net = blockwiseModules(datExpr, power = softPower,
TOMType = "unsigned", minModuleSize = 30,
reassignThreshold = 0, mergeCutHeight = 0.25,
numericLabels = TRUE, pamRespectsDendro = FALSE,
saveTOMs = TRUE,
saveTOMFileBase = "BCell_TOM",
verbose = 3)
Calculating module eigengenes block-wise from all genes
Flagging genes and samples with too many missing values...
..step 1
....pre-clustering genes to determine blocks..
Projective K-means:
..k-means clustering..
..merging smaller clusters...
Block sizes:
gBlocks
1 2 3
4947 3284 3053
..Working on block 1 .
TOM calculation: adjacency..
..will use 11 parallel threads.
Fraction of slow calculations: 0.000000
..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.
..saving TOM for block 1 into file BCell_TOM-block.1.RData
....clustering..
....detecting modules..
....calculating module eigengenes..
....checking kME in modules..
..removing 326 genes from module 1 because their KME is too low.
..removing 226 genes from module 2 because their KME is too low.
..removing 3 genes from module 4 because their KME is too low.
..removing 4 genes from module 5 because their KME is too low.
..Working on block 2 .
TOM calculation: adjacency..
..will use 11 parallel threads.
Fraction of slow calculations: 0.000000
..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.
..saving TOM for block 2 into file BCell_TOM-block.2.RData
....clustering..
....detecting modules..
....calculating module eigengenes..
....checking kME in modules..
..removing 198 genes from module 1 because their KME is too low.
..removing 25 genes from module 2 because their KME is too low.
..removing 32 genes from module 3 because their KME is too low.
..removing 7 genes from module 4 because their KME is too low.
..removing 1 genes from module 5 because their KME is too low.
..removing 6 genes from module 6 because their KME is too low.
..removing 6 genes from module 7 because their KME is too low.
..removing 11 genes from module 9 because their KME is too low.
..removing 6 genes from module 10 because their KME is too low.
..removing 1 genes from module 17 because their KME is too low.
..Working on block 3 .
TOM calculation: adjacency..
..will use 11 parallel threads.
Fraction of slow calculations: 0.000000
..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.
..saving TOM for block 3 into file BCell_TOM-block.3.RData
....clustering..
....detecting modules..
....calculating module eigengenes..
....checking kME in modules..
..removing 143 genes from module 1 because their KME is too low.
..removing 33 genes from module 2 because their KME is too low.
..removing 48 genes from module 3 because their KME is too low.
..removing 44 genes from module 4 because their KME is too low.
..removing 22 genes from module 5 because their KME is too low.
..removing 18 genes from module 6 because their KME is too low.
..removing 2 genes from module 7 because their KME is too low.
..removing 2 genes from module 9 because their KME is too low.
Error in (new("standardGeneric", .Data = function (x, y = NULL, use = "everything", : unused arguments (weights.x = NULL, weights.y = NULL, cosine = FALSE)
Traceback:
- blockwiseModules(datExpr, power = softPower, TOMType = "unsigned",
. minModuleSize = 30, reassignThreshold = 0, mergeCutHeight = 0.25,
. numericLabels = TRUE, pamRespectsDendro = FALSE, saveTOMs = TRUE,
. saveTOMFileBase = "SLE_BCell_TOM", verbose = 3) - do.call(match.fun(corFnc), c(list(datExpr[, goodLabels != 0],
. AllMEs), if (corFncAcceptsWeights) list(weights.x = if (haveWeights) weights[,
. goodLabels != 0] else NULL, weights.y = NULL) else NULL,
. corOptions))
#############################################
在biostas問答版的帖子中就有高手做出了解答：## Error in (function...) thrown by WGCNA tutorial (R)
原因為：WGCNA與其他軟件包之間存在沖突隧哮。WGCNA中的cor函數(shù)與R中自帶的cor在命名空間上有沖突朽缎。
解決方法為：在使用該函數(shù)之前暫時重新分配功能锋玲，見下方正確用法
cor <- WGCNA::cor
net = blockwiseModules(datExpr, power = 6,
TOMType = "unsigned", minModuleSize = 30,
reassignThreshold = 0, mergeCutHeight = 0.25,
numericLabels = TRUE, pamRespectsDendro = FALSE,
saveTOMs = TRUE,
saveTOMFileBase = "femaleMouseTOM",
verbose = 3)
cor<-stats::cor
As correctly answered by multiple people here, the problem is that WGCNA has its own function "cor" and this correlates in the namespace with "cor" from the package stats.
Rather than loading and unloading modules or restarting R one might as well temporarily re-assign the function:
