Hi guys,

When at the end of the analysis I export the results with this code:

write.csv(as.data.frame(resOrdered), 
          file="condition_treated_results.csv")

I have a file containing a list of genes with Log2FC and p-value. let say I have two conditions treated and untreated (6 and 2 ), are the values of gene expression of two replicates (untreated) normalized and compared with the values of the 6 replicates (treated) ? I mean are this taken as a single condition isn't it?

What if I have more conditions? let say 4 different conditions, to create that table 3 will be considered as a unique and one as reference condition?

Thank you

also, to make a heatmap where all the biological replicates cluster together I could take the top 1000 gene that are similar among replicates and different with the reference level but I don't know if that is right and/or DESeq2 can do that, what do you guys think?

Thanks

Take a look at the workflow which describes what shows up in the results table:

https://bioconductor.org/packages/rnaseqGene

To contrast multiple groups, you need to call results() multiple times using the contrast argument. See:

?results

For the last case, you can find these genes by performing a likelihood ratio test. If you have a dds with design ~condition, you can do:

dds <- DESeq(dds, test="LRT", reduced=~1)
res <- results(dds)
heatmap.genes <- head(order(res$padj), n)

where n is the number of genes for the heatmap.