@@ -1,17 +1,16 @@

 Package: tRanslatome

 Type: Package

 Title: Comparison between multiple levels of gene expression.

-Version: 1.1.0

-Date: 2013-05-06

+Version: 1.1.1

+Date: 2013-11-28

 Author: Toma Tebaldi, Erik Dassi, Galena Kostoska

-Maintainer: Toma Tebaldi <tebaldi@science.unitn.it>

+Maintainer: Toma Tebaldi <tebaldi@science.unitn.it>, Erik Dassi <erik.dassi@unitn.it>

 Depends: R (>= 2.15.0), methods, limma, sigPathway, samr, anota, DESeq,

         edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus,

         gplots, plotrix

-Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods:  Rank Product, t-test, SAM, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. 

+Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods:  Rank Product, Translational Efficiency, t-test, SAM, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. 

 License: LGPL

 LazyLoad: yes

 biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression,

         DifferentialExpression, Microarray, HighThroughputSequencing,

         QualityControl, GO, MultipleComparisons, Bioinformatics

-Packaged: 2013-06-05 06:48:29 UTC; wolf

@@ -59,7 +59,7 @@ setGeneric("GOEnrichment", signature="object",

 setGeneric("RegulatoryEnrichment", signature="object", 

 					function(object, classOfDEGs="both", 

-									 significance.threshold = 0.05, mult.cor=TRUE)

+									 significance.threshold = 0.05, mult.cor=TRUE, regulated.identities=NULL, regulated.counts=NULL)

 					standardGeneric("RegulatoryEnrichment"))

@@ -589,7 +589,7 @@ setMethod("GOEnrichment", "DEGs",

 ## Implementation of the RegulatoryEnrichment method

 setMethod("RegulatoryEnrichment", "DEGs", 

           function(object, classOfDEGs="both", 

-									 significance.threshold= 0.05, mult.cor=TRUE) {

+									 significance.threshold= 0.05, mult.cor=TRUE, regulated.identities=NULL, regulated.counts=NULL) {

 	resultMatrix <- DEGs.table(object)

 	label.fc <- grep(c("FC"), colnames(resultMatrix), value=TRUE)

@@ -624,9 +624,9 @@ setMethod("RegulatoryEnrichment", "DEGs",

 	}

 	else {

 		enriched.1 <- computeGeneListEnrichment(genes.1stlevel,

-											label.level.DEGs[1], significance.threshold, mult.cor)

+											label.level.DEGs[1], significance.threshold, mult.cor, regulated.identities=NULL, regulated.counts=NULL)

 		enriched.2 <- computeGeneListEnrichment(genes.2ndlevel,

-											label.level.DEGs[2], significance.threshold, mult.cor)

+											label.level.DEGs[2], significance.threshold, mult.cor, regulated.identities=NULL, regulated.counts=NULL)

 		return(new("EnrichedSets", enriched.table=rbind(enriched.1, enriched.2), 

 												 label.level.enriched=label.level.DEGs))	

@@ -706,15 +706,24 @@ createspecifictable <- function(background,myInterestedGenes,ontology,

 computeGeneListEnrichment <- 

-		function(DEGs.genes, level.label, significance.threshold, mult.cor) {

+		function(DEGs.genes, level.label, significance.threshold, mult.cor, regulated.identities=NULL, regulated.counts=NULL) {

-	# explicitly define the two tables (contained in tRanslatomeDataset)

-	# to avoid Rcmd check complaining as it does not see these	

-	regulatory.elements.regulated <- NULL

-	regulatory.elements.counts <- NULL

-	

-	# we need data contained in our tRanslatome dataset, so load it

-	data(tRanslatomeSampleData, envir=environment())

+		# explicitly define the two tables (contained in tRanslatomeDataset)

+		# to avoid Rcmd check complaining as it does not see these	

+		regulatory.elements.regulated <- NULL

+		regulatory.elements.counts <- NULL

+		

+		# if the user specified custom regulator-target identities and regulated

+		# counts matrices, use these

+		if (!is.null(regulated.identities) && !is.null(regulated.counts)){

+			regulatory.elements.regulated <- regulated.identities

+			regulatory.elements.counts <- regulated.counts

+		}

+		else {

+			# we need our default regulatory element data contained 

+			# in tRanslatome dataset, so load it

+			data(tRanslatomeSampleData, envir=environment())

+		}

 		enrichments <- c()

@@ -762,5 +771,6 @@ computeGeneListEnrichment <-

 												 "pv.fisher.BH"=p.adjust(enrichments[,5], 

 												 method="BH",n=nrow(regulatory.elements.counts)))

-	return(as.data.frame(enrichments[order(as.numeric(enrichments[,5])),],stringsAsFactors=F))

+	return(as.data.frame(enrichments[order(as.numeric(enrichments[,5])),],

+											stringsAsFactors=F))

 }

\ No newline at end of file

@@ -101,7 +101,7 @@ setMethod("computeDEGs", "TranslatomeDataset",

 			to calculate DEGs with statistical methods!')

 		if (!(method %in% 

-					c("limma", "SAM", "t-test", "RP", "ANOTA", "DESeq", "edgeR", "none"))) 

+					c("limma", "SAM", "t-test", "TE", "RP", "ANOTA", "DESeq", "edgeR", "none"))) 

 			stop('This method is not recognized!')

 		# conditions for the two levels (first is 1,2 and second is 3,4)

@@ -115,14 +115,25 @@ setMethod("computeDEGs", "TranslatomeDataset",

 			cond2 <- log(cond2, base=2)  

 			cond3 <- log(cond3, base=2)

 			cond4 <- log(cond4, base=2)

-		}

-		

+		}	

+	

 		cond <- cbind(cond1, cond2)

 		cond.vector <- c(rep(0, ncol(cond1)), rep(1, ncol(cond2)))

 		cond.2 <- cbind(cond3,cond4)

 		cond.2.vector <- c(rep(0, ncol(cond3)), rep(1, ncol(cond4)))

-

+		

+		# if the chosen method is translational efficiency, put together samples

+		# as first & second level case and first & second level control

+		# meaning: Pol Case vs Sub/Tot Case and Pol Ctrl vs Sub/Tot Ctrl

+		if (method == "TE") {

+			cond <- cbind(cond1, cond3)

+			cond.vector <- c(rep(0, ncol(cond1)), rep(1, ncol(cond3)))

+

+			cond.2 <- cbind(cond2,cond4)

+			cond.2.vector <- c(rep(0, ncol(cond2)), rep(1, ncol(cond4)))

+		}

+		

 		#Calculation of FC, avg, and sd for the first level

 		FC <- apply(cond, 1,

 					function(x) mean(x[which(cond.vector == 1)], na.rm=TRUE) - 

@@ -165,6 +176,11 @@ setMethod("computeDEGs", "TranslatomeDataset",

 			sig.matrix <- methodRP(cond, cond.2, cond.vector, cond.2.vector, mult.cor)

 		if (method == "t-test") 

 			sig.matrix <- methodTTest(cond, cond.2, cond.vector, cond.2.vector)

+		if (method == "TE")

+			# compute translational efficiency p-values with Limma as if it was

+			# the normal condition, but cond and cond.2 have been built in a

+			# different way (tot/sub + pol ctrl) and (tot/sub + pol case)

+			sig.matrix <- methodLimma(cond, cond.2, cond.vector, cond.2.vector)

 		if (method == "SAM") 

 			sig.matrix <- methodSAM(cond, cond.2, cond.vector, cond.2.vector)

 		if (method == "limma") 

@@ -319,6 +335,7 @@ methodTTest <- function(cond, cond.2, cond.vector, cond.2.vector) {

 							 t.test.pval2, t.test.pval.adj2))	

 }

+

 # Implementation of the SAM helper function

 methodSAM <- function(cond, cond.2, cond.vector, cond.2.vector) {

Binary files a/data/tRanslatomeSampleData.RData and b/data/tRanslatomeSampleData.RData differ

@@ -3,15 +3,17 @@

 \title{RegulatoryEnrichmentHelpfile}

 \description{

-RegulatoryEnrichment is a function which, given as input an object of class \code{\linkS4class{DEGs}}, identifies overrepresented post-transcriptional regulators (RNA-binding proteins, microRNA, etc) controlling differentially expressed genes. The analysis is applied to a dataset of experimentally determined post-transcriptional interactions (i.e. regulator-UTR interaction) extracted from AURA (http://aura.science.unitn.it). Moreover, the function can identify enriched regulators for separate classes of genes of interest: only up-regulated genes, only down-regulated genes or both of them together. The output of the function is an object of class \code{\linkS4class{EnrichedSets}}, containing the results of the enrichment analysis. 

+RegulatoryEnrichment is a function which, given as input an object of class \code{\linkS4class{DEGs}}, identifies overrepresented post-transcriptional regulators (RNA-binding proteins, microRNA, etc) controlling differentially expressed genes. The analysis is by default applied to a dataset of experimentally determined post-transcriptional interactions (i.e. regulator-UTR interaction) extracted from AURA (http://aura.science.unitn.it). However, the user can specify a custom dataset onto which the analysis can be performed (see arguments for details). Moreover, the function can identify enriched regulators for separate classes of genes of interest: only up-regulated genes, only down-regulated genes or both of them together. The method works by exploiting two lists: one containing all genes regulated by each of the post-transcriptional regulators, and the other containing the number of regulated and non-regulated genes for each of these post-transcriptional regulators in the backgroung gene set (usually the whole genome). By means of these two lists it is possible to compute a Fisher enrichment p-value indicating whether a significant group of genes in the DEGs list is likely to be regulated by one or more of these post-transcriptional regulators. The output of the function is an object of class \code{\linkS4class{EnrichedSets}}, containing the results of the enrichment analysis. 

 }

 \usage{RegulatoryEnrichment(object, classOfDEGs="both",

-    significance.threshold = 0.05, mult.cor=TRUE)}

+    significance.threshold = 0.05, mult.cor=TRUE, regulated.identities=NULL, regulated.counts=NULL)}

 \arguments{

  \item{object}{an object of class \code{\linkS4class{DEGs}}}

  \item{classOfDEGs}{a character string specifying the class of genes for which we want to detect enriched regulators: \code{up} for considering only up-regulated genes, \code{down} for considering only down-regulated genes, \code{both} for considering all DEGs, independently from the direction of their changes. The default is set to \code{both}.}

  \item{significance.threshold}{a numeric value specifying the significance threshold upon which the regulators are considered significantly over-represented. By default it is se to \code{0.05}.}

  \item{mult.cor}{a boolean variable specifying whether the significance threshold is applied to the multiple test corrected or to the original p-values obtained from the selected enrichment method. By default it is set to \code{TRUE}.}

+ \item{regulated.identities}{a matrix containing two columns (RegulatoryElement, RegulatedGenes) specyifing, for each row, a regulatory element name and the comma-separated list of genes it regulates. The user can use the regulatory.elements.regulated table in the tRanslatomeSampleData dataset as a template. By default this argument is NULL, which implies the dataset obtained from AURA will be used.}

+ \item{regulated.counts}{a matrix containing three columns (RegulatoryElement, RegulatedGenes, NonRegulatedGenes) specyifing, for each row, a regulatory element name, the number of genes it regulates in the background gene set and the number of genes it does not regulate in the background gene set. The user can use the regulatory.elements.counts table in the tRanslatomeSampleData dataset as a template. By default this argument is NULL, which implies the dataset obtained from AURA will be used.}

 }

 \value{

 	An object of class \code{\linkS4class{EnrichedSets}}

@@ -4,13 +4,13 @@

 \title{computeDEGsHelpfile}

 \description{

 This function takes as input an object of the class \code{\linkS4class{TranslatomeDataset}} which contains a normalized data matrix coming from high throughput experiment. 

-It takes as an input a character label specifying the method that we want to employ in order to detect DEGs(t-test, ANOTA, DESeq, edgeR, SAM, RP, limma) and returns an object of the class \code{\linkS4class{DEGs}}, in which each gene is assigned an expression class: up- or down-regulated at the first level, up- or down-regulated at the second level, up-regulated at both levels, down-regulated at both levels, up-regulated at the first level and down-regulated at the second level and vice versa.

+It takes as an input a character label specifying the method that we want to employ in order to detect DEGs(t-test, translational efficiency, ANOTA, DESeq, edgeR, SAM, RP, limma) and returns an object of the class \code{\linkS4class{DEGs}}, in which each gene is assigned an expression class: up- or down-regulated at the first level, up- or down-regulated at the second level, up-regulated at both levels, down-regulated at both levels, up-regulated at the first level and down-regulated at the second level and vice versa.

 }

 \usage{computeDEGs(object, method="limma", significance.threshold= 0.05,

     FC.threshold= 0, log.transformed = FALSE, mult.cor=TRUE)}

 \arguments{

  \item{object}{an object of class \code{\linkS4class{TranslatomeDataset}}}

- \item{method}{a character string that specifies the method that the user wants to employ in the differential expression analysis. It can have one the following values: \code{limma}, \code{t-test}, \code{RP}, \code{SAM}, \code{ANOTA}, \code{DESeq} and \code{none}.By default, this value is set to \code{limma},}

+ \item{method}{a character string that specifies the method that the user wants to employ in the differential expression analysis. It can have one the following values: \code{limma}, \code{t-test}, \code{RP}, \code{TE}, \code{SAM}, \code{ANOTA}, \code{DESeq} and \code{none}.By default, this value is set to \code{limma},}

  \item{significance.threshold}{a numeric value specifying the threshold on the statistical significance below which the genes are considered as differentially expressed, the default is set to \code{0.05},}

  \item{FC.threshold}{a numeric value specifying the threshold on the absolute log2 fold change, above which the genes are considered as differentially expressed, the default is set to \code{0},}

   \item{log.transformed}{a boolean variable specifying whether the signals contained in expr.matrix have been previously log2 transformed. By default it is set to \code{FALSE},}

@@ -30,6 +30,9 @@ Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ.(2005)

 Discovering statistically significant pathways in expression profiing

 studies. Proc Natl Acad Sci USA, 102(38):13544-9.

+Courtes FC et al. (2013) Translatome analysis of CHO cells identify

+key growth genes. Journal of Biotechnology, 167, 215-24.

+

 Breitling R, Armengaud P, Amtmann A, Herzyk P.(2004) Rank products: a

 simple, yet powerful, new method to detect differentially regulated

 genes in replicated microarray experiments. FEBS Lett., 573(1-3):83-92.

@@ -3,7 +3,7 @@

 \title{getDEGsMethodDEGsHelpfile}

 \description{

-This function displays an object of class \code{character} specifying the method that the user employed in the differential expression analysis. It can have one the following values: \code{limma}, \code{t-test}, \code{RP}, \code{SAM}, \code{ANOTA}, \code{DESeq} and \code{none}.By default, this value is set to \code{limma}. It takes as input an object of class \code{\linkS4class{DEGs}}.

+This function displays an object of class \code{character} specifying the method that the user employed in the differential expression analysis. It can have one the following values: \code{limma}, \code{t-test}, \code{TE}, \code{RP}, \code{SAM}, \code{ANOTA}, \code{DESeq} and \code{none}.By default, this value is set to \code{limma}. It takes as input an object of class \code{\linkS4class{DEGs}}.

 }

 \usage{getDEGsMethod(object)}

 \arguments{

@@ -6,6 +6,6 @@

 \title{tRanslatome}

 \description{

-Description:  Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods:  Rank Product, t-test, SAM, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the GO terms with heatmaps, radar plots and barplots.}

+Description:  Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods:  Translational Efficiency, Rank Product, t-test, SAM, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the GO terms with heatmaps, radar plots and barplots.}

 \keyword{package}

@@ -9,6 +9,7 @@

 \usepackage{isorot}

 \usepackage{epsfig}

 \usepackage{fullpage} % standard 1 inch margins

+\usepackage[utf8]{inputenc}

 \newcommand{\Robject}[1]{{\texttt{#1}}}

 \newcommand{\Rfunction}[1]{{\texttt{#1}}}

@@ -21,18 +22,18 @@

 \usepackage{Sweave}

 \begin{document}

-\title{tRanslatome: portrayal of translational controls hidden in high-throughput assays}

+\title{tRanslatome: an R/Bioconductor package to portray translational control}

 \author{

   Toma Tebaldi

-  \email{<tebaldi@science.unitn.it>},\\

+  \email{<t.tebaldi@unitn.it>},\\

   Erik Dassi

   \email{<erik.dassi@unitn.it>},\\

   Galena Kostoska

-  \email{<galena.kostoska@gmail.com>},\\

+  \email{<kostoska.galena@unitn.it>},\\

     Gabriella Viero 

-  \email{<viero@science.unitn.it>},\\

+  \email{<viero@fbk.eu>},\\

    Alessandro Quattrone

-  \email{<viero@science.unitn.it>},\\

+  \email{<alessandro.quattrone@unitn.it>},\\

 }

 \maketitle

@@ -40,7 +41,7 @@

 \section{Introduction}

-One way to achieve a comprehensive estimation of the influence of different layers of control on gene expression is to analyze the changes in abundances of gene expression intermediates at different levels. For example, comparing changes between abundances of mRNAs in active translation with respect to the corresponding changes in abundances of total mRNAs (by mean of parallel high-throughput profiling), we can estimate the influence of translational controls on each transcript. tRanslatome represents a complete platform for comparing data coming from two parallel high-throughput assays, profiling two different levels of gene expression (e.g. transcriptome, translatome, proteome). It provides a broad variety of statistical methods covering each step of the standard data analysis workflow: detection and comparison between levels of differentially expressed genes (DEGs), detection and comparison between levels of enriched biological themes through Gene Ontology (GO) annotation, and indirect detection of post-transcriptional regulators influencing the translational efficience of target mRNAs. The package provides tools to visually compare/contrast the resulting DEGs using scatterplots, histograms and MA plots. An additional feature lies in the possibility to detect differently enriched biological themes, provided as lists of GO terms, in the lists of DEGs identified by the gene detection method. In this way a comparison between the biological themes detected among different levels is possible: tRanslatome splits GO terms enriched exclusively in one of the two levels, calculating also the semantic similarity  between the two lists of GO terms. The differential enrichment of GO terms can be visually examined with heatmaps and radar plots. Finally, enrichment analysis of RNA binding protein binding sites, miRNA binding sites or other RNA regulatory motifs (e.g. AU rich elements) can be performed on the lists of differentially expressed genes, in order to know if some specific trans-factor is responsible for their changes in translation.

+One way to achieve a comprehensive estimation of the influence of different layers of  control on gene expression is to analyze the changes in abundances of molecular intermediates at different levels. For example, comparing changes between abundances of mRNAs in active translation with respect to the corresponding changes in abundances of total mRNAs (by mean of parallel high-throughput profiling) we can estimate the influence of translational controls on each transcript. The tRanslatome package represents a complete platform for comparing data coming from two parallel high-throughput assays, profiling two different levels of gene expression. The package focusses on the comparison between the translatome and the transcriptome, but it can be used to compare any variation monitored at two “-omics” levels (e.g. the transcriptome and the proteome). The package provides a broad variety of statistical methods covering each step of the standard data analysis workflow: detection and comparison of differentially expressed genes (DEGs), detection and comparison of enriched biological themes through Gene Ontology (GO) annotation. The package provides tools to visually compare/contrast the results. An additional feature lies in the possibility to detect enrichment of targets of translational regulators using the experimental annotation contained in the AURA database \url{<http://aura.science.unitn.it/>}.

 \section{tRanslatome in practice}

 The following code illustrates an analysis pipeline with tRanslatome.

@@ -75,8 +76,10 @@ All the steps contained in the code will be explained in more detail in the foll

 @ 

 \section{DEGs detection}

-The core of the package consists of the class holding input data and results, called \code{TranslatomeDataset}.

-Objects of this class can be created through the \code{newTranslatomeDataset} function. This function takes as input a normalized data matrix coming from the high throughput experiment with entities (genes) in rows and samples (microarray hybridizations) in columns. Since tRanslatome doesn't provide any normalization, signals contained in the data matrix should be normalized before (if appropriate). In our example (see Section 2) data were previously quantile normalized.

+The initial core of the package consists of the class holding input data and results, called \code{TranslatomeDataset}.

+Objects of this class can be created through the \code{newTranslatomeDataset} function. This function takes as input a normalized data matrix coming from the high throughput experiment with entities (genes, transcripts, exons) in rows and samples (normalized signals coming from microarray, next generation sequencing, mass spectrometry) in columns. Since tRanslatome doesn't provide any normalization, signals contained in the data matrix should be normalized before, unless the DEGs selection method doesn’t provide also a normalization step, as in the case of edgeR and DEseq. 

+In our worked example microarray data were previously quantile normalized.

+

 The function has the following input parameters:

 \begin{itemize}

 \item expr.matrix, a matrix that contains the normalized signal intensity data, each row representing a gene and each column representing a sample;

@@ -103,7 +106,7 @@ The function has the following input parameters:

 \begin{itemize}

 \item object, an object of class \code{TranslatomeDataset} containing the data needed for DEGs identification;

 \item   method, a label that specifies the statistical method for DEGs detection. 

-It can have one the following values: \code{limma} \cite{Limma}, \code{t-test} \cite{t-test}, \code{RP} \cite{RP}, \code{SAM} \cite{SAM}, \code{ANOTA} \cite{ANOTA}, \code{DESeq} \cite{DESeq}, \code{edgeR} \cite{edgeR} and \code{none};

+It can have one the following values: \code{limma} \cite{Limma}, \code{t-test} \cite{t-test}, \code{RP} \cite{RP}, \code{TE} \cite{TE}, \code{SAM} \cite{SAM}, \code{ANOTA} \cite{ANOTA}, \code{DESeq} \cite{DESeq}, \code{edgeR} \cite{edgeR} and \code{none};

 \item  significance.threshold, a threshold on the statistical significance below which the genes are

 considered as differentially expressed, the default is set to 0.05;

 \item   FC.threshold, additional threshold on the absolute log2 fold change, above which the genes are

@@ -287,7 +290,7 @@ The method \code{SimilarityPlot()}, applied to an object of class \code{GOsims},

 \section{Regulatory Enrichment}

-RegulatoryEnrichment is a function which, given as input an object of class \code{DEGs}, identifies overrepresented post-transcriptional regulators (RNA-binding proteins, microRNA, etc) possibly controlling differentially expressed genes. The analysis is applied to a dataset of experimentally determined post-transcriptional interactions extracted from the AURA database(http://aura.science.unitn.it). Moreover, the function can identify enriched regulators for separate classes of genes of interest: only up-regulated genes, only down-regulated genes or both of them together. The output of the function is an object of class \code{EnrichedSets}, containing the results of the enrichment analysis. 

+RegulatoryEnrichment is a function which, given as input an object of class \code{DEGs}, identifies overrepresented post-transcriptional regulators (RNA-binding proteins, microRNA, etc) possibly controlling differentially expressed genes. The analysis is applied to a dataset of experimentally determined post-transcriptional interactions extracted from the AURA database(http://aura.science.unitn.it). However, the user can specify a custom dataset onto which the analysis can be performed (see arguments for details). Moreover, the function can identify enriched regulators for separate classes of genes of interest: only up-regulated genes, only down-regulated genes or both of them together. Moreover, the function can identify enriched regulators for separate classes of genes of interest: only up-regulated genes, only down-regulated genes or both of them together. The method works by exploiting two lists: one containing all genes regulated by each of the post-transcriptional regulators, and the other containing the number of regulated and non-regulated genes for each of these post-transcriptional regulators in the backgroung gene set (usually the whole genome). By means of these two lists it is possible to compute a Fisher enrichment p-value indicating wether a significant group of genes in the DEGs list is likely to be regulated by one or more of these post-transcriptional regulators. The output of the function is an object of class \code{EnrichedSets}, containing the results of the enrichment analysis. 

 The method \code{Radar()} and the method \code{Heatmap()} can be applied also to objects of class \code{EnrichedSets} in order to display the top enriched regulatory elements for the first and second expression level in a radar plot or in a heatmap display.

@@ -314,7 +317,12 @@ The method \code{Radar()} and the method \code{Heatmap()} can be applied also to

   Breitling R, Armengaud P, Amtmann A, Herzyk P.

   Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments

   {\em FEBS Lett.}, 2004, 573(1-3):83-92.

-   

+  

+\bibitem{TE}  

+  Courtes FC et al. (2013) 

+  Translatome analysis of CHO cells identify key growth genes. 

+  {\em Journal of Biotechnology}, 167, 215-24.

+

 \bibitem{SAM}

   Tusher VG, Tibshirani R, Chu G.

   Significance analysis of microarrays applied to the ionizing radiation response