[NAME]	DESeq
[SCRIPT-NAME]	scriptDESeq.R
[MAX-2-CONDITIONS]	true
[NEEDS-REPLICATES]	true
[RESULT]	3-MAPlot.png
[RESULT]	2-histogram.png
[RESULT]	5-plots.pdf
[MANDATORY-RESULT]	1-res.tsv
[RESULT]	4-volcanoPlot.png
[REQUIRED-INPUT-FILE]	condition-input.tsv
[REQUIRED-INPUT-FILE]	rna-seq-input.tsv
[REQUIRED-PARAMETER]	fdr	Float
[REQUIRED-PARAMETER]	foldChange	Float
[REQUIRED-PARAMETER]	pValue	Float
[REQUIRED-PARAMETER]	tops	Integer
[PACKAGE]	DESeq	1.28.0
[INFO]	DESeq: Differential gene expression analysis based on the negative binomial distribution.
[INFO]	Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution
[INFO]	As a first processing step is to estimate the effective library size from input file. This step is sometimes also called normalisation.
[INFO]	After Obtains dispersion estimates, i.e. the dispersion can be understood as the square of the coefficient of biological variation for a count data set.
[INFO]	Having  estimated  the  dispersion  for  each  gene,  it  is  straight-forward  to  look  for  differentially  expressed  genes.
[INFO]	In conclusion of nbinomTest function the output is one dataframe with data which will filtered from the defined parameters in R-Peridot.
[INFO]	Author: Simon Anders, EMBL Heidelberg <sanders at fs.tum.de>.
[INFO]	Maintainer: Simon Anders <sanders at fs.tum.de>.
