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I followed the instruction in the tutorial to analyze my data. After run the CreateiDEAObject command, I got the following output.
> idea <- iDEA.fit(idea,
+ fit_noGS=FALSE,
+ init_beta=NULL,
+ init_tau=c(-2,0.5),
+ min_degene=5,
+ em_iter=15,
+ mcmc_iter=1000,
+ fit.tol=1e-5,
+ modelVariant = F,
+ verbose=TRUE)
## ===== iDEA INPUT SUMMARY ==== ##
## number of annotations: 100
## number of genes: 1384
## number of cores: 1
## fitting the model with gene sets information...
However, looking at the result of idea@gsea, all the 100 annotations have pvalues >0.5. I wonder how to test more annotations and find the most significant ones for my gene set.
Thank you!
The text was updated successfully, but these errors were encountered:
Thank you for your interest in our package. Sorry for the late reply as I somehow cannot receive the email notification.
Based on the iDEA input summary, the total number of genes you provided in the summary stats is too small. iDEA requires all gene levels summary statistics not just DE genes. Could you please tell me how did you get the summary statistics? Did you only maintain the DE summary statistics as input for iDEA? If so, please use all gene levels summary statistics.
I followed the instruction in the tutorial to analyze my data. After run the CreateiDEAObject command, I got the following output.
However, looking at the result of idea@gsea, all the 100 annotations have pvalues >0.5. I wonder how to test more annotations and find the most significant ones for my gene set.
Thank you!
The text was updated successfully, but these errors were encountered: