Question: best value of lfc threshold
0
gravatar for rthapa
6 days ago by
rthapa0
rthapa0 wrote:

What is the best value to assign for lfc threshold while using DESeq2 package? With 1 as lfc threshold, I got more than 3000 upregulated genes. Any suggestion please? Thanks

rna-seq • 109 views
ADD COMMENTlink modified 5 days ago by Kevin Blighe11k • written 6 days ago by rthapa0
1
gravatar for Kevin Blighe
5 days ago by
Kevin Blighe11k
London/Brazil
Kevin Blighe11k wrote:

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.

Kevin

ADD COMMENTlink modified 5 days ago • written 5 days ago by Kevin Blighe11k
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