I just start learning bioinformatics from RNA-seq/DEGs. I am trying to replicate an article. I very much appreciate any help. I used salmon in combination with tximport and DESeq2.  PCA plot showed 77%PC1 8%PC2. Apparently, different conditions are mixed and not well separated. Is it healthy to continue this analysis? ![PCA][1]

    library(tidyverse) 
    library(tximport) 
    library(DESeq2)
    
    sample_table <- read.csv("SraTable.txt") %>% arrange(sample_table$Run) %>% select(Run)
    
    sample_table$Run <- c("pre_ischemia1","ischemia1","pre_ischemia2","ischemia2","pre_ischemia3", "ischemia3","pre_ischemia4","ischemia4","pre_ischemia5","ischemia5")
    
    SRR10700832_quant/quant.sf sample_files <- paste0(c("SRR10700832_quant","SRR10700833_quant","SRR10700835_quant","SRR10700836_quant","SRR10700838_quant", "SRR10700839_quant","SRR10700841_quant","SRR10700842_quant","SRR10700844_quant","SRR10700846_quant"), "/quant.sf")
    
    names(sample_files) <- pull(sample_table, Run)
    gene_map <- read.csv("gene_map.csv", col.names = c("enstid", "ensgid")) 
    count_data <- tximport(files = sample_files, type="salmon", tx2gene= gene_map, ignoreTxVersion = TRUE)
    sample_table <- as.data.frame(sample_table)
    sample_table$condition <- factor(rep(c("pre_ischemia", "ischemia"), times=5), levels = c("pre_ischemia", "ischemia"))
    
    deseq_dataset <- DESeqDataSetFromTximport(txi= count_data, colData = sample_table, design = ~condition) 
    deseq_dataset <- DESeq(deseq_dataset)
    vst <- varianceStabilizingTransformation(deseq_dataset)
    
    plotPCA(vst, intgroup='condition')
    results <- results(deseq_dataset)
    
    summary(results)

`res <- results(deseq_dataset)` yielded following;

    out of 38464 with nonzero total read count
    adjusted p-value < 0.1
    LFC > 0 (up)       : 1, 0.0026%
    LFC < 0 (down)     : 0, 0%
    outliers [1]       : 263, 0.68%
    low counts [2]     : 0, 0%
    (mean count < 0)
    [1] see 'cooksCutoff' argument of ?results
    [2] see 'independentFiltering' argument of ?results

There is only 1 gene upregulated, zero down and zero low counts which are not likely.  Which step could be the problem ? I would like to know sanity checkpoint in the entire flow. 

Thank you for your time 

P.S sorry it this hurts your eyes. I couldnt format the code in the post.


  [1]: /media/images/2c5350ce-6c6e-4c8e-afb7-8034d5b2
Hello,

I just played with these data a bit as the salmon quantification directories were available at GEO, so this was no effort.

It seems the authors filtered on p-value based on the methods text...

    The significantly differentially expressed genes (DEGs) between groups were defined at cut-off criteria of |log2 fold-change| ≥ 1 and p-value < 0.05.

...instead of padj. When you check some of the top20 DEGs that they provide in Supp. Table S3 you will see that these "significant" ones only pass the p-value cutoff but all have a padj >>> 0.05, so there indeed simply are no DEGs at standard cutoffs, at least not without correcting for potential batch effects. I did not look into the paper at any detail, but this at least seems to "replicate" the results they have. Not discussing now whether this strategy they chose was good or not.