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60 lines
1.8 KiB
R
60 lines
1.8 KiB
R
# Read the inital feature table in
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feature_table <- read.table("metaxa-feature-table.tsv",
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header=TRUE,
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sep="\t",
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quote="",
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strip.white=TRUE,
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check.names=FALSE)
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# Get the dimensions of the table
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numSamples <- ncol(feature_table) - 1
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numFeatures <- nrow(feature_table)
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# Rearrange taxonomy so biom and QIIME like it
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feature_table$taxonomy <- feature_table$Taxa
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feature_table$Taxa <- NULL
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# Add unique SampleIds for QIIME to work with
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ids <- vector(length = numFeatures)
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for (i in 1:numFeatures){
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# ARCC won't let us install packages, so we have to deal
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# with generating UUIDs ourselves using the code from:
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# https://stackoverflow.com/a/10493590/3922521
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baseuuid <- paste(sample(c(letters[1:6],0:9),30,replace=TRUE),collapse="")
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ids[i] <- paste(
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substr(baseuuid,1,8),
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"-",
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substr(baseuuid,9,12),
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"-",
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"4",
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substr(baseuuid,13,15),
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"-",
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sample(c("8","9","a","b"),1),
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substr(baseuuid,16,18),
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"-",
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substr(baseuuid,19,30),
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sep="",
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collapse=""
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)
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}
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feature_table$'#SampleId' <- ids
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feature_table <- feature_table[c(numSamples+2, 1:(numSamples+1))]
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# Find minimum and maximum rarefaction values
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numCounts <- vector(length=numSamples)
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for (i in 2:(numSamples+1)) {
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numCounts[i-1] <- sum(feature_table[,i])
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}
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minRarefaction <- min(numCounts)
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maxRarefaction <- max(numCounts)
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write.table(minRarefaction, "rarefaction.min.txt",
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row.names=FALSE, col.names=FALSE, quote=FALSE)
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write.table(maxRarefaction, "rarefaction.max.txt",
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row.names=FALSE, col.names=FALSE, quote=FALSE)
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# Write the file out
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write.table(feature_table,
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file="feature-table.tsv",
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sep = "\t",
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quote = FALSE,
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row.names = FALSE) |