@@ -1,7 +1,7 @@

 Package: tRanslatome

 Type: Package

 Title: Comparison between multiple levels of gene expression

-Version: 1.23.0

+Version: 1.23.1

 Date: 2018-08-03

 Author: Toma Tebaldi, Erik Dassi, Galena Kostoska

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

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

 setClass("DEGs",

 	representation(

-		DEGs.table="matrix", method="character", 

+		DEGs.table="matrix", method="character",

 		significance.threshold="numeric", FC.threshold="numeric",

 		label.level.DEGs="character", label.condition="character"

 	)

@@ -13,52 +13,52 @@ setClass("DEGs",

 ## Generics declaration (getters, setters and methods)

 ###############################################################################

-setGeneric("DEGs.table", signature="object", 

+setGeneric("DEGs.table", signature="object",

 					function(object) standardGeneric("DEGs.table"))

-					

-setGeneric("getDEGsMethod", signature="object", 

+

+setGeneric("getDEGsMethod", signature="object",

 					function(object) standardGeneric("getDEGsMethod"))

-setGeneric("significance.threshold", signature="object", 

+setGeneric("significance.threshold", signature="object",

 					function(object) standardGeneric("significance.threshold"))

-					

-setGeneric("FC.threshold", signature="object", 

+

+setGeneric("FC.threshold", signature="object",

 					function(object) standardGeneric("FC.threshold"))

-					

-setGeneric("label.level.DEGs", signature="object", 

+

+setGeneric("label.level.DEGs", signature="object",

 					function(object) standardGeneric("label.level.DEGs"))

-					

-setGeneric("label.condition", signature="object", 

+

+setGeneric("label.condition", signature="object",

 					function(object) standardGeneric("label.condition"))

-setGeneric("MAplot", signature="object", 

-					function(object, outputformat="on screen",track="") 

+setGeneric("MAplot", signature="object",

+					function(object, outputformat="on screen",track="")

 					standardGeneric("MAplot"))

-					

-setGeneric("CVplot", signature="object", 

-					function(object, outputformat="on screen",track="") 

+

+setGeneric("CVplot", signature="object",

+					function(object, outputformat="on screen",track="")

 					standardGeneric("CVplot"))

-					

-setGeneric("SDplot", signature="object", 

-					function(object, outputformat="on screen",track="") 

+

+setGeneric("SDplot", signature="object",

+					function(object, outputformat="on screen",track="")

 					standardGeneric("SDplot"))

-					

-setGeneric("Histogram", signature="object", 

-					function(object, plottype="summary", outputformat="on screen") 

+

+setGeneric("Histogram", signature="object",

+					function(object, plottype="summary", outputformat="on screen")

 					standardGeneric("Histogram"))

-					

+

 setGeneric("Scatterplot", signature="object",

-					function(object, outputformat="on screen",track="") 

+					function(object, outputformat="on screen",track="")

 					standardGeneric("Scatterplot"))

-					

-setGeneric("GOEnrichment", signature="object", 

-					function(object, ontology="all", 

+

+setGeneric("GOEnrichment", signature="object",

+					function(object, ontology="all",

 									classOfDEGs="both", test.method="classic",

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

+									test.threshold = 0.05, mult.cor=TRUE)

 					standardGeneric("GOEnrichment"))

-		

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

-					function(object, classOfDEGs="both", 

+

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

+					function(object, classOfDEGs="both",

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

 					standardGeneric("RegulatoryEnrichment"))

@@ -127,13 +127,13 @@ setMethod("CVplot", "DEGs",

 		c <- grep(c("avg"), colnames(resultMatrix),value=TRUE)

 		b <- grep(c("sd"), colnames(resultMatrix),value=TRUE)

-		vector1 <- rowMeans(resultMatrix[,b[1:2]]) / 

+		vector1 <- rowMeans(resultMatrix[,b[1:2]]) /

 												abs(rowMeans(resultMatrix[,c[1:2]]))

-		vector2 <- resultMatrix[,a[1]] 

+		vector2 <- resultMatrix[,a[1]]

 		vector3 <- rowMeans(resultMatrix[,b[3:4]]) /

 												abs(rowMeans(resultMatrix[,c[3:4]]))

-		vector4 <- resultMatrix[,a[2]] 

+		vector4 <- resultMatrix[,a[2]]

 		xlabel <- "CV"

 		ylabel <- "log2 FC"

@@ -142,13 +142,13 @@ setMethod("CVplot", "DEGs",

 		if (outputformat == "postscript") postscript(file="CVplot.ps")

 		if (outputformat == "jpeg") jpeg(filename="CVplot.jpeg")

-		par(fig=c(0,1,0,1), mar=c(4,4,1,2), mgp=c(2, 0.75, 0))	

+		par(fig=c(0,1,0,1), mar=c(4,4,1,2), mgp=c(2, 0.75, 0))

 		layout(matrix(c(1,2), 2, 1, byrow = TRUE))

-		plot(vector1, vector2, xlab=xlabel, ylab=ylabel, 

-					col="grey", pch=20, frame.plot=TRUE,cex=0.8, 

+		plot(vector1, vector2, xlab=xlabel, ylab=ylabel,

+					col="grey", pch=20, frame.plot=TRUE,cex=0.8,

 					main=substr(a[1], 10, nchar(a[1])-1))

-		list1 <- rownames(resultMatrix)[which(resultMatrix[,"level1Up"]==1 | 

+		list1 <- rownames(resultMatrix)[which(resultMatrix[,"level1Up"]==1 |

 																					resultMatrix[,"level1Down"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list1)

 		color <- "steelblue3"

@@ -163,12 +163,12 @@ setMethod("CVplot", "DEGs",

 			text(vector1[variable],vector2[variable],labels=listm,pos=3,cex=0.7)

 		}

-		plot(vector3, vector4, xlab=xlabel, ylab=ylabel, 

+		plot(vector3, vector4, xlab=xlabel, ylab=ylabel,

 					col="grey", pch=20, frame.plot=TRUE,cex=0.8,

 					main=substr(a[2], 10, nchar(a[2])-1))

 		abline(h=0,lty=2,col="darkgray")

-		list2 <- rownames(resultMatrix)[which(resultMatrix[,"level2Up"]==1 | 

+		list2 <- rownames(resultMatrix)[which(resultMatrix[,"level2Up"]==1 |

 																					resultMatrix[,"level2Down"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list2)

 		color <- "gold1"

@@ -197,10 +197,10 @@ setMethod("MAplot", "DEGs",

 		b <- grep(c("avg"),colnames(resultMatrix),value=TRUE)

 		vector1 <- rowMeans(resultMatrix[,b[1:2]])

-		vector2 <- resultMatrix[,a[1]] 

+		vector2 <- resultMatrix[,a[1]]

 		vector3 <- rowMeans(resultMatrix[,b[3:4]])

-		vector4 <- resultMatrix[,a[2]] 

+		vector4 <- resultMatrix[,a[2]]

 		xlabel="log2 avg signal (A)"

 		ylabel="log2 FC (M)"

@@ -209,13 +209,13 @@ setMethod("MAplot", "DEGs",

 		if (outputformat == "postscript") postscript(file="MAplot.DEGs.ps")

 		if (outputformat == "jpeg") jpeg(filename="MAplot.DEGs.jpeg")

-		par(fig=c(0,1,0,1), mar=c(4,4,1,2), mgp=c(2, 0.75, 0))	

+		par(fig=c(0,1,0,1), mar=c(4,4,1,2), mgp=c(2, 0.75, 0))

 		layout(matrix(c(1,2), 2, 1, byrow = TRUE))

-		plot(vector1, vector2, xlab=xlabel, ylab=ylabel, 

-					col="grey", pch=20, frame.plot=TRUE, cex=0.8, 

+		plot(vector1, vector2, xlab=xlabel, ylab=ylabel,

+					col="grey", pch=20, frame.plot=TRUE, cex=0.8,

 					main=substr(a[1], 10, nchar(a[1])-1))

-		list1 <- rownames(resultMatrix)[which(resultMatrix[,"level1Up"]==1 | 

+		list1 <- rownames(resultMatrix)[which(resultMatrix[,"level1Up"]==1 |

 																					resultMatrix[,"level1Down"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list1)

 		color <- "steelblue3"

@@ -230,12 +230,12 @@ setMethod("MAplot", "DEGs",

 			text(vector1[variable],vector2[variable],labels=listm,pos=3,cex=0.7)

 		}

-		plot(vector3, vector4, xlab=xlabel, ylab=ylabel, 

+		plot(vector3, vector4, xlab=xlabel, ylab=ylabel,

 					col="grey", pch=20, frame.plot=TRUE,cex=0.8,

 					main=substr(a[2], 10, nchar(a[2])-1))

 		abline(h=0,lty=2,col="darkgray")

-		list2 <- rownames(resultMatrix)[which(resultMatrix[,"level2Up"]==1 | 

+		list2 <- rownames(resultMatrix)[which(resultMatrix[,"level2Up"]==1 |

 																					resultMatrix[,"level2Down"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list2)

 		color <- "gold1"

@@ -278,21 +278,21 @@ setMethod("Histogram", "DEGs",

 		if (outputformat == "pdf") pdf(file="Histogram.DEGs.pdf")

 		if (outputformat == "postscript") postscript(file="Histogram.DEGs.ps")

 		if (outputformat == "jpeg") jpeg(filename="Histogram.DEGs.jpeg")

-			

+

 		if (plottype=="summary") {

 			inputforsummaryplot <- matrix(c(length(list1stlevelUP),

-																			length(list1stlevelDOWN), 

-																			length(list2ndlevelUP), 

+																			length(list1stlevelDOWN),

+																			length(list2ndlevelUP),

 																			length(list2ndlevelDOWN),

 																			0, 0), nrow=2, byrow=FALSE)

 			par(fig=c(0, 1, 0, 1), mar=c(4, 5, 4, 2), mgp=c(2, 0.75, 0))

 			barplot(as.table(inputforsummaryplot), xlab="number of DEGs",

-							names.arg=c(substr(a[1], 10, nchar(a[1])-1), 

-													substr(a[2], 10, nchar(a[2])-1),""), 

-							col=c("grey25","grey75"), las=1, 

-							border=NA, horiz=TRUE, beside=FALSE) 

-			

+							names.arg=c(substr(a[1], 10, nchar(a[1])-1),

+													substr(a[2], 10, nchar(a[2])-1),""),

+							col=c("grey25","grey75"), las=1,

+							border=NA, horiz=TRUE, beside=FALSE)

+

 			legend("topright", c("up", "down"), fill=c("grey25", "grey75"), bty="n")

 		}

@@ -303,19 +303,19 @@ setMethod("Histogram", "DEGs",

 				length(list1stlevelDOWN) - (length(listDOWNDOWN) + length(listDOWNUP)),

 				length(list2ndlevelUP) - (length(listUPUP) + length(listDOWNUP)),

 				length(list2ndlevelDOWN) - (length(listDOWNDOWN) + length(listUPDOWN)),

-				length(listUPUP), length(listDOWNDOWN), 

+				length(listUPUP), length(listDOWNDOWN),

 				length(listUPDOWN), length(listDOWNUP)),

 				nrow=1, byrow=FALSE)

-				

+

 			par(fig=c(0,1,0,1), mar=c(4,7,4,2), mgp=c(2, 0.75, 0))

 			barplot(as.table(inputfordetailedplot),

-				names.arg= c("up / -","down / -","- / up","- / down", 

+				names.arg= c("up / -","down / -","- / up","- / down",

 									   "up / up","down / down","up / down","down / up"),

 				las=1,border=NA,horiz=TRUE,

-				col=c("steelblue","steelblue4","gold","orange", 

+				col=c("steelblue","steelblue4","gold","orange",

 							"chartreuse","chartreuse4","red","red3"),

 				beside=TRUE,xlab="number of DEGs")

-				

+

 			mtext(paste(substr(a[1], 10, nchar(a[1])-1),

 									substr(a[2], 10, nchar(a[2])-1), sep="/"),

 						adj=0, line=0, cex=1)

@@ -325,15 +325,15 @@ setMethod("Histogram", "DEGs",

 		if (!(outputformat == "on screen")) dev.off()

 	}

 )

-  

-  

-## Implementation of the Scatterplot method  

+

+

+## Implementation of the Scatterplot method

 setMethod("Scatterplot", "DEGs",

 	function(object, outputformat="on screen", track="") {

-	

+

 		resultMatrix <- DEGs.table(object)

 		a <- grep(c("FC"), colnames(resultMatrix), value=TRUE)

-		 

+

 		vector1 <- resultMatrix[,a[1]]

 		vector2 <- resultMatrix[,a[2]]

@@ -347,31 +347,31 @@ setMethod("Scatterplot", "DEGs",

 		if (outputformat == "postscript") postscript(file="Scatterplot.DEGs.ps")

 		if (outputformat == "jpeg") jpeg(filename="Scatterplot.DEGs.jpeg")

-		plot(originalFCvalX, originalFCvalY, xlab=xlabel, ylab=ylabel, 

+		plot(originalFCvalX, originalFCvalY, xlab=xlabel, ylab=ylabel,

 				 col="grey", pch=20, frame.plot=TRUE, cex=0.8, log="xy")

-		mtext(paste("Spearman all:", 

+		mtext(paste("Spearman all:",

 					round(cor.test(vector1, vector2, method="spearman")$estimate, 2),

 					" "),

 					side=1, adj=1, line=-2.5, cex=0.8)

-		list0 <- rownames(resultMatrix)[c(which(resultMatrix[,"level1Up"]==1), 

+		list0 <- rownames(resultMatrix)[c(which(resultMatrix[,"level1Up"]==1),

 																			which(resultMatrix[,"level1Down"]==1),

-																			which(resultMatrix[,"level2Up"]==1), 

+																			which(resultMatrix[,"level2Up"]==1),

 																			which(resultMatrix[,"level2Down"]==1))]

 		variable <- which(rownames(resultMatrix)%in%list0)

 		if (length(list0)>1)

 			mtext(paste("Spearman DEGs:",

-						round(cor.test(vector1[list0], 

+						round(cor.test(vector1[list0],

 													 vector2[list0],

 													 method="spearman")$estimate, 2),

-						" "), 

+						" "),

 						side=1, adj=1, line=-1.5, cex=0.8)

 		leg <- NULL

 		leg.col <- NULL

-		list1 <- rownames(resultMatrix)[which(resultMatrix[,"level1Up"]==1 | 

+		list1 <- rownames(resultMatrix)[which(resultMatrix[,"level1Up"]==1 |

 																					resultMatrix[,"level1Down"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list1)

 		color <- "steelblue3"

@@ -380,11 +380,11 @@ setMethod("Scatterplot", "DEGs",

 		leg <- c(leg, paste(substr(a[1], 10, nchar(a[1])-1), "only"))

 		leg.col <- c(leg.col, color)

-		list2 <- rownames(resultMatrix)[which(resultMatrix[,"level2Up"]==1 | 

+		list2 <- rownames(resultMatrix)[which(resultMatrix[,"level2Up"]==1 |

 																					resultMatrix[,"level2Down"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list2)

 		color <- "gold1"

-		points(originalFCvalX[variable], originalFCvalY[variable], 

+		points(originalFCvalX[variable], originalFCvalY[variable],

 					 col=color, pch=20, cex=0.8)

 		leg <- c(leg, paste(substr(a[2], 10, nchar(a[2])-1), "only"))

 		leg.col <- c(leg.col, color)

@@ -393,12 +393,12 @@ setMethod("Scatterplot", "DEGs",

 																					resultMatrix[,"DownDown"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list3)

 		color <- "chartreuse3"

-		points(originalFCvalX[variable], originalFCvalY[variable], 

+		points(originalFCvalX[variable], originalFCvalY[variable],

 					 col=color, pch=20, cex=0.8)

 		leg <- c(leg, "homodirectional")

 		leg.col <- c(leg.col, color)

-		list4 <- rownames(resultMatrix)[which(resultMatrix[,"UpDown"]==1 | 

+		list4 <- rownames(resultMatrix)[which(resultMatrix[,"UpDown"]==1 |

 																					resultMatrix[,"DownUp"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list4)

 		color <- "red2"

@@ -414,9 +414,9 @@ setMethod("Scatterplot", "DEGs",

 		listm <- rownames(resultMatrix)[rownames(resultMatrix) %in% track]

 		if (length(listm) > 0) {

 			variable <- which(rownames(resultMatrix) %in% track)

-			points(originalFCvalX[variable], originalFCvalY[variable], 

+			points(originalFCvalX[variable], originalFCvalY[variable],

 						 col="white", pch=4, cex=0.5)

-			text(originalFCvalX[variable], originalFCvalY[variable], 

+			text(originalFCvalX[variable], originalFCvalY[variable],

 					 labels=listm, pos=3, cex=0.7)

 		}

@@ -429,16 +429,16 @@ setMethod("Scatterplot", "DEGs",

 ## Implementation of the SDplot method

 setMethod("SDplot", "DEGs",

 	function(object, outputformat="on screen", track="") {

-	

+

 		resultMatrix <- DEGs.table(object)

 		a <- grep(c("FC"), colnames(resultMatrix), value=TRUE)

 		b <- grep(c("sd"), colnames(resultMatrix), value=TRUE)

 		vector1 <- rowMeans(resultMatrix[,b[1:2]])

-		vector2 <- resultMatrix[,a[1]] 

+		vector2 <- resultMatrix[,a[1]]

 		vector3 <- rowMeans(resultMatrix[,b[3:4]])

-		vector4 <- resultMatrix[,a[2]] 

+		vector4 <- resultMatrix[,a[2]]

 		xlabel="SD"

 		ylabel="log2 FC"

@@ -446,18 +446,18 @@ setMethod("SDplot", "DEGs",

 		if (outputformat == "pdf") pdf(file="SDplot.DEGs.pdf")

 		if (outputformat == "ps") postscript(file="SDplot.DEGs.ps")

 		if (outputformat == "jpeg") jpeg(filename="SDplot.DEGs.jpeg")

-		

-		par(fig=c(0,1,0,1), mar=c(4,4,1,2), mgp=c(2, 0.75, 0))	

+

+		par(fig=c(0,1,0,1), mar=c(4,4,1,2), mgp=c(2, 0.75, 0))

 		layout(matrix(c(1,2), 2, 1, byrow = TRUE))

-		plot(vector1, vector2, xlab=xlabel, ylab=ylabel, 

+		plot(vector1, vector2, xlab=xlabel, ylab=ylabel,

 				 col="grey", pch=20, frame.plot=TRUE,cex=0.8,

 				 main=substr(a[1], 10, nchar(a[1])-1))

-		list1 <- rownames(resultMatrix)[which(resultMatrix[,"level1Up"]==1 | 

+		list1 <- rownames(resultMatrix)[which(resultMatrix[,"level1Up"]==1 |

 																					resultMatrix[,"level1Down"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list1)

 		color <- "steelblue3"

-		points(vector1[variable], vector2[variable], 

+		points(vector1[variable], vector2[variable],

 					 col=color, pch=20, cex=0.8)

 		abline(h=0, lty=2, col="darkgray")

 		legend("topright", c("DEGs"), fill=c(color), bty="n", cex=0.8)

@@ -469,12 +469,12 @@ setMethod("SDplot", "DEGs",

 			text(vector1[variable], vector2[variable], labels=listm, pos=3, cex=0.7)

 		}

-		plot(vector3, vector4, xlab=xlabel, ylab=ylabel, 

+		plot(vector3, vector4, xlab=xlabel, ylab=ylabel,

 				 col="grey", pch=20, frame.plot=TRUE, cex=0.8,

 				 main=substr(a[2], 10, nchar(a[2])-1))

 		abline(h=0, lty=2, col="darkgray")

-		list2 <- rownames(resultMatrix)[which(resultMatrix[,"level2Up"]==1 | 

+		list2 <- rownames(resultMatrix)[which(resultMatrix[,"level2Up"]==1 |

 																					resultMatrix[,"level2Down"]==1)]

 		variable <- which(rownames(resultMatrix) %in% list2)

 		color <- "gold1"

@@ -486,7 +486,7 @@ setMethod("SDplot", "DEGs",

 		listm <- rownames(resultMatrix)[rownames(resultMatrix) %in% track]

 		if (length(listm) > 0) {

 			variable <- which(rownames(resultMatrix) %in% track)

-			points(vector3[variable], vector4[variable], 

+			points(vector3[variable], vector4[variable],

 						 col="white", pch=4, cex=0.5)

 			text(vector3[variable], vector4[variable], labels=listm, pos=3, cex=0.7)

 		}

@@ -499,26 +499,26 @@ setMethod("SDplot", "DEGs",

 ## Implementation of the GOenrichment method

 setMethod("GOEnrichment", "DEGs",

-	function(object, ontology="all", classOfDEGs="both", test.method="classic", 

+	function(object, ontology="all", classOfDEGs="both", test.method="classic",

 					 test.threshold = 0.05, mult.cor=TRUE) {

-		

+

 		resultMatrix <- DEGs.table(object)

 		a <- grep(c("FC"), colnames(resultMatrix), value=TRUE)

 		myInterestedGenes1stlevel <- rownames(resultMatrix)[

-																	which(resultMatrix[,"level1Up"]==1 | 

+																	which(resultMatrix[,"level1Up"]==1 |

 																				resultMatrix[,"level1Down"]==1)]

 		myInterestedGenes2ndlevel <- rownames(resultMatrix)[

-																	which(resultMatrix[,"level2Up"]==1 | 

+																	which(resultMatrix[,"level2Up"]==1 |

 																				resultMatrix[,"level2Down"]==1)]

-		

+

 		if (classOfDEGs == "up") {

 			myInterestedGenes1stlevel <- rownames(resultMatrix)[

 																		which(resultMatrix[,"level1Up"]==1)]

 			myInterestedGenes2ndlevel <- rownames(resultMatrix)[

 																		which(resultMatrix[,"level2Up"]==1)]

 		}

-			

+

 		if (classOfDEGs == "down") {

 			myInterestedGenes1stlevel <- rownames(resultMatrix)[

 																		which(resultMatrix[,"level1Down"]==1)]

@@ -526,7 +526,7 @@ setMethod("GOEnrichment", "DEGs",

 																		which(resultMatrix[,"level2Down"]==1)]

 		}

-		if (length(myInterestedGenes1stlevel) == 0 && 

+		if (length(myInterestedGenes1stlevel) == 0 &&

 				length(myInterestedGenes2ndlevel) == 0) {

 			print("There are no genes selected for enrichment analysis.")

 			print("Please try to use a less stringent gene filter.")

@@ -537,47 +537,47 @@ setMethod("GOEnrichment", "DEGs",

 														substr(a[2], 10, nchar(a[2])-1))

 			# Choice of the ontology

-			

+

 			if (ontology !=  "all") {

-				onto.first <- createspecifictable(background, 

-											myInterestedGenes1stlevel, ontology, 

-											test.method, test.threshold, mult.cor, 

+				onto.first <- createspecifictable(background,

+											myInterestedGenes1stlevel, ontology,

+											test.method, test.threshold, mult.cor,

 											label.level.DEGs[1])

-											

+

 				onto.second <- createspecifictable(background,

-											 myInterestedGenes2ndlevel, ontology, 

-											 test.method, test.threshold, mult.cor, 

+											 myInterestedGenes2ndlevel, ontology,

+											 test.method, test.threshold, mult.cor,

 											 label.level.DEGs[2])

-											 

+

 				final.table <- rbind(onto.first, onto.second)

-						

-				allRes <- new("GOsets", enriched.table = final.table, 

+

+				allRes <- new("GOsets", enriched.table = final.table,

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

-			} 

-			else {		

-				BP.first <- createspecifictable(background, myInterestedGenes1stlevel, 

-																				"BP", test.method, test.threshold, 

+			}

+			else {

+				BP.first <- createspecifictable(background, myInterestedGenes1stlevel,

+																				"BP", test.method, test.threshold,

 																				mult.cor, label.level.DEGs[1])

-				MF.first <- createspecifictable(background, myInterestedGenes1stlevel, 

+				MF.first <- createspecifictable(background, myInterestedGenes1stlevel,

 																				"MF", test.method, test.threshold,

 																				mult.cor, label.level.DEGs[1])

-				CC.first <- createspecifictable(background, myInterestedGenes1stlevel, 

+				CC.first <- createspecifictable(background, myInterestedGenes1stlevel,

 																				"CC", test.method, test.threshold,

 																				mult.cor, label.level.DEGs[1])

-				

-				BP.second <- createspecifictable(background, myInterestedGenes2ndlevel, 

+

+				BP.second <- createspecifictable(background, myInterestedGenes2ndlevel,

 																				"BP", test.method, test.threshold,

 																				mult.cor, label.level.DEGs[2])

-				MF.second <- createspecifictable(background, myInterestedGenes2ndlevel, 

+				MF.second <- createspecifictable(background, myInterestedGenes2ndlevel,

 																				"MF", test.method, test.threshold,

 																				mult.cor, label.level.DEGs[2])

 				CC.second <- createspecifictable(background, myInterestedGenes2ndlevel,

 																				"CC", test.method, test.threshold,

 																				mult.cor, label.level.DEGs[2])

-				final.table <- rbind(BP.first, MF.first, CC.first, 

+				final.table <- rbind(BP.first, MF.first, CC.first,

 														 BP.second, MF.second, CC.second)

-				allRes <- new("GOsets", enriched.table=final.table, 

+				allRes <- new("GOsets", enriched.table=final.table,

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

 			}

 		}

@@ -587,30 +587,30 @@ setMethod("GOEnrichment", "DEGs",

 ## Implementation of the RegulatoryEnrichment method

-setMethod("RegulatoryEnrichment", "DEGs", 

-          function(object, classOfDEGs="both", 

+setMethod("RegulatoryEnrichment", "DEGs",

+          function(object, classOfDEGs="both",

 									 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)

 	label.level.DEGs <- c(substr(label.fc[1], 10, nchar(label.fc[1])-1),

 												substr(label.fc[2], 10, nchar(label.fc[2])-1))

-	

+

 	genes.1stlevel <- rownames(resultMatrix)[

-															which(resultMatrix[,"level1Up"]==1 | 

+															which(resultMatrix[,"level1Up"]==1 |

 																		resultMatrix[,"level1Down"]==1)]

 	genes.2ndlevel <- rownames(resultMatrix)[

-															which(resultMatrix[,"level2Up"]==1 | 

+															which(resultMatrix[,"level2Up"]==1 |

 																		resultMatrix[,"level2Down"]==1)]

-		

+

 	if (classOfDEGs == "up") {

 		genes.1stlevel <- rownames(resultMatrix)[

 															which(resultMatrix[,"level1Up"]==1)]

 		genes.2ndlevel <- rownames(resultMatrix)[

 															which(resultMatrix[,"level2Up"]==1)]

 	}

-			

+

 	if (classOfDEGs == "down") {

 		genes.1stlevel <- rownames(resultMatrix)[

 															which(resultMatrix[,"level1Down"]==1)]

@@ -627,9 +627,9 @@ setMethod("RegulatoryEnrichment", "DEGs",

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

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

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

-		

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

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

+

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

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

 	}

 }

 )

@@ -640,58 +640,58 @@ setMethod("RegulatoryEnrichment", "DEGs",

 createspecifictable <- function(background,myInterestedGenes,ontology,

 	test.method,test.threshold,mult.cor,level.label) {

-	

-	xx <- annFUN.org(ontology, feasibleGenes=background, 

+

+	xx <- annFUN.org(ontology, feasibleGenes=background,

 									mapping="org.Hs.eg.db", ID="symbol")

 	xxxx <- inverseList(xx)

 	geneNames <- names(xxxx)

 	geneList <- factor(as.integer(geneNames %in% myInterestedGenes))

 	names(geneList) <- geneNames

 	str(geneList)

-	

+

 	GOdata <- new("topGOdata", ontology = ontology, allGenes = geneList,

 								annot = annFUN.gene2GO, gene2GO = xxxx, nodeSize= 5)

-		

-	if (test.method == "classic") 

-		resultFisher <- runTest(GOdata, algorithm = "classic", 

+

+	if (test.method == "classic")

+		resultFisher <- runTest(GOdata, algorithm = "classic",

 														statistic = "fisher")

-	if (test.method == "elim") 

-		resultFisher <- runTest(GOdata, algorithm = "elim", 

+	if (test.method == "elim")

+		resultFisher <- runTest(GOdata, algorithm = "elim",

 														statistic = "fisher")

-	if (test.method == "weight") 

-		resultFisher <- runTest(GOdata, algorithm = "weight", 

+	if (test.method == "weight")

+		resultFisher <- runTest(GOdata, algorithm = "weight",

 														statistic = "fisher")

-	if (test.method == "weight01") 

+	if (test.method == "weight01")

 		resultFisher <- runTest(GOdata, algorithm = "weight01",

 														statistic = "fisher")

-	if (test.method == "parentchild") 

-		resultFisher <- runTest(GOdata, algorithm = "parentchild", 

+	if (test.method == "parentchild")

+		resultFisher <- runTest(GOdata, algorithm = "parentchild",

 														statistic = "fisher")

 	s <- score(resultFisher)

-	

-	enrich.table <- GenTable(GOdata, Fisher.result = resultFisher, 

-													 orderBy = "Fisher.result", 

+

+	enrich.table <- GenTable(GOdata, Fisher.result = resultFisher,

+													 orderBy = "Fisher.result",

 													 ranksOf = "Fisher.result", topNodes = length(s))

 	enrich.table$Fisher.classic.BH <- p.adjust(

 																		as.numeric(enrich.table[,"Fisher.result"]),

-																		"BH", 

+																		"BH",

 																		n=length(enrich.table[,"Fisher.result"]))

-	

+

 	if (!mult.cor) {

 		enrich.table <- enrich.table[

 											which(

-												enrich.table[,"Fisher.result"] <= test.threshold),]

+												as.numeric(enrich.table[,"Fisher.result"]) <= test.threshold),]

 	}

 	else {

 		enrich.table <- enrich.table[

 											which(

-												enrich.table[,"Fisher.classic.BH"] <= test.threshold),]

+												as.numeric(enrich.table[,"Fisher.classic.BH"]) <= test.threshold),]

 	}

-		

-	enrich.table <- enrich.table[order(enrich.table[,"Fisher.result"], 

+

+	enrich.table <- enrich.table[order(as.numeric(enrich.table[,"Fisher.result"]),

 															 na.last = TRUE, decreasing = FALSE),]

-	

+

 	OntologyLevel <- matrix(,nrow=dim(enrich.table)[1], ncol=2)

 	colnames(OntologyLevel) <- c("ontology", "level")

 	OntologyLevel[,1] <- ontology

@@ -700,19 +700,19 @@ createspecifictable <- function(background,myInterestedGenes,ontology,

 	colnames(final.table) <- c("ontology", "level","GO.ID", "term", "annotated",

 														 "significant", "expected",

 														 "pv.fisher", "pv.fisher.BH")

-	

+

 	return (final.table)

 }

-computeGeneListEnrichment <- 

+computeGeneListEnrichment <-

 		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	

+		# 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)){

@@ -720,27 +720,27 @@ computeGeneListEnrichment <-

 			regulatory.elements.counts <- regulated.counts

 		}

 		else {

-			# we need our default regulatory element data contained 

+			# we need our default regulatory element data contained

 			# in tRanslatome dataset, so load it

 			data(tRanslatomeSampleData, envir=environment())

 		}

-	

+

 		enrichments <- c()

-		

+

 		for (i in c(1:nrow(regulatory.elements.regulated))) {

 			regulated.list <- strsplit(

-								as.character(regulatory.elements.regulated[i,2]), 

+								as.character(regulatory.elements.regulated[i,2]),

 								split=",")[[1]]

-			

+

 			regel.name <- as.character(regulatory.elements.regulated[i,1])

 			regelIdx <- which(regulatory.elements.counts[,1] == regel.name)

 			regel.regulated <- as.integer(regulatory.elements.counts[regelIdx,2])

 			regel.nonregulated <- as.integer(regulatory.elements.counts[regelIdx,3])

-			

+

 			# get genes from input list regulated by this regulator

 			regel.input.regulated <- DEGs.genes[

-																		which(DEGs.genes %in% regulated.list)] 

-			

+																		which(DEGs.genes %in% regulated.list)]

+

 			# build the contingency table for fisher test

 			cont.table <- matrix(nrow=2, ncol=2)

 			# number of input genes regulated by this element

@@ -750,27 +750,33 @@ computeGeneListEnrichment <-

 			# number of non-input genes regulated by this element

 			cont.table[2,1] <- as.integer(regel.regulated) - cont.table[1,1]

 			# number of non-input genes NOT regulated by this element

-			cont.table[2,2] <- (regel.regulated + regel.nonregulated) - 

+			cont.table[2,2] <- (regel.regulated + regel.nonregulated) -

 												 cont.table[2,1]

-			

+

 			# compute the fisher test p-value and report the regulator only if

 			# the enrichment p-value is below the given significance threshold

 			pvalue <- fisher.test(cont.table)$p.value

-			if (pvalue <= significance.threshold) 

-				enrichments <- rbind(enrichments, c(regel.name, level.label, 

+			if (pvalue <= significance.threshold)

+				enrichments <- rbind(enrichments, c(regel.name, level.label,

 														 cont.table[1,1],

 														 paste(regel.input.regulated, collapse=","),

 														 pvalue))

-		} 

-	

-	colnames(enrichments) <- c("ID", "level", "number","list","pv.fisher")															 	

+		}

+

+	colnames(enrichments) <- c("ID", "level", "number","list","pv.fisher")

 	# if requested, adjust the p-value for multiple testing and add

-	# the corrected p-value to the enrichments table

-	if (mult.cor)

-		enrichments <- cbind(enrichments, 

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

+	# the corrected p-value to the enrichments table, and end by

+	# filtering by significance threshold on adjusted p-value

+	if (mult.cor) {

+		enrichments <- cbind(enrichments,

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

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

-												

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

+		enrichments <- enrichments[which(enrichments[,6] <= significance.threshold),]

+		return(as.data.frame(enrichments[order(as.numeric(enrichments[,6])),],

 											stringsAsFactors=F))

+	}

+	else {

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

+											stringsAsFactors=F))

+	}

 }

\ No newline at end of file