Hi, what do you mean by "apart from alignment free vs alignment based reasons" ? Actually that makes lot of difference depending on the type of RNA/genes you are looking for. You can see this recent article where they explain it in detail.

Can you we rely on Fold changes even if we don't have significant q-value?

• If you have good number of replicates in each group, I would still prefer to fix some cut-off with q-value/FDR (not the regular 0.05 but little lenient for FDR/q-value and strict in LogFC cutoff).

If you still like to use only logFC as cut-off, have a look at GFOLD.

I'll just answer this one:

Can you we rely on Fold changes even if we don't have significant q-value?

No, you absolutely can't. High/low log2 fold changes are highly biased toward lowly expressed genes, as you can see in the typical MA-plot below. However, the measure of the expression of those genes is hampered with proportionaly more noise than for highly expressed genes. Such high log2FC doesn't usually reflect a biological reality, hence the need for a treshold on the FDR/p-value that will usually filter out those lowly expressed genes with high log2FC.

There are two main differences between the Kallisto-sleuth pipeline and the 'Ballgown' pipeline.

Sleuth makes use of Kallisto's bootstrap analyses in order to decompose variance into variance associated with between sample differences and variance associated with quantificaiton uncertainty. I don't believe ballgown accounts for uncertainty in the transcript quantification.

The second difference is the quantifier. The ballgown pipeline uses Cufflinks for quantification by default, where as obviously kallisto-sleuth uses kallisto. I've not had good experiences with Cufflinks' quantification accuracy, whereas Kallisto's seems pretty good to me.

If Kallisto-sleuth gives you results, why not just use that? If you really wanted to carry on using Ballgown, I suggest quantifying with RSEM rather than Cufflinks, which I think is possible.