Error in serialize(data, node$con) : ignoring SIGPIPE signal

[hahamark-learn]

hello, I get the same error. I check the node number in the tree, the number isn't abnormally low compared to the tip number.

the code like this

----bMPD bin i=15 in 435 ---- Tue Aug 1 21:42:18 2023 Now calculating observed betaMPD. Begin at Tue Aug 1 21:42:18 2023. Please wait... Now randomizing by parallel computing. Begin at Tue Aug 1 21:43:31 2023. Please wait... Now fixing special cases. Begin at Tue Aug 1 21:44:54 2023. Please wait... All match very well. Now calculating observed MPD. Begin at Tue Aug 1 21:44:55 2023. Please wait... Error in makePSOCKcluster(names = spec, ...) : Cluster setup failed. 15 of 15 workers failed to connect. Error in makePSOCKcluster(names = spec, ...) : Cluster setup failed. 14 of 15 workers failed to connect. Now randomizing by parallel computing. Begin at Tue Aug 1 21:47:54 2023. Please wait... Error in serialize(data, node$con) : ignoring SIGPIPE signal

Can you give me some advices?

[hahamark-learn]

otu <- read.csv("C:\Users\DELL\Desktop\Abundant\16s-otu.csv",row.names = 1,header = T) comm <- t(otu) tree <- read.tree("C:\Users\DELL\Desktop\Abundant\16s-fasttree") class <- read.csv("C:\Users\DELL\Desktop\Abundant\16s-class.csv",row.names = 1,header = T,sep = "\t") treat <- read.csv("C:\Users\DELL\Desktop\Abundant\site-group",sep="\t",row.names = 1,header = T) save.wd=("C:\Users\DELL\Desktop\Abundant\") # please change to the folder you want to save the pd.big output. nworker=20 # parallel computing thread number rand.time=10 # usually use 1000 for real data. bin.size.limit=40 setwd(save.wd) if(!file.exists("pd.desc")) { pd.big=iCAMP::pdist.big(tree = tree, wd=save.wd, nworker = nworker) }else{ pd.big=list() pd.big$tip.label=read.csv(paste0(save.wd,"/pd.taxon.name.csv"),row.names = 1,stringsAsFactors = FALSE)[,1] pd.big$pd.wd=save.wd pd.big$pd.file="pd.desc" pd.big$pd.name.file="pd.taxon.name.csv" } icamp.out=iCAMP::icamp.big(comm=comm,tree=tree,pd.desc = pd.big$pd.file, pd.spname=pd.big$tip.label, pd.wd = pd.big$pd.wd, rand=rand.time, nworker=nworker, bin.size.limit=bin.size.limit, sig.index = "SES.RC",phylo.metric ="bNRI")

[DaliangNing]

Please send me (ningdaliang@ou.edu) your input files to debug.

[hahamark-learn]

i sent the raw data to you, please check it.

[DaliangNing]

I am testing your data with the R code below and the latest version of iCAMP (v1.6.4, available in https://github.com/DaliangNing/iCAMP1/tree/master/RPackage/AllVersions). Till now, it is running bNRI in the 141st bin in 317 bins; no error yet.

R code: https://github.com/DaliangNing/iCAMP1/blob/master/Examples/Debug/20230908.YL.icamp.test.r

Some notes: (1) Your OTU table has different sequence numbers in different samples. We usually rarefy all samples to the same sequence number. If your research requires to use OTU table without rarefaction, please skip the step 3.2 in the above R code. (2) rand.time is set as 20 for a quick test. Generally, use 1000. If too time-consuming, 200 may be acceptable. (3) Please check the example code for more options and steps https://github.com/DaliangNing/iCAMP1/blob/master/Examples/SimpleOTU/icamp.test.r

[DaliangNing]

Hope your problem is solved. If no more questions, I will close this issue soon.