulaye Dembele [e-mail]

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

   data(fdata);

   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform fcrosFCmat()
   fc <- fcrosFCmat(fdata, cont, test, log2.opt);

   # plot histogram of the fold change (log2 scale) in dataset
   hist(log2(fc$FC),nclass = 50);