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Question: Identify up and down regulated genes in affymetrix data
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gravatar for j_jamal96
3 months ago by
j_jamal960
j_jamal960 wrote:

Dear All, How can I recognize (detect) ups and downs genes where  "Limma" genes have been Significant?  What threshold do you recommend to use in this matter?  I was wondering If you could explain me why this threshold have been chosen.

Thank you so much

ADD COMMENTlink modified 3 months ago by tg36930 • written 3 months ago by j_jamal960
3
gravatar for James W. MacDonald
3 months ago by
United States
James W. MacDonald45k wrote:

People conventionally use an FDR of 0.05, but depending on the experiment may choose different thresholds, or possibly include a fold-change criterion using e.g., the treat function. But what you do with your data is up to you as the analyst.

ADD COMMENTlink written 3 months ago by James W. MacDonald45k

To elaborate on James' answer: the FDR describes the expected proportion of false positives in your set of significantly DE genes. We usually use a 5% threshold because 5% of genes being false positives seems to be a tolerable proportion. However, the exact value should be chosen based on what you want to do with those DE genes. For example, if you're working in a setting where validation and follow-up studies are cheap, you might consider relaxing the FDR threshold to get more discoveries at the cost of a higher proportion of false positives. On the other hand, if you're going to do something expensive with your DE genes (e.g., setting up a knockout mouse strain), you might be inclined to be more stringent and use a lower threshold. So 5% is a good place to start, but as James has said, you will need to think a bit about what is most suitable for your setting.

ADD REPLYlink written 3 months ago by Aaron Lun17k

Thank you for your consideration, but there is something misunderstanding for me, how can I detect UP and DOWN genes after detection the differentially expression genes? (In some sites I have seen they considered the fold change higher than 0 as UP and less than 0 as DOWN. It should be noted that lima use 'log2foldchange' and numbers less than 1 'log2' have taken value (amount) less than 0 that it has a different way than the way you guys used, so could you let me know what is your idea about this?)

ADD REPLYlink written 3 months ago by j_jamal960
1

I don't really understand your problem. Genes that have positive log-fold changes are going up. Genes with negative log-fold changes are going down. Look at the sign of the log-fold change in the output of topTable.

ADD REPLYlink written 3 months ago by Aaron Lun17k

 

Thank you for your consideration, but there is something misunderstanding for me, how can I detect UP and DOWN genes after detection the differentially expression genes? (In some sites I have seen they considered the fold change higher than 0 as UP and less than 0 as DOWN. It should be noted that lima use 'log2foldchange' and numbers less than 1 'log2' have taken value (amount) less than 0 that it has a different way than the way you guys used, so could you let me know what is your idea about this?)

ADD REPLYlink written 3 months ago by j_jamal960
3
gravatar for tg369
3 months ago by
tg36930
tg36930 wrote:

I generally draw a volcano plot to see where I can put threshold for adjusted pvalue and logFC. I most cases I use padj<0.05 and logFC>=0.58 (i.e. 1.5 fold up) or logFC<=-0.58 (i.e. 1.5 fold down) to identify differentially expressed genes. However, where you can put threshold to the logFC depend on your experiments so that you can capture biological insight. Hope this helps. -Tanay

ADD COMMENTlink modified 3 months ago • written 3 months ago by tg36930

Thank you very much for your explanation. Would you please explain how  to choose such thresholds for LogFC.

(logFC>=0.58 (i.e. 1.5 fold up) or logFC<=-0.58 (i.e. 1.5 fold down) )

ADD REPLYlink modified 3 months ago • written 3 months ago by j_jamal960
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