lhe17
commented
8 months ago Hi Miguel,
Thank you for your questions.
- If you add "output_re=TRUE" to nebula, then you will see the random effects in the output.
-
To get the covariance matrix, you could run the following as illustrated in the tutorial
cov= matrix(NA,12,12) # 12 is the number of the coefs in your design matrix
gene_i = 1 # gene_i is the index of the gene of which you want the covariance
cov[lower.tri(cov,diag=T)] = as.numeric(re$covariance[gene_i,])
cov[upper.tri(cov)] = t(cov)[upper.tri(cov)] # cov is the covariance
Best regards,
Liang
On Fri, Oct 13, 2023 at 7:34 AM miguecass @.***> wrote:
Hi,
I ran your method for my data and it was surprisingly fast, so congrats on that first. However, I have some doubts:
- In the output random effect is always NULL in my case. I assumed the random effect is the second input in the function nebula? Otherwise I am not sure where to put it, because in your tutorial it was not included in the design.
re = nebula(data,metadata$Batch,pred=df, covariance = TRUE)
- Another doubt is about the covariances. I have 78 covariances but they are not named. So I am not sure about the order of the namings. My design matrix has 12 coefficients but I would like to know what is the order of the coefficients because I need to compute some complex contrasts.
Thanks in advance,
Best Miguel
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miguecass
Hi,
I ran your method for my data and it was surprisingly fast, so congrats on that first. However, I have some doubts:
1) In the output random effect is always NULL in my case. I assumed the random effect is the second input (
metadata$Batch) in the function nebula? Otherwise I am not sure where to put it, because in your tutorial it was not included in the design.re = nebula(data,metadata$Batch,pred=df, covariance = TRUE)2) Another doubt is about the covariances. I have 78 covariances but they are not named. So I am not sure about the order of the namings. My design matrix has 12 coefficients but I would like to know what is the order of the covariances since I have 78 of them and I need to compute some complex contrasts.
EDIT: Actually thinking it a bit more, would I need the covariance matrix to compute DE genes for my contrasts? Can I just not multiply the coefficients for each gene by my contrast matrix? (Same as with limma approach)
Thanks in advance,
Best Miguel