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Commit ad2f3310 authored by Stevenn Volant's avatar Stevenn Volant
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correction bug diag plot

parent c4e30c3b
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internal.R
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......@@ -171,6 +171,8 @@ CheckCountsTable <- function(counts)
{
counts= NULL
CheckTarget = FALSE
CT_noNorm = NULL
normFactors = NULL
## Counts and taxo tables
CT = dataInput$counts
......@@ -187,17 +189,16 @@ CheckCountsTable <- function(counts)
## Order CT according to the target
CT = OrderCounts(counts=CT,labels=labels)$CountsOrder
CT_noNorm = CT
# ind0 = which(rowSums(CT)==0)
# if(length(ind0)>0) CT = CT[-ind0,]
## Counts normalisation
print(head(CT))
print(head(target))
dds <- DESeqDataSetFromMatrix(countData=CT, colData=target, design=design)
dds <- estimateSizeFactors(dds,locfunc=eval(as.name(input$locfunc)))
normFactors = sizeFactors(dds)
print(normFactors)
print(colSums(CT))
normFactors = sizeFactors(dds)
CT = as.data.frame(round(counts(dds, normalized = TRUE)))
ordOTU = order(rownames(taxo))
indOTU_annot = which(rownames(CT)%in%rownames(taxo))
......@@ -205,9 +206,9 @@ print(colSums(CT))
if(taxoSelect=="OTU") counts = counts_annot
else{
taxoS = taxo[ordOTU,taxoSelect]
counts = aggregate(counts_annot,by=list(Taxonomy = taxoS),sum)
rownames(counts)=counts[,1];counts=counts[,-1]
taxoS = taxo[ordOTU,taxoSelect]
counts = aggregate(counts_annot,by=list(Taxonomy = taxoS),sum)
rownames(counts)=counts[,1];counts=counts[,-1]
}
## Ordering the counts table according to the target labels
......@@ -216,7 +217,7 @@ print(colSums(CT))
normFactors = tmpOrder$normFactorsOrder
CheckTarget = TRUE
}
return(list(counts=counts,CheckTarget=CheckTarget,normFactors=normFactors))
return(list(counts=counts,CheckTarget=CheckTarget,normFactors=normFactors, CT_noNorm=CT_noNorm))
}
## Order the counts
......@@ -238,7 +239,7 @@ print(colSums(CT))
## Get the dds object of DESeq2
Get_dds_object <- function(input,counts,target,design,normFactorsOTU)
Get_dds_object <- function(input,counts,target,design,normFactorsOTU,CT_noNorm)
{
dds <- DESeqDataSetFromMatrix(countData=counts, colData=target, design=design)
......@@ -249,7 +250,7 @@ print(colSums(CT))
sizeFactors(dds)<- normFactors
dds <- estimateDispersions(dds, fitType=input$fitType)
dds <- nbinomWaldTest(dds)
return(list(dds = dds,counts=counts,target=target,design=design,normFactors = normFactorsOTU))
return(list(dds = dds,counts=counts,target=target,design=design,normFactors = normFactorsOTU,CT_noNorm=CT_noNorm))
}
## Get the design according to the input
......@@ -275,25 +276,31 @@ print(colSums(CT))
counts = resDiff$counts
target = resDiff$target
normFactors = resDiff$normFactors
CT_noNorm = resDiff$CT_noNorm
group = as.data.frame(target[,VarInt])
rownames(group) = rownames(target)
res = NULL
if(ncol(group)>0 && nrow(counts)>0)
{
## If more than 4 levels for one factor
if(length(VarInt)>1) maxFact =max(sapply(group,FUN=function(x) length(levels(x))))
else maxFact = length(levels(group))
if(maxFact>=4) colors = rainbow(maxFact)
if(input$DiagPlot=="barplotTot") res = barplotTot(input,counts,group = group, col=colors)
if(input$DiagPlot=="barplotNul") res = barPlotNul(input,counts, group = group, col=colors)
if(input$DiagPlot=="densityPlot") res = densityPlotTot(input,counts, group = group, col=colors)
if(input$DiagPlot=="MajTax") res = majTaxPlot(input,counts, group = group, col=colors)
if(input$DiagPlot=="SERE") res = SEREplot(input,counts)
#if(input$DiagPlot=="Sfactors") diagSFactors(input,dds,frame=1)
if(input$DiagPlot=="SfactorsVStot") res = diagSFactors(input,dds,normFactors,CT_noNorm,frame=2)
if(input$DiagPlot=="pcaPlot") res = PCAPlot_meta(input,dds, group, type.trans = input$TransType, col = colors)
if(input$DiagPlot=="pcoaPlot") res = PCoAPlot_meta(input,dds, group)
if(input$DiagPlot=="clustPlot") res = HCPlot(input,dds,group,type.trans=input$TransType)
}
## If more than 4 levels for one factor
if(length(VarInt)>1) maxFact =max(sapply(group,FUN=function(x) length(levels(x))))
else maxFact = length(levels(group))
if(maxFact>=4) colors = rainbow(maxFact)
if(input$DiagPlot=="barplotTot") barplotTot(input,counts,group = group, col=colors)
if(input$DiagPlot=="barplotNul") barPlotNul(input,counts, group = group, col=colors)
if(input$DiagPlot=="densityPlot") densityPlotTot(input,counts, group = group, col=colors)
if(input$DiagPlot=="MajTax") majTaxPlot(input,counts, group = group, col=colors)
if(input$DiagPlot=="SERE") SEREplot(input,counts)
#if(input$DiagPlot=="Sfactors") diagSFactors(input,dds,frame=1)
if(input$DiagPlot=="SfactorsVStot") diagSFactors(input,dds,normFactors,frame=2)
if(input$DiagPlot=="pcaPlot") PCAPlot_meta(input,dds, group, type.trans = input$TransType, col = colors)
if(input$DiagPlot=="pcoaPlot") PCoAPlot_meta(input,dds, group)
if(input$DiagPlot=="clustPlot") HCPlot(input,dds,group,type.trans=input$TransType)
return(res)
}
......@@ -538,12 +545,14 @@ print(colSums(CT))
## Get the SERE COEF
SEREcoef<-function(counts)
{
sere <- matrix(NA, ncol = ncol(counts), nrow = ncol(counts))
for (i in 1:ncol(counts)) {
for (j in 1:ncol(counts)) {
counts = as.matrix(counts)
sere <- matrix(0, ncol = ncol(counts), nrow = ncol(counts))
for (i in 1:(ncol(counts)-1)) {
for (j in (i+1):ncol(counts)) {
sere[i, j] <- sigfun_Pearson_meta(counts[, c(i, j)])
}
}
sere=sere+t(sere)
colnames(sere) <- rownames(sere) <- colnames(counts)
sere <- round(sere, digits = 3)
......@@ -553,58 +562,52 @@ print(colSums(CT))
## function for the SERE coef
sigfun_Pearson_meta <- function(observed) {
print("OK1")
laneTotals <- colSums(observed)
total <- sum(laneTotals)
print("OK2")
fullObserved <- observed[rowSums(observed) > 0, ]
fullLambda <- rowSums(fullObserved)/total
fullLhat <- fullLambda > 0
print("OK3")
fullExpected <- outer(fullLambda, laneTotals)
fullKeep <- which(fullExpected > 0)
print(fullKeep)
print(fullExpected)
oeFull <- (fullObserved[fullKeep] - fullExpected[fullKeep])^2/fullExpected[fullKeep]
print(oeFull)
dfFull <- length(fullKeep) - sum(fullLhat != 0)
print(dfFull)
sqrt(sum(oeFull)/dfFull)
}
## Plots of size factors
diagSFactors<-function (input,dds,normFactors,frame=1)
diagSFactors<-function (input,dds,normFactors,CT_noNorm,frame=1)
{
geomeans <- exp(rowMeans(log(counts(dds))))
samples <- colnames(counts(dds))
counts.trans <- log2(counts(dds)/geomeans)
xmin <- min(counts.trans[is.finite(counts.trans)], na.rm = TRUE)
xmax <- max(counts.trans[is.finite(counts.trans)], na.rm = TRUE)
# if(!is.na(input$NbcolSfactors)) parCols = as.numeric(input$NbcolSfactors)
# else parCols = ceiling(ncol(counts.trans)/3)
counts = CT_noNorm
geomeans <- exp(rowMeans(log(counts)))
samples <- colnames(counts)
# counts.trans <- log2(counts/geomeans)
# xmin <- min(counts.trans[is.finite(counts.trans)], na.rm = TRUE)
# xmax <- max(counts.trans[is.finite(counts.trans)], na.rm = TRUE)
#
# parRows = ceiling(ncol(counts.trans)/parCols)
if(frame==1)
{
par(mfrow=c(parRows,parCols))
for (j in 1:ncol(dds)) {
hist(log2(counts(dds)[, j]/geomeans), nclass = 100,
xlab = expression(log[2] ~ (counts/geometric ~ mean)), las = 1, xlim = c(xmin, xmax),
main = paste("Size factors diagnostic - Sample ",samples[j], sep = ""), col = "skyblue")
abline(v = log2(normFactors[j]), col = "red", lwd = 1.5)
}
}
# # if(!is.na(input$NbcolSfactors)) parCols = as.numeric(input$NbcolSfactors)
# # else parCols = ceiling(ncol(counts.trans)/3)
# #
# # parRows = ceiling(ncol(counts.trans)/parCols)
#
# if(frame==1)
# {
# par(mfrow=c(parRows,parCols))
# for (j in 1:ncol(dds)) {
# hist(log2(counts[, j]/geomeans), nclass = 100,
# xlab = expression(log[2] ~ (counts/geometric ~ mean)), las = 1, xlim = c(xmin, xmax),
# main = paste("Size factors diagnostic - Sample ",samples[j], sep = ""), col = "skyblue")
#
# abline(v = log2(normFactors[j]), col = "red", lwd = 1.5)
# }
# }
if(frame==2)
{
plot(normFactors, colSums(counts(dds)), pch = 19, las = 1,
plot(normFactors, colSums(counts), pch = 19, las = 1,
ylab = "Total number of reads", xlab = "Size factors",
main = "Diagnostic: size factors vs total number of reads")
abline(lm(colSums(counts(dds)) ~ normFactors + 0), lty = 2, col = "grey")
abline(lm(colSums(counts) ~ normFactors + 0), lty = 2, col = "grey")
}
}
......@@ -613,10 +616,13 @@ print(colSums(CT))
PCoAPlot_meta <-function (input,dds, group_init,col = c("SpringGreen","dodgerblue","black","firebrick1"), plot = "pcoa")
{
cval=c()
time_set = 0
# Set of shape
shape=c(19,17,15,18)
## Var of interest
VarInt = input$VarInt
## Group
group = as.character(apply(group_init,1,paste, collapse = "-"))
......@@ -632,77 +638,77 @@ print(colSums(CT))
if(length(VarInt)>1) Kval = apply(expand.grid(val,val),1,paste, collapse = "-")
else Kval = val
ind_kept = which(as.character(group)%in%Kval)
## Get the group corresponding to the modalities
group = group[ind_kept]
nb = length(unique((group)))
group = as.factor(group)
## Get the norm data
counts.norm = as.data.frame(round(counts(dds, normalized = TRUE)))
# was removed
counts.norm = counts.norm[,ind_kept]
## Get the distance
dist.counts.norm = vegdist(t(counts.norm), method = input$DistPCOA)
## Do PCoA
pco.counts.norm = dudi.pco(d = dist.counts.norm, scannf = FALSE,nf=3)
## Get eigen values
eigen=(pco.counts.norm$eig/sum(pco.counts.norm$eig))*100
## xlim and ylim of the plot
min = min(pco.counts.norm$li)
max = max(pco.counts.norm$li)
## get condition set
condition_set=val[which(val %in% unique(group_init$condition))]
time_set=val[which(val %in% unique(group_init$time))]
## Colors
if(length(col)<length(condition_set) * length(time_set))# && !input$colorgroup)
{
col = rainbow(length(condition_set) * length(time_set))
}
#else if(length(col)<length(condition_set) * length(time_set) && input$colorgroup){
# col = rep(col[1:length(condition_set)], length(time_set))
#}
print(condition_set)
print(time_set)
if (length(time_set) == 1 && length(condition_set) <= 4){
cval = apply(expand.grid(condition_set,time_set),1,paste, collapse = "-")
cval = sort(cval)
}
print(col)
# to reactivate
#pco.counts.norm$li = pco.counts.norm$li[ind_kept,]
if (plot == "pcoa"){
## Plot axis, label and circles
plot(pco.counts.norm$li[1:2], xlab=paste("PC1 : ",round(eigen[1],1),"%") , ylab=paste("PC2 : ",round(eigen[2],1),"%"),
xlim=c(min+0.25*min,max+0.25*max), ylim=c(min-0.1,max+0.1), cex.axis=1, cex.lab=1,lwd=2, type="n")
# Set different shapes
if(input$labelPCOA == "Group"){
print(cval)
print(length(cval))
if(!is.null(cval)){
for (i in 1:length(cval)){
points(pco.counts.norm$li[which(group==cval[i]),1:2],pch=shape[i],col=col[i], cex=input$cexpoint)
}
s.class(dfxy = pco.counts.norm$li, fac = group, col = col, label = levels(group),
add.plot = TRUE, cpoint = 0, cell=input$cexcircle, clabel=input$cexLabelDiag, cstar = input$cexstar)
}else s.class(dfxy = pco.counts.norm$li, fac = group, col = col, label = levels(group),
add.plot = TRUE, cpoint = input$cexpoint, cell=input$cexcircle, clabel=input$cexLabelDiag, cstar = input$cexstar)
}
else{
s.label(pco.counts.norm$li, clabel = input$cexLabelDiag,boxes=FALSE, add.plot = TRUE)
s.class(dfxy = pco.counts.norm$li, fac = group, col = col, label = levels(group), add.plot = TRUE, cpoint = 0, clabel = 0, cstar = input$cexstar, cell=input$cexcircle)
if(nlevels(group)!=0)
{
## Get the norm data
counts.norm = as.data.frame(round(counts(dds, normalized = TRUE)))
# was removed
counts.norm = counts.norm[,ind_kept]
## Get the distance
dist.counts.norm = vegdist(t(counts.norm), method = input$DistPCOA)
## Do PCoA
pco.counts.norm = dudi.pco(d = dist.counts.norm, scannf = FALSE,nf=3)
## Get eigen values
eigen=(pco.counts.norm$eig/sum(pco.counts.norm$eig))*100
print(eigen)
## xlim and ylim of the plot
min = min(pco.counts.norm$li)
max = max(pco.counts.norm$li)
## get condition set
condition_set=val[which(val %in% unique(group_init$condition))]
time_set=val[which(val %in% unique(group_init$time))]
## Colors
if(length(col)<length(condition_set) * length(time_set))# && !input$colorgroup)
{
col = rainbow(length(condition_set) * length(time_set))
}
}else{
barplot(eigen[1:7], xlab="Dimensions", ylab="Eigenvalues (%)", names.arg = 1:7, col = c(rep("black", 2), rep("grey", 5)), ylim=c(0,max(eigen)+5), cex.axis=1.2, cex.lab=1.4,cex.names=1.2)
}
#else if(length(col)<length(condition_set) * length(time_set) && input$colorgroup){
# col = rep(col[1:length(condition_set)], length(time_set))
#}
if (length(time_set) == 1 && length(condition_set) <= 4){
cval = apply(expand.grid(condition_set,time_set),1,paste, collapse = "-")
cval = sort(cval)
}
# to reactivate
#pco.counts.norm$li = pco.counts.norm$li[ind_kept,]
if (plot == "pcoa"){
## Plot axis, label and circles
plot(pco.counts.norm$li[1:2], xlab=paste("PC1 : ",round(eigen[1],1),"%") , ylab=paste("PC2 : ",round(eigen[2],1),"%"),
xlim=c(min+0.25*min,max+0.25*max), ylim=c(min-0.1,max+0.1), cex.axis=1, cex.lab=1,lwd=2, type="n")
# Set different shapes
if(input$labelPCOA == "Group"){
if(!is.null(cval)){
for (i in 1:length(cval)){
points(pco.counts.norm$li[which(group==cval[i]),1:2],pch=shape[i],col=col[i], cex=input$cexpoint)
}
s.class(dfxy = pco.counts.norm$li, fac = group, col = col, label = levels(group),
add.plot = TRUE, cpoint = 0, cell=input$cexcircle, clabel=input$cexLabelDiag, cstar = input$cexstar)
}else s.class(dfxy = pco.counts.norm$li, fac = group, col = col, label = levels(group),
add.plot = TRUE, cpoint = input$cexpoint, cell=input$cexcircle, clabel=input$cexLabelDiag, cstar = input$cexstar)
}
else{
s.label(pco.counts.norm$li, clabel = input$cexLabelDiag,boxes=FALSE, add.plot = TRUE)
s.class(dfxy = pco.counts.norm$li, fac = group, col = col, label = levels(group), add.plot = TRUE, cpoint = 0, clabel = 0, cstar = input$cexstar, cell=input$cexcircle)
}
}else{
barplot(eigen[1:7], xlab="Dimensions", ylab="Eigenvalues (%)", names.arg = 1:7, col = c(rep("black", 2), rep("grey", 5)), ylim=c(0,max(eigen)+5), cex.axis=1.2, cex.lab=1.4,cex.names=1.2)
}
}
}
### PCA
......@@ -717,10 +723,6 @@ print(colSums(CT))
rv = apply(counts.trans, 1, var, na.rm = TRUE)
pca = prcomp(t(counts.trans[order(rv, decreasing = TRUE),][1:n, ]))
if(plot=="pca")
{
prp <- pca$sdev^2 * 100/sum(pca$sdev^2)
......@@ -818,6 +820,7 @@ print(colSums(CT))
counts_tmp_combined = NULL
prop_tmp_combined = NULL
targetInt = NULL
namesCounts = NULL
## Select a subset within the taxonomy level (default is the 12 most abundant)
nbKept = length(ind_taxo)
Taxonomy = rownames(counts)
......@@ -838,6 +841,7 @@ print(colSums(CT))
{
counts_tmp_combined = aggregate(t(counts_tmp),by=list(targetInt$AllVar),sum)
rownames(counts_tmp_combined) = counts_tmp_combined$Group.1
namesCounts = counts_tmp_combined$Group.1
counts_tmp_combined = as.matrix(counts_tmp_combined[,-1])
}
if(!aggregate)
......@@ -845,8 +849,10 @@ print(colSums(CT))
counts_tmp_combined = t(counts_tmp)
prop_tmp_combined = counts_tmp_combined/colSums(counts)
rownames(counts_tmp_combined) = targetInt$AllVar
namesCounts = targetInt$AllVar
rownames(prop_tmp_combined) = targetInt$AllVar
}
## Ordering the counts
MeanCounts = apply(counts_tmp_combined,2,mean)
ord = order(MeanCounts,decreasing=TRUE)
......@@ -854,7 +860,7 @@ print(colSums(CT))
if(!aggregate) prop_tmp_combined = as.matrix(prop_tmp_combined[,ord])
}
return(list(counts = counts_tmp_combined,targetInt=targetInt,prop=prop_tmp_combined))
return(list(counts = counts_tmp_combined,targetInt=targetInt,prop=prop_tmp_combined,namesCounts=namesCounts))
}
......@@ -872,13 +878,17 @@ print(colSums(CT))
VarInt = input$VisuVarInt
ind_taxo = input$selectTaxoPlot
counts_tmp_combined = GetDataToPlot(resDiff,VarInt,ind_taxo)$counts
tmp_combined = GetDataToPlot(resDiff,VarInt,ind_taxo)
counts_tmp_combined = tmp_combined$counts
nbKept = length(ind_taxo)
SamplesNames = tmp_combined$namesCounts
if(nbKept>1) namesTax = colnames(counts_tmp_combined)
if(nbKept==1) namesTax = ind_taxo
if(!is.null(counts_tmp_combined) && nrow(counts_tmp_combined)>0)
{
counts_tmp_combined = GetDataToPlot(resDiff,VarInt,ind_taxo)$counts
Taxonomy = rownames(counts_tmp_combined)
## Create the data frame for the plot function
dataBarPlot_mat = c()
tmp_mat = matrix(0,ncol=3,nrow=nbKept)
......@@ -887,7 +897,7 @@ print(colSums(CT))
for(i in 1:(nrow(counts_tmp_combined)))
{
## Taxo
tmp_mat[1:nbKept,1] = colnames(counts_tmp_combined)
tmp_mat[1:nbKept,1] = namesTax
## Counts
......@@ -900,7 +910,7 @@ print(colSums(CT))
tmp_counts = c(tmp_counts,tmpProp)
## Meta data
tmp_mat[1:nbKept,3] = as.character(rep(rownames(counts_tmp_combined)[i],nbKept))
tmp_mat[1:nbKept,3] = as.character(rep(SamplesNames[i],nbKept))
## Conbined the sample
dataBarPlot_mat = rbind(dataBarPlot_mat,tmp_mat)
......@@ -917,13 +927,32 @@ print(colSums(CT))
plotd3 <- nvd3Plot(Proportions ~ AllVar | Taxonomy, data = dataBarPlot_mat, type = Sens, id = 'barplotTaxo',height = input$heightVisu,width=input$widthVisu)
plotd3$chart(stacked = TRUE)
##################################
## Same plot in ggplot2 for export
##################################
tax.colors=rep(c("#1f77b4","#aec7e8","#ff7f0e","#ffbb78", "#2ca02c","#98df8a","#d62728","#ff9896","#9467bd","#c5b0d5","#8c564b",
"#c49c94","#e377c2","#f7b6d2","#7f7f7f", "#c7c7c7","#bcbd22","#dbdb8d","#17becf","#9edae5"),ceiling(nbKept/20))
dataBarPlot_mat$Taxonomy = factor(dataBarPlot_mat$Taxonomy,levels = namesTax)
gg= ggplot(dataBarPlot_mat, aes(x=AllVar, y=Proportions, fill=Taxonomy))
gg= gg + geom_bar(stat="identity")
gg= gg + theme_bw()+ scale_fill_manual(values=tax.colors)
gg = gg +theme(panel.grid.minor.x=element_blank(),panel.grid.major.x=element_blank())
if(input$CountsOrProp=="prop") gg = gg+labs(y="Relative abundance (%)",x="")
if(input$CountsOrProp=="counts") gg = gg+labs(y="Abundance",x="")
if(input$SensPlotVisu == "Horizontal") gg = gg + coord_flip()
}
else{
## Pb affichage quand data NULL
dataNull = data.frame(x=c(1,2),y=c(1,2))
plotd3 = nvd3Plot(x ~ y , data = dataNull, type = "multiBarChart", id = 'barplotTaxoNyll',height = input$heightVisu,width=input$widthVisu)
plotd3 = NULL
gg = NULL
}
return(plotd3)
return(list(plotd3=plotd3,gg=gg))
}
......@@ -935,7 +964,7 @@ print(colSums(CT))
######################################################
Plot_Visu_Heatmap <- function(input,resDiff){
Plot_Visu_Heatmap <- function(input,resDiff,export=FALSE){
VarInt = input$VisuVarInt
ind_taxo = input$selectTaxoPlot
......@@ -955,12 +984,11 @@ print(colSums(CT))
## Transpose matrix if Horizontal
if(input$SensPlotVisu=="Horizontal") counts_tmp_combined = t(as.matrix(counts_tmp_combined))
#print(counts_tmp_combined)
#return(heatmap.2(counts_tmp_combined, dendrogram = "none", Rowv = NA, Colv = NA, na.rm = TRUE, density.info="none", margins=c(12,8),trace="none",srtCol=45,
#col = col, scale = input$scaleHeatmap,cexRow = 0.6))
return(d3heatmap(counts_tmp_combined, dendrogram = "none", Rowv = NA, Colv = NA, na.rm = TRUE,
width = input$widthVisu, height = input$heightVisu, show_grid = FALSE, colors = col, scale = input$scaleHeatmap,
cexRow = 0.6))
if(!export) plot = d3heatmap(counts_tmp_combined, dendrogram = "none", Rowv = NA, Colv = NA, na.rm = TRUE,width = input$widthVisu, height = input$heightVisu, show_grid = FALSE, colors = col, scale = input$scaleHeatmap,cexRow = 0.6)
if(export) plot = heatmap.2(counts_tmp_combined, dendrogram = "none", Rowv = NA, Colv = NA, na.rm = TRUE, density.info="none", margins=c(12,8),trace="none",srtCol=45,col = col, scale = input$scaleHeatmap,cexRow = 0.6)
return(plot)
}
......@@ -987,7 +1015,6 @@ print(colSums(CT))
if(!is.null(counts_tmp_combined) && nrow(counts_tmp_combined)>0)
{
Taxonomy = rownames(counts_tmp_combined)
if(input$typeDataBox == "Relative")
{
......@@ -1029,7 +1056,7 @@ print(colSums(CT))
gg = gg + ylab(input$typeDataBox)
if(input$CheckAddPointsBox) gg = gg + geom_point(position=position_jitterdodge(dodge.width=0.9))
if(input$SensPlotVisu=="Horizontal") gg = gg + coord_flip()
if(nbKept>1) gg = gg + facet_wrap(~ Taxonomy)
if(nbKept>1) gg = gg + facet_wrap(~ Taxonomy,scales = input$ScaleBoxplot)
}
return(gg)
......@@ -1250,10 +1277,11 @@ print(colSums(CT))
mcols.add$outlier <- ifelse(mcols(dds)$maxCooks > cooksCutoff, "Yes", "No")
complete.name <- merge(complete.name, mcols.add, by = "Id")
complete[[name]] <- complete.name
up.name <- complete.name[which(complete.name$padj <= alpha & complete.name$betaConv & complete.name$log2FoldChange >= 0), ]
up.name <- complete.name[which(complete.name$padj <= alpha & complete.name$betaConv & complete.name$log2FoldChange>=0), ]
up.name <- up.name[order(up.name$padj), ]
down.name <- complete.name[which(complete.name$padj <= alpha & complete.name$betaConv & complete.name$log2FoldChange <= 0), ]
down.name <- complete.name[which(complete.name$padj<=alpha & complete.name$betaConv & complete.name$log2FoldChange<=0), ]
down.name <- down.name[order(down.name$padj), ]
name <- gsub(" ", "", name)
# keep <- c("FC", "log2FoldChange", "padj")
# complete.complete[, paste(name, keep, sep = ".")] <- complete.name[, keep]
......@@ -1303,7 +1331,7 @@ print(colSums(CT))
}
Plot_Visu_Heatmap_FC <- function(input,BaseContrast,resDiff){
Plot_Visu_Heatmap_FC <- function(input,BaseContrast,resDiff,export=FALSE){
res = NULL
SelContrast = input$ContrastList_table_FC
......@@ -1321,10 +1349,12 @@ print(colSums(CT))
col <- c(colorRampPalette(c("royalblue4","royalblue3","royalblue2","royalblue1","white"))(n = 100),colorRampPalette(c("white", "firebrick1", "firebrick2", "firebrick3", "firebrick4"))(n = 100))
## Transpose matrix if Horizontal
if(input$SensPlotVisu=="Horizontal") log2FC = t(as.matrix(log2FC))
if(!export) res = d3heatmap(log2FC, dendrogram = "row", Rowv = TRUE, Colv = NA, na.rm = TRUE, width = input$widthVisu, height = input$heightVisu, show_grid = FALSE, colors = col, scale = input$scaleHeatmap,cexRow = input$LabelSizeHeatmap,cexCol =input$LabelSizeHeatmap, offsetCol=input$LabelColOffsetHeatmap,offsetRow=input$LabelRowOffsetHeatmap)
if(export) res = heatmap.2(log2FC, dendrogram = "row", Rowv = TRUE, Colv = NA, na.rm = TRUE, width = input$widthVisu, height = input$heightVisu, show_grid = FALSE, colors = col, scale = input$scaleHeatmap,cexRow = input$LabelSizeHeatmap,cexCol =input$LabelSizeHeatmap, offsetCol=input$LabelColOffsetHeatmap,offsetRow=input$LabelRowOffsetHeatmap)
res = d3heatmap(log2FC, dendrogram = "row", Rowv = TRUE, Colv = NA, na.rm = TRUE,
width = input$widthVisu, height = input$heightVisu, show_grid = FALSE, colors = col, scale = input$scaleHeatmap,cexRow = input$LabelSizeHeatmap,cexCol =input$LabelSizeHeatmap,
offsetCol=input$LabelColOffsetHeatmap,offsetRow=input$LabelRowOffsetHeatmap)
}
return(res)
}
......
server.R
View file @ ad2f3310
......@@ -3,6 +3,7 @@ if (!require(rNVD3)) {
install.packages('rNVD3')
library(rNVD3)
}
library(plotly)
if (!require(psych)) {
install.packages('psych')