Hello!
I'm currently trying to identify consensus gene sets between two different tissue types, Muscle/PBMC, using WGCNA.
I have two normalized count matrices from DESeq2 for each tissue type. I simply transposed them and made a list as written in the tutorial.
> nSets = 2;
>
> setLabels = c("Muscle", "PBMC")
> shortLabels = c("Muscle", "PBMC")
>
> multiExpr = vector(mode = "list", length = nSets)
>
> multiExpr[[1]] = list(data = as.data.frame(t(cntNorm_muscle)));
> multiExpr[[2]] = list(data = as.data.frame(t(cntNorm_pbmc)));
>
> exprSize = checkSets(multiExpr)
> exprSize
$nSets
[1] 2
$nGenes
[1] 23418
$nSamples
[1] 36 36
$structureOK
[1] TRUE
While following the steps in the tutorial, I encountered a problem that 'cutreeStatic' failed to pick the samples to be kept. nSamples
were 0 0
.
> for (set in 1:nSets)
+ {
+ # Find clusters cut by the line
+ labels <- cutreeStatic(sampleTrees[[set]], cutHeight = cutHeights[set])
+ # Keep the largest one (labeled by the number 1)
+ keep <- (labels==1)
+ multiExpr[[set]]$data <- multiExpr[[set]]$data[keep, ]
+ }
>
> collectGarbage();
> # Check the size of the leftover data
> exprSize = checkSets(multiExpr)
> exprSize
$nSets
[1] 2
$nGenes
[1] 19004
$nSamples
[1] 0 0
$structureOK
[1] TRUE
I sat the cutheight high enough to cover all samples still I got 0 0
samples.
I also check the cutreeStatic results and found none of samples were label with 1
.
How come WGCNA couldn't cut the trees as intended? Can anyone help me?
> cutreeStatic(sampleTrees[[1]], cutHeight = cutHeights[1])
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> cutreeStatic(sampleTrees[[2]], cutHeight = cutHeights[2])
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
FYI,
> cutHeights
[1] 6e+05 6e+05
> sampleTrees[[1]]$height
[1] 16484.18 25836.46 32216.74 35307.74 35905.62 37133.48 37950.88 41846.43 43549.16 55866.53 61818.43 62851.72
[13] 64316.41 64653.67 65689.82 69606.64 70672.40 73954.10 76376.41 85008.73 86168.49 90321.42 91414.92 112718.35
[25] 115076.76 118800.65 126296.05 130568.74 149581.57 152108.47 165931.89 188281.41 238617.13 287446.07 585708.12
> sampleTrees[[2]]$height
[1] 16499.12 24565.24 25181.20 28051.09 35179.36 36054.75 40314.80 47953.41 50462.81 50464.60 53223.79
[12] 53403.29 58119.68 59881.10 75830.31 88511.20 90003.12 90048.38 101188.62 105594.16 106010.83 107061.76
[23] 112152.86 112711.72 134007.36 144897.14 172335.21 197200.68 203269.72 252626.52 281625.70 420075.13 512717.55
[34] 1190471.26 3384086.76