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

 Package: tRanslatome

 Type: Package

 Title: Comparison between multiple levels of gene expression

-Version: 1.18.0

-Date: 2016-10-05

+Version: 1.19.5

+Date: 2018-08-03

 Author: Toma Tebaldi, Erik Dassi, Galena Kostoska

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

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

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

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

         gplots, plotrix, Biobase

-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. 

+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, 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: GPL-3

 LazyLoad: yes

 biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression,

         DifferentialExpression, Microarray, HighThroughputSequencing,

         QualityControl, GO, MultipleComparisons, Bioinformatics

-Packaged: 2016-10-04 08:48:55 UTC; toma

+Packaged: 2018-08-03 12:35:55 UTC; toma

@@ -12,7 +12,6 @@ importFrom(limma, eBayes)

 import(methods)

 import(org.Hs.eg.db)

 importFrom(plotrix, radial.plot)

-import(samr)

 import(sigPathway)

 import(topGO)

 export(newTranslatomeDataset, computeDEGs, getExprMatrix, getConditionA, getConditionB, getConditionC, getConditionD, getDataType, getConditionLabels, getLevelLabels, getDEGs)

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

 			to calculate DEGs with statistical methods!')

 		if (!(method %in% 

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

+					c("limma", "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)

@@ -181,8 +181,6 @@ setMethod("computeDEGs", "TranslatomeDataset",

 			# 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") 

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

 		if (method == "ANOTA") 

@@ -336,23 +334,6 @@ methodTTest <- function(cond, cond.2, cond.vector, cond.2.vector) {

 }

-# Implementation of the SAM helper function

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

-	

-	data.tot <- list(x=cond, y=(cond.vector+1), logged2=TRUE)

-	sam <- samr(data.tot, resp.type="Two class unpaired", nperms=1000)

-	pv.sam <- samr.pvalues.from.perms(sam$tt, sam$ttstar)

-	pv.sam.adj <- p.adjust(pv.sam, method="BH", n=length(pv.sam))

-

-	data.tot2 <- list(x=cond.2, y=(cond.2.vector+1), logged2=TRUE)

-	sam2 <- samr(data.tot2, resp.type="Two class unpaired", nperms=1000)

-	pv.sam2 <- samr.pvalues.from.perms(sam2$tt, sam2$ttstar)

-	pv.sam.adj2 <- p.adjust(pv.sam2, method="BH", n=length(pv.sam2))

-

-	# build the significance p-values matrix and return it	

-	return(cbind(pv.sam, pv.sam.adj, pv.sam2, pv.sam.adj2))

-}

-

 # Implementation of the limma helper function

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

@@ -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, 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.

+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, 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{TE}, \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{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},}

@@ -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{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}}.

+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{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:  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.}

+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, 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}

@@ -106,7 +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{TE} \cite{TE}, \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{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

@@ -322,11 +322,6 @@ The method \code{Radar()} and the method \code{Heatmap()} can be applied also to

   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

-  {\em Proc Natl Acad Sci USA.}, 2001, 98(9):5116-21.

 \bibitem{ANOTA}

   Larsson O, Sonenberg N, Nadon R.