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Question: log2 fold change in result of DEseq2
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gravatar for elhamdallalbashi
11 weeks ago by
elhamdallalbashi0 wrote:

Hello,

I have a question about influence of log2 fold change in determining best genes in differential expression of DEseq2,as an example with adjusted p-value < 0.1,I have 23 genes,

out of 26998 with nonzero total read count

adjusted p-value < 0.1

LFC > 0 (up) : 10, 0.037%

LFC < 0 (down) : 13, 0.048%

outliers [1] : 241, 0.89%

low counts [2] : 11375, 42%

(mean count < 1)

what is the best range of log2 fold change for selecting genes?

ADD COMMENTlink modified 10 weeks ago by Michael Love11k • written 11 weeks ago by elhamdallalbashi0
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gravatar for theobroma22
10 weeks ago by
theobroma2210
theobroma2210 wrote:

Question: did you have technical replicates?? Those stats are quite odd. 

ADD COMMENTlink written 10 weeks ago by theobroma2210

The numbers in themselves don't necessarily imply too few biological replicates. The numbers you detect depend on numerous properties of the experiment or study, and the conditions or treatments being compared (or more complex relationship for more complex experimental designs): as you mention the sample size (number of biological replicates), but also the true effect sizes or interaction sizes for each gene, the variability among biological replicates, the sequencing depth, just to mention a few.

It could be an under-powered experiment, or an under-sequenced experiment, or a well-powered experiment with sufficient sequencing depth, low biological variability and the "treatment" only induces a few changes in expression. 

ADD REPLYlink written 10 weeks ago by Michael Love11k
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gravatar for Michael Love
10 weeks ago by
Michael Love11k
United States
Michael Love11k wrote:

Can you be more specific about exactly what you are interested in? You could choose the genes with lowest adjusted p-value, or you could rank the genes among the significant set by the absolute value of the log2 fold change.

ADD COMMENTlink written 10 weeks ago by Michael Love11k

Hello Michael,

if you remember, I wanted to do DE for all treated vs all controls samples.and you suggested me a design of ~experiment + condition, I need DE for co-expression network,so for coding and non coding I did that,but I see that there are a variety value in log2 fold change.I can not understand which has important role in select genes,fold change or adjust p-value?

this the result for coding;

out of 19933 with nonzero total read count

adjusted p-value < 0.1

LFC > 0 (up) : 335, 1.7%

LFC < 0 (down) : 300, 1.5%

outliers [1] : 289, 1.4%

low counts [2] : 6012, 30%

(mean count < 61)

I should say that samples are similar for coding and non coding but the result are very different!

ADD REPLYlink modified 10 weeks ago • written 10 weeks ago by elhamdallalbashi0

"I can not understand which has important role in select genes"

I can't help you with this one, this is up to you as the analyst. You might discuss with a local statistician the difference between effect size (log fold change) and statistical significance (p-values, FDR sets), and which is more appropriate for your analysis.

 

ADD REPLYlink written 10 weeks ago by Michael Love11k
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