An_awing_pig 2023-03-28 13:48 采纳率: 100%
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运行WGCNA代码时出现Error in merge$colors : object of type 'closure' is not subsettable


library(WGCNA)
setwd("D:\\julei")
enableWGCNAThreads() 
## 读取txt文件格式数据
WGCNA.fpkm = read.table("ExpData_WGCNA.txt",header=T,
                        comment.char = "",
                        check.names=F)
###############
# 读取csv文件格式
WGCNA.fpkm = read.csv("ExpData_WGCNA.csv", header = T, check.names = F)

dim(WGCNA.fpkm)
names(WGCNA.fpkm)
datExpr0 = as.data.frame(t(WGCNA.fpkm[,-1]))
names(datExpr0) = WGCNA.fpkm$sample;##########如果第一行不是ID命名,就写成fpkm[,1]
rownames(datExpr0) = names(WGCNA.fpkm[,-1])

gsg = goodSamplesGenes(datExpr0, verbose = 3)
gsg$allOK
if (!gsg$allOK)
{
  if (sum(!gsg$goodGenes)>0)
    printFlush(paste("Removing genes:", paste(names(datExpr0)[!gsg$goodGenes], collapse = ", ")))
  if (sum(!gsg$goodSamples)>0)
    printFlush(paste("Removing samples:", paste(rownames(datExpr0)[!gsg$goodSamples], collapse = ", ")))
  # Remove the offending genes and samples from the data:
  datExpr0 = datExpr0[gsg$goodSamples, gsg$goodGenes]
}

##filter
meanFPKM=0.5  ###--过滤标准,可以修改
n=nrow(datExpr0)
datExpr0[n+1,]=apply(datExpr0[c(1:nrow(datExpr0)),],2,mean)
datExpr0=datExpr0[1:n,datExpr0[n+1,] > meanFPKM]
# for meanFpkm in row n+1 and it must be above what you set--select meanFpkm>opt$meanFpkm(by rp)
filtered_fpkm=t(datExpr0)
filtered_fpkm=data.frame(rownames(filtered_fpkm),filtered_fpkm)
names(filtered_fpkm)[1]="sample"
head(filtered_fpkm)
write.table(filtered_fpkm, file="mRNA.filter.txt",
            row.names=F, col.names=T,quote=FALSE,sep="\t")

sampleTree = hclust(dist(datExpr0), method = "average")
pdf(file = "1.sampleClustering.pdf", width = 15, height = 8)
par(cex = 0.6)
par(mar = c(0,6,6,0))
plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 2,
     cex.axis = 1.5, cex.main = 2)
### Plot a line to show the cut
#abline(h = 180, col = "red")##剪切高度不确定,故无红线
dev.off()

traitData = read.table("TraitData.txt",row.names=1,header=T,comment.char = "",check.names=F)
allTraits = traitData
dim(allTraits)
names(allTraits)

## 形成一个类似于表达数据的数据框架
fpkmSamples = rownames(datExpr0)
traitSamples =rownames(allTraits)
traitRows = match(fpkmSamples, traitSamples)
datTraits = allTraits[traitRows,]
rownames(datTraits)
collectGarbage()

sampleTree2 = hclust(dist(datExpr0), method = "average")
# Convert traits to a color representation: white means low, red means high, grey means missing entry
traitColors = numbers2colors(datTraits, signed = FALSE)

pdf(file="2.Sample_dendrogram_and_trait_heatmap.pdf",width=20,height=12)
plotDendroAndColors(sampleTree2, traitColors,
                    groupLabels = names(datTraits),
                    main = "Sample dendrogram and trait heatmap",cex.colorLabels = 1.5, cex.dendroLabels = 1, cex.rowText = 2)
dev.off()


enableWGCNAThreads()
# 设置soft-thresholding powers的数量
powers = c(1:30)
sft = pickSoftThreshold(datExpr0, powerVector = powers, verbose = 5)


cex1=0.9
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=0.8,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")
dev.off()


softPower =sft$powerEstimate

adjacency = adjacency(datExpr0, power = softPower)
TOM = TOMsimilarity(adjacency);
dissTOM = 1-TOM
geneTree = hclust(as.dist(dissTOM), method = "average");
pdf(file="4_Gene clustering on TOM-based dissimilarity.pdf",width=24,height=18)
plot(geneTree, xlab="", sub="", main = "Gene clustering on TOM-based dissimilarity",
     labels = FALSE, hang = 0.04)
dev.off()

minModuleSize = 30
# Module identification using dynamic tree cut:
dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM,
                            deepSplit = 2, pamRespectsDendro = FALSE,
                            minClusterSize = minModuleSize);
table(dynamicMods)

# Convert numeric lables into colors
dynamicColors = labels2colors(dynamicMods)
table(dynamicColors)
# Plot the dendrogram and colors underneath
#sizeGrWindow(8,6)
pdf(file="5_Dynamic Tree Cut.pdf",width=8,height=6)
plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut",
                    dendroLabels = FALSE, hang = 0.03,
                    addGuide = TRUE, guideHang = 0.05,
                    main = "Gene dendrogram and module colors")
dev.off()


MEList = moduleEigengenes(datExpr0, colors = dynamicColors)
MEs = MEList$eigengenes
# Calculate dissimilarity of module eigengenes
MEDiss = 1-cor(MEs);
# Cluster module eigengenes
METree = hclust(as.dist(MEDiss), method = "average")
# Plot the result
#sizeGrWindow(7, 6)
pdf(file="6_Clustering of module eigengenes.pdf",width=7,height=6)
plot(METree, main = "Clustering of module eigengenes",
     xlab = "", sub = "")

######剪切高度可修改
MEDissThres = 0.4  
# Plot the cut line into the dendrogram
abline(h=MEDissThres, col = "red")
dev.off()



mergeCloseModules(datExpr0, dynamicColors, cutHeight = MEDissThres, verbose = 3)
# The merged module colors
mergedColors = merge$colors
# Eigengenes of the new merged modules:
mergedMEs = merge$newMEs
table(mergedColors)


在最后运行出现 Error in merge$colors : object of type 'closure' is not subsettable,有什么办法能解决这个问题吗,查了很多代码都要用到,没法不用,应该也不存在包没有引用和大小写之类的错误

原文来自https://blog.csdn.net/kanghua_du/article/details/129232234

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4条回答 默认 最新

  • 阳光宅男xxb 2023-03-28 15:05
    关注

    直接的错误信息是说merge$colors那里,这个是封闭类型的数据,不能进行子集操作。说白了就是调用方式不对,请检查下你的变量名或函数名有没有写错,比如你的merge$colors这个变量哪里来的,你程序中都没有,这些都要检查下。

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