SEQanswers

Go Back   SEQanswers > Applications Forums > RNA Sequencing



Similar Threads
Thread Thread Starter Forum Replies Last Post
infinite fold change RNAseq biofreak Bioinformatics 6 07-02-2012 10:02 AM
EdgeR Fold Change Calculation umnklang Bioinformatics 3 06-21-2012 12:17 AM
DESeq DiffExpress Vs. Fold Change KellerMac Bioinformatics 3 06-10-2011 10:49 AM
fold change value-cuffdiff madsaan Bioinformatics 4 02-10-2011 06:51 AM
fold change for genes with 0 reads rnaseq General 2 11-04-2010 07:44 PM

Reply
 
Thread Tools
Old 07-10-2012, 12:54 AM   #1
smalan
Junior Member
 
Location: South Africa

Join Date: Feb 2012
Posts: 8
Default Cut-off values for fold change in RNA seq

Hi. I am new at RNA seq. Our bioinformaticist has asked me to provide them with cut-off values for fold change and p-values to use in our differential expression analysis. I have seen some variation in different papers, ranging from 1.5 to those using between 1.8 and 2 for fold change and then p<0.05 and p<0.01. What is the general consensus regarding these values?
Thanks.
smalan is offline   Reply With Quote
Old 07-12-2012, 10:10 AM   #2
mbblack
Senior Member
 
Location: Research Triangle Park, NC

Join Date: Aug 2009
Posts: 245
Default

Any cutoff is arbitrary, so it really is a matter of what you think is an acceptable level of control for false positives and a reasonable level for biologically relevant or interesting changes.

The group I work with generally use a fold change cutoff of +/- 1.5 and an FDR of < 0.05. Going to a fold change of 2 tends to cause us to loose too many interesting and relevant genes, and pushing the FDR to less seems too stringent and so again, we risk missing biologically interesting and relevant genes. But, it really is arbitrary what you pick.

Personally, I'd ask that your analyst return you a list of all p-values, FDR values and fold change for all genes and let you filter them to see what cutoffs give you workable gene lists without being overly stringent or overly lax. There is no hard and fast rule here - the trick is to get biologically valuable gene lists without risking too many false positives or throwing out important genes with too a stringent cutoff. It is something you need to look at in the context of the whole results and the context of your specific biological system and experimental question(s), not just randomly pick a value out of thin air and rigidly apply it.

I always save the full tables from my analyses (with R/BioConductor tools it is simple to output a tab delimited table of results for all genes) and then filter those afterwards to see what I get back out with different cutoffs to decide what my final choice will be to create gene lists to go forward with.
__________________
Michael Black, Ph.D.
ScitoVation LLC. RTP, N.C.

Last edited by mbblack; 07-12-2012 at 10:18 AM.
mbblack is offline   Reply With Quote
Reply

Thread Tools

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is Off




All times are GMT -8. The time now is 07:08 PM.


Powered by vBulletin® Version 3.8.9
Copyright ©2000 - 2020, vBulletin Solutions, Inc.
Single Sign On provided by vBSSO