get_peak_blocks_modules <-
function(dataA,simmat,adjacencyfromsimilarity=FALSE,time_step=3,max.rt.diff=10,outloc,column.rm.index=NA,cor.thresh=NA,
deepsplit=2,minclustsize=30,cutheight=0.2,networktype="unsigned",num_nodes=2){
#fname<-"/Users/karanuppal/Documents/Emory/JonesLab/Projects/NIST_QSTD_60K/apLCMS_with_xMSanalyzer_merged_data/apLCMS_feature_list_at_p1_U_p2cor0.7_CV100.txt"
#setwd("/Users/karanuppal/Documents/Emory/JonesLab/Projects/NIST_QSTD_60K/apLCMS_with_xMSanalyzer_merged_data/")
#fname<-"/Users/karanuppal/Documents/Emory/JonesLab/Projects/NIST_QSTD_60K/XCMS/xcmsCW_snthresh3step0.1mzdiff-0.001max50bw10ppm10.txt"
#setwd("/Users/karanuppal/Documents/Emory/JonesLab/Projects/NIST_QSTD_60K/XCMS/")
#setwd(outloc)
#time_step<-3
#dataA<-read.table(fname,sep="\t",header=TRUE)
#dataexpA_comp<-dataA
cnames<-colnames(dataA)
cnames[1]<-"mz"
cnames[2]<-"time"
colnames(dataA)<-as.character(cnames)
data_mzrt<-dataA[,c(1:2)]
#1:3,6:11
if(is.na(column.rm.index)==FALSE){
dataA<-dataA[,-c(column.rm.index)]
}
feat_inf<-paste(dataA[,1],dataA[,2],sep="_")
dataA<-dataA[,-c(1:2)]
data_m<-t(dataA)
allowWGCNAThreads()
multiExpr = vector(mode = "list", length = 1)
multiExpr[[1]] = list(data = as.data.frame(data_m));
powers = c(c(1:10), seq(from = 12, to=20, by=2))
sft = pickSoftThreshold(data=data_m, dataIsExpr=TRUE,powerVector = powers, verbose = 0)
power_val=sft$powerEstimate
net = blockwiseModules(
datExpr=data_m, power = power_val, minModuleSize = minclustsize, deepSplit = deepsplit,
pamRespectsDendro = FALSE,
numericLabels = TRUE,saveTOMs = TRUE, verbose = 0,
corType = "pearson",networkType = networktype,nThreads=num_nodes)
consMEs = net$multiMEs;
moduleLabels = net$colors;
# Convert the numeric labels to color labels
moduleColors = labels2colors(moduleLabels)
consTree = net$dendrograms[[1]];
save(consMEs, moduleLabels, moduleColors, consTree, file = "Consensus-NetworkConstruction-auto_min30.RData")
mod_list<-moduleLabels
t1<-table(mod_list)
mod_names<-names(t1)
mod_names<-as.numeric(mod_names)
time_mult_fact<-1
diffmatC<-{}
dataA<-cbind(data_mzrt,dataA)
dataA<-as.data.frame(dataA)
d1<-density(dataA$time,bw="nrd",from=min(dataA$time),to=(10+max(dataA$time,na.rm=TRUE)))
# time_step<-3
time_step<-abs(d1$x[1]-d1$x[time_step+1])
t1<-d1$x
time_step<-1*time_step
diffmatB<-{}
for(i in 1:length(mod_names)){
#groupB_res<-sapply(1:length(mod_names),function(i){
groupA_num<-mod_names[i]
subdata<-dataA[which(moduleLabels==groupA_num),]
#subdata<-dataA[which(m1==groupA_num),]
subdata<-subdata[order(subdata$time),]
groupB<-group_by_rt(subdata,time_step,max.rt.diff=max.rt.diff,groupnum=groupA_num)
diffmatB<-rbind(diffmatB,groupB)
}
return(diffmatB)
}
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