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  • Olha
    Junior Member
    • Sep 2015
    • 6

    #1

    edgeR doesn't produce output files

    Dear SEQanswer community,

    I run edgeR analysis for 8 samples (4-leukemia patients, 4-healthy donors)
    I want to perform normalization, estimate mean-variance relationship and test differential expression of the genes.
    Before I prepared input data in HTSeq software. So I got 8 separate txt files, each with 2 columns: 1st - for gene name, 2nd - for read counts. The path to the folder is the following: /home/olha/test_dataset/

    Here is my script:

    Code:
    > library(edgeR)
    Loading required package: limma
    > samples <- matrix(c("A15","blood","ART_LOS","ART_blood",              
                        "A17","blood","ART_LOS","ART_blood",                
                        "A18","blood","ART_LOS","ART_blood",  
                        "A19","blood","ART_LOS","ART_blood",
     		    "H11","blood","AI_control","AI_blood",                  
                        "H12","blood","AI_control","AI_blood",                  
                        "H13","blood","AI_control","AI_blood",
                        "H15","blood","AI_control","AI_blood"),
                      nrow = 8, ncol = 4, byrow = TRUE, 
                      dimnames = list(c(1:8), 
                                   c("library_name","organ","condition","group_LOS_vs_control")))                    
    + samples <- as.data.frame (samples, row.names = NULL, 
                              optional = FALSE, stringAsFactors = default.stringAsFactors())  
    + counts <- readDGE(samples$library_name, 
                      path = "/home/olha/test_dataset/",
                      columns=c(1,2), group = samples$group_LOS_vs_control, header = FALSE)    
    + colnames(counts) <- samples$library_name
    + dim(counts)
    + counts$samples
    + noint <- rownames(counts) %in% c('__no_feature','__ambiguous','__too_low_aQual','__not_aligned','__alignment_not_unique')
    + cpms <- cpm(counts)
    + keep <- rowSums (cpms > 1) >= 4 & !noint
    + counts <- counts[keep,]
    + counts <- DGEList(counts=counts,group = samples$group_ALL_vs_control)
    + counts <- calcNormFactors(counts)
    + counts$samples
    + counts
    + pdf(file = 'HCB_ALL.pdf', width = 9, height = 6)
    + plotMDS(counts,
            labels = c('A15','A17','A18','A19','H11','H12','H13','H15'), 
            xlab = 'Dimension 1', 
            ylab = 'Dimension 2', 
            asp = 6/9,
            cex = 0.8,
            main = 'Multidimentional scaling plot of blood')            
    + par(cex.axis =0.6, cex.lab = 0.6, cex.main = 1)
    + dev.off()
    + counts <- estimateCommonDisp(counts, verbos = TRUE)
    + counts <- estimateTagwiseDisp(counts)
    + pdf(file = 'ALL.pdf', width = 7, height = 7)
    + plotBCV(counts,
            main = 'Biological coefficient of variation vs. mean log CPM of brain')+ 
    + par(cex.axis =0.6, cex.lab = 0.6, cex.main = 1)
    + dev.off()
    + pdf(file = 'Mean_variance_relationships_blood_simple.pdf', width = 7, height = 7)
    plotMeanVar(counts, show.tagwise.vars =TRUE, NBline= TRUE,
                main = 'Mean_variance relationships of blood')
    par(cex.axis =0.6, cex.lab = 0.6, cex.main = 1)
    dev.off()        
    + de <- exactTest(counts)
    + tt <- topTags(de, n=nrow(counts), sort.by = 'logFC')
    head(tt$table)  
    + nc <- cpm(counts, normalized.lib.sizes = TRUE)
    rn <- rownames(tt$table)
    head (nc[rn, order(samples$condition)], 4)    
    + deg <- rn[tt$table$FDR <.01]
    pdf(file = 'differentail_genes_blood_simple.pdf')
    plotSmear(counts, de.tags = deg,
              main = 'Log fold change of expression level in blood: ART_LOS vs. AI')
    abline(h=c(-1, 1), col='blue')
    par(cex.axis =0.6, cex.lab = 0.6, cex.main = 1)
    dev.off()+ +         
    + summary(de <- decideTestsDGE(de))
    + pdf (file = 'test.pdf')
    detags <- rownames(counts)[as.logical(de)]
    plotSmear(counts, de.tags=detags)
    abline(h=c(-1, 1), col='blue')
    dev.off()        
    + write.csv(tt$table, file = 'differential_genes_blood_simple.txt')
    However, edgeR does not generate any files (plots, tables, etc.). I run my analysis in LinuxMint shell using Perl.

    I saw, that after first line instead of ">" sign, the edgeR produce "+" sign.
    How I can cope with this issue?

    Thank You very much for help!

    Olha

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