Hi,

I am getting this error while running TOMplot.

TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")
Error in .heatmap(as.matrix(dissim), Rowv = as.dendrogram(dendro, hang = 0.1),  :
  row dendrogram ordering gave index of wrong length

geneTree and plotTOM were made exactly the way as described in the manual https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html

 Here is the complete code:


library(WGCNA)
femData = read.csv("lg2FPKM.csv");
dim(femData);
names(femData);
datExpr0 = as.data.frame(t(femData[, -1]))
names(datExpr0) = femData$substanceBXH;
rownames(datExpr0) = names(femData)[-1];
gsg = goodSamplesGenes(datExpr0, verbose = 3);
gsg$allOK

sampleTree = hclust(dist(datExpr0), method = "average");
# Plot the sample tree: Open a graphic output window of size 12 by 9 inches
# The user should change the dimensions if the window is too large or too small.
sizeGrWindow(12,9)
#pdf(file = "Plots/sampleClustering.pdf", width = 12, height = 9);
par(cex = 0.6);
par(mar = c(0,4,2,0))
plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 1.5, cex.axis = 1.5, cex.main = 2)

Plot a line to show the cut
abline(h = 15, col = "red");
# Determine cluster under the line
clust = cutreeStatic(sampleTree, cutHeight = 15, minSize = 10)
table(clust)
# clust 1 contains the samples we want to keep.
keepSamples = (clust==0)
datExpr = datExpr0[keepSamples, ]
nGenes = ncol(datExpr)
nSamples = nrow(datExpr)

traitData = read.csv("ClinicalTraits.csv");
dim(traitData)
names(traitData)

#allTraits = traitData[, -c(31, 16)];
allTraits = traitData[, c(2, 3,4) ];
dim(allTraits)
names(allTraits)

femaleSamples = rownames(datExpr);
traitRows = match(femaleSamples, allTraits$Pig);
datTraits = allTraits[traitRows, -1];
rownames(datTraits) = allTraits[traitRows, 1];
collectGarbage()

# Re-cluster samples
sampleTree2 = hclust(dist(datExpr), method = "average")
# Convert traits to a color representation: white means low, red means high, grey means missing entry
traitColors = numbers2colors(datTraits, signed = FALSE);
# Plot the sample dendrogram and the colors underneath.
plotDendroAndColors(sampleTree2, traitColors, groupLabels = names(datTraits), main = "Sample dendrogram and trait heatmap")


lnames = load(file = "FemaleLiver-01-dataInput.RData");
lnames
powers = c(c(1:10), seq(from = 12, to=20, by=2))
# Call the network topology analysis function
sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)
# Plot the results:
sizeGrWindow(9, 5)
par(mfrow = c(1,2));
cex1 = 0.9;

# Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2], xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n", main = paste("Scale independence")); text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],     labels=powers,cex=cex1,col="red");
# this line corresponds to using an R^2 cut-off of h
abline(h=0.90,col="red")
# Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[,1], sft$fitIndices[,5],
     xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
     main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")

net = blockwiseModules(datExpr, power = 6,
                       TOMType = "unsigned", minModuleSize = 30,
                       reassignThreshold = 0, mergeCutHeight = 0.25,
                       numericLabels = TRUE, pamRespectsDendro = FALSE,
                       saveTOMs = TRUE,
                       saveTOMFileBase = "femaleMouseTOM",
                       verbose = 3)

# open a graphics window
sizeGrWindow(12, 9)
# Convert labels to colors for plotting
mergedColors = labels2colors(net$colors)
# Plot the dendrogram and the module colors underneath
plotDendroAndColors(net$dendrograms[[1]], mergedColors[net$blockGenes[[1]]],
                    "Module colors",
                    dendroLabels = FALSE, hang = 0.03,
                    addGuide = TRUE, guideHang = 0.05)

moduleLabels = net$colors
moduleColors = labels2colors(net$colors)
MEs = net$MEs;
geneTree = net$dendrograms[[1]];
save(MEs, moduleLabels, moduleColors, geneTree,
     file = "FemaleLiver-02-networkConstruction-auto.RData")

lnames = load(file = "FemaleLiver-01-dataInput.RData");
#The variable lnames contains the names of loaded variables.
lnames
# Load network data saved in the second part.
lnames = load(file = "FemaleLiver-02-networkConstruction-auto.RData");
lnames
enableWGCNAThreads()
nGenes = ncol(datExpr)
nSamples = nrow(datExpr)

# Calculate topological overlap anew: this could be done more efficiently by saving the TOM
# calculated during module detection, but let us do it again here.
dissTOM = 1-TOMsimilarityFromExpr(datExpr, power = 6);
# Transform dissTOM with a power to make moderately strong connections more visible in the heatmap
plotTOM = dissTOM^7;
# Set diagonal to NA for a nicer plot
diag(plotTOM) = NA;
# Call the plot function
sizeGrWindow(9,9)
TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")

Could please help me in fixing this error.

I tried looking through Search engines but it was not helpful.

Thank you

Hi,

The reduction in number of genes from 12000 to 3500 did the trick for me. I still don't know the reason but will wait for the authors to reply on this.