fcros-package {fcros}

A Method to Search for Differentially Expressed Genes and to Detect Recurrent Chromosomal Copy Number Aberrations

Description

Implementation of a method based on fold change rank ordering statistics to search for differentially expressed genes or to detect recurrent chromosomal copy number aberrations. This package can be used for two biological conditions high-throughput dataset (microarray, RNA-seq, ...), for expression profiling dataset over time without replicates or for cytogenetics dataset (aCGH, Sequencing).

Details

Package:	fcros
Type:	Package
Version:	1.6.1
Date:	2019-05-28
License:	GPL (>= 2)

Package fcros has the following functions:

fcros():	The function to use with a dataset from two biological condition samples. The
	dataset should be in a single table. The function fcros() performs a pairwise
	conparison of samples to obtain a matrix of fold changes. The fold changes
	are sorted and their rank values are used to associate statistic with genes/probes.
fcros2():	The function to use with datasets from two biological biological conditions. The
	datasets should be in two separate tables as inputs. The
	function fcros2() performs a pairwise comparison of samples from each table
	to obtain fold changes. The fold changes are sorted, their rank values are
	combined and then used to associate statistic with genes/probes.
pfco():	The function to use with a dataset from two biological condition samples. The
	dataset should be in a single table. The function pfco() performs a pairwise
	conparison of samples to obtain a matrix of fold changes. The fold changes
	are sorted and their rank values are used to associate statistic with genes/probes
	using a singular value decomposition.
fcrosMod():	This function uses fold changes or ratios matrix as input to associate statistic
	with genes/probes.
pfcoMod():	This function uses fold changes or ratios matrix as input to associate statistic
	with genes/probes using a singular value decomposition.
fcrosFCmat():	This function allows to compute a matrix of fold changes using
	pairwise comparisons of the two biological condition samples in a dataset.
fcrosTtest():	This function allows to use the Student t-test to calculate p-values
	for the genes in a dataset.
fcrosRead():	This function allows to read a tab delimited text file to be use as an
	input for the function fcros(), fcros2() or fcrosMod().
fcrosWrite():	This function allows to save the results obtained using the function fcros(),
	fcros2() or fcrosMod() in a tab delimited text file.
fcrosTopN():	This function allows to search for the top N down- and/or up-regulated genes
	from the results obtained using the function fcros(), fcros2(), pfco(),
	fcrosMod() or pfcoMod().
fvalTopN():	This function allows to search for the top N down- and/or up-regulated genes
	from the results obtained using the function fcros(), fcros2(), pfco(),
	fcrosMod() or pfcoMod().
pvalTopN():	This function allows to search for the top N down- and/or up-regulated genes
	from the results obtained using the function fcros(), fcros2(), pfco(),
	fcrosMod() or pfcoMod().
histoPlot():	This function plots on the screen the histogram of the FCROS statistics
	obtained using the results of the function fcros(), fcros2(), pfco(),
	fcrosMod() or pfcoMod()
fvalVolcanoPlot():	This function performs a volcano plot of the results obtained
	using the function fcros(), fcros2(), pfco(), fcrosMod() or pfcomod()
pvalVolcanoPlot():	This function performs a volcano plot of the results obtained
	using the function fcros(), fcros2(), pfco(), fcrosMod() or pfcoMod()
chrSummary():	This function summarizes detection results by chromosome
chrSegment():	This function segments a chromosome data
chrPlot():	This function performs a plot of the chromosome probes data
chrPlot2():	This function performs a plot of the chromosome segmentation results
voomReads():	This function performs a transformation of the read counts
tcnReads():	This function performs a total count normalization of reads
rankReads():	The function to use with a dataset from two biological condition samples. The
	dataset should be in a single table. The function rankReads() performs a
	pairwise conparison of samples to obtain a matrix of fold changes. Small uniform
	values are added to read counts. This is repeated nrun time. The fold changes
	are sorted and their rank values are used to associate statistic with genes/probes.
scoreThr():	Using the log10 transformed score values obtained with the rankReads(), this function
	computes numerically the inflection point value given lower and upper bound
	values for the slope region.

Author(s)

Doulaye Dembele Maintainer: 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

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

   data(fdata);

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

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;
   trim.opt <- 0.25;

   # perform fcros()
   af <- fcros(fdata, cont, test, log2.opt, trim.opt);

   # perform Volcano plot
   fvalVolcanoPlot(af, thr = 0.01)

   # save fcros values in a file
   fcrosWrite(af, file = "test2delete_values.txt");

   # now select top 20 down and/or up regulated genes
   top20 <- fcrosTopN(af, 20);
   alpha1 <- top20$alpha[1];
   alpha2 <- top20$alpha[2];
   id.down  <- matrix(c(0,11), ncol = 1);
   id.up <- matrix(c(rep(0,11)), ncol = 1);
   n <- length(af$FC);
   f.value <- af$f.value;

   idown <- 1;
   iup <- 1;
   for (i in 1:n) {
       if (f.value[i] <= alpha1) { id.down[idown] <- i; idown <- idown+1; }
       if (f.value[i] >= alpha2) { id.up[iup] <- i; iup <- iup+1; }
   }

   data.down <- fdata[id.down[1:(idown-1)], ];
   ndown <- nrow(data.down);
   data.up <- fdata[id.up[1:(iup-1)], ];
   nup <- nrow(data.up);

   # now plot down regulated genes
   t <- 1:20;
   op = par(mfrow = c(2,1));
   plot(t, data.down[1,2:21], type = "l", col = "blue", xlim = c(1,20),
          ylim = c(0,18), main = "Top down-regulated genes");
   for (i in 2:ndown) {
       lines(t, data.down[i,2:21], type = "l", col = "blue")
   }

   # now plot down and up regulated genes
   plot(t, data.up[1,2:21], type = "l", col = "red", xlim = c(1,20),
       ylim = c(0,18), main = "Top up-regulated genes");
   for (i in 2:nup) {
       lines(t, data.up[i,2:21], type = "l", col = "red")
   }
   par(op)