comparison GO_MWU.R @ 1:f7287f82602f draft

"planemo upload commit 486235d6560c9e95bd42152ad19bf7c3941cdc1b"

0:91261b42c07e

    source(paste(base_dir, fname, sep = "/"))
}

source_local("gomwu.functions.R")

# It might take a few minutes for MF and BP. Do not rerun it if you just want
# to replot the data with different cutoffs, go straight to gomwuPlot. If you
# change any of the numeric values below, delete the files that were generated
# in previos runs first.

# ----------- Plotting results

# change this to a pdf output
pdf()

results <- gomwuPlot(opt$input, opt$goAnnotations, opt$goDivision,
    # genes with the measure value exceeding this will be counted as "good genes".
    # This setting is for signed log-pvalues. Specify absValue=0.001 if you are doing
    # Fisher's exact test for standard GO enrichment or analyzing a WGCNA module (all non-zero genes = "good genes").

)
# manually rescale the plot so the tree matches the text
# if there are too many categories displayed, try make it more stringent with level1 = 0.05,level2=0.01,level3=0.001.

# text representation of results, with actual adjusted p-values
write.table(results[[1]], "results.tsv", sep = "\t")


# ------- extracting representative GOs

# this module chooses GO terms that best represent *independent* groups of significant GO terms

    if (bestrr$pval[best] <= pcut) {
        annots <- c(annots, sub("\\d+\\/\\d+ ", "", row.names(bestrr)[best]))
    }
}

mwus <- read.table(paste("MWU", opt$goDivision, opt$input, sep = "_"), header = T)
best_GOs <- mwus[mwus$name %in% annots, ]
write.table(best_GOs, "best_go.tsv", sep = "\t")
dev.off()

1:f7287f82602f

    source(paste(base_dir, fname, sep = "/"))
}

source_local("gomwu.functions.R")


nn <- strsplit(opt$input, "[/.]")
if (length(nn[[1]]) == 3) {
    dir <- nn[[1]][1]
    name <- nn[[1]][2]
    ext <- nn[[1]][3]
} else if (length(nn[[1]]) == 2) {
    dir <- "."
    name <- nn[[1]][1]
    ext <- nn[[1]][2]
}
# It might take a few minutes for MF and BP. Do not rerun it if you just want
# to replot the data with different cutoffs, go straight to gomwuPlot. If you
# change any of the numeric values below, delete the files that were generated
# in previos runs first.

# ----------- Plotting results

# change this to a pdf output
pdf(paste0(dir,"/","Rplots.pdf"))

results <- gomwuPlot(opt$input, opt$goAnnotations, opt$goDivision,
    # genes with the measure value exceeding this will be counted as "good genes".
    # This setting is for signed log-pvalues. Specify absValue=0.001 if you are doing
    # Fisher's exact test for standard GO enrichment or analyzing a WGCNA module (all non-zero genes = "good genes").

)
# manually rescale the plot so the tree matches the text
# if there are too many categories displayed, try make it more stringent with level1 = 0.05,level2=0.01,level3=0.001.

# text representation of results, with actual adjusted p-values
write.table(results[[1]], paste0(dir, "/", "results.tsv"), sep = "\t")


# ------- extracting representative GOs

# this module chooses GO terms that best represent *independent* groups of significant GO terms

    if (bestrr$pval[best] <= pcut) {
        annots <- c(annots, sub("\\d+\\/\\d+ ", "", row.names(bestrr)[best]))
    }
}

mwus <- read.table(paste0(dir,"/",paste("MWU", opt$goDivision, name, sep = "_"), ".", ext), header = T)
best_GOs <- mwus[mwus$name %in% annots, ]
write.table(best_GOs, paste0(dir, "/","best_go.tsv"), sep = "\t")
dev.off()