In DESeq2, the 'lfc' values are on the log [base 2] scale (log2fc)..
This is an open-ended question. Ask 100 people and you'll get very different answers.
- Log2fc of 1 is equivalent to linear fold change of 2
- Log2fc of 2 is equivalent to linear fold change of 4
- Log2fc of 3 is equivalent to linear fold change of 8
Each person appears to choose a cut-off value that relates to whatever the first trusted person in their careers told them. The mistake that these people then make is in rigidly adhering to this cut-off and in thinking that it's the only answer. In some cases, people do not even use any cut-off for fold-change and just use adjusted P-values (Q values) and then rank the statistically significant genes based on fold-change. As I recall, the first trusted voice in my own career told me: 'FDR Q<0.05 and absolute log2fC>2', but that was during a time when RNA-seq was not even available.
There really is no answer, though, and it depends on many factors, including:
- The normalisation type (with FPKM/RPKM, unrealistically large log2fc values will be observed; with quantile or geometric normalisation, as used in DESeq2, log2fc values will be lower than in FPKM and will be balanced between negative and positive fold-changes)
- how many genes you want to include for downstream analysis
- previous literature of how many transcripts to expect in such a comparison that you're conducting
- the adjusted P value that you are using for cut-off. For example, even at FDR Q<0.05 and log2fc=2, many of the transcripts will not be that much different when you visualise the normalised counts between your comparisons (this comment only has validity in certain experimental setups though)
- the variance of your data (high variance = unreliable log2fc values in any setting)
So, the message? - there is absolutely no standard cut-off. Use what is most appropriate for your data and what works best.