WGCNA module consensus

Question

[philipbody]

Hi I am using WGCNA to create a consensus network from 4 different gene expression data sets. I want to have at least 25% consensus genes between gene expression data sets.

Thank’s

[WouterDeCoster]

And what's your question?

[philipbody]

hi my question is :Is it possible to have the command line that allows at least 25% of consensus genes between data sets,? If yes how ? Thank's

net = blockwiseConsensusModules( multiExpr, power = 6, minModuleSize = 30, minCoreKME = 0.25 ,deepSplit = 2,maxBlockSize = 6000, pamRespectsDendro = FALSE, mergeCutHeight = 0.25, numericLabels = TRUE, minKMEtoStay = 0.25, saveTOMs = TRUE, verbose = 5)

names(net)

Clustering

consTree = hclust(as.dist(1-consensusTOM), method = "average");

We like large modules, so we set the minimum module size relatively high:

minModuleSize = minModuleSize = 30; minCoreKME = 0.25;

Module identification using dynamic tree cut:

unmergedLabels = cutreeDynamic(dendro = consTree, distM = 1-consensusTOM, deepSplit = 2, cutHeight = 0.995, minClusterSize = minModuleSize, pamRespectsDendro = FALSE ); unmergedColors = labels2colors(unmergedLabels)

bnet = blockwiseConsensusModules( multiExpr, maxBlockSize = 6000, power = 6, minModuleSize = 30, minCoreKME = 0.25, deepSplit = 2, pamRespectsDendro = FALSE, mergeCutHeight = 0.25, numericLabels = TRUE, minKMEtoStay = 0, saveTOMs = TRUE, verbose = 5)

[philipbody]

hi my question is :Is it possible to have the command line that allows at least 25% of consensus genes between data sets,? If yes how ? Thank's

net = blockwiseConsensusModules( multiExpr, power = 6, minModuleSize = 30, minCoreKME = 0.25 ,deepSplit = 2,maxBlockSize = 6000, pamRespectsDendro = FALSE, mergeCutHeight = 0.25, numericLabels = TRUE, minKMEtoStay = 0.25, saveTOMs = TRUE, verbose = 5)

names(net)

Clustering

consTree = hclust(as.dist(1-consensusTOM), method = "average");

We like large modules, so we set the minimum module size relatively high:

minModuleSize = minModuleSize = 30; minCoreKME = 0.25;

Module identification using dynamic tree cut:

unmergedLabels = cutreeDynamic(dendro = consTree, distM = 1-consensusTOM, deepSplit = 2, cutHeight = 0.995, minClusterSize = minModuleSize, pamRespectsDendro = FALSE ); unmergedColors = labels2colors(unmergedLabels)

bnet = blockwiseConsensusModules( multiExpr, maxBlockSize = 6000, power = 6, minModuleSize = 30, minCoreKME = 0.25, deepSplit = 2, pamRespectsDendro = FALSE, mergeCutHeight = 0.25, numericLabels = TRUE, minKMEtoStay = 0, saveTOMs = TRUE, verbose = 5)