Hi,

I have this host and pathogen RNA-seq data generated from the same individuals and I have performed DGEA on both data to determine the DE genes between two disease groups. With this, I would like to find out which host DE genes are correlated with which pathogen DE genes. I thought a simple pearson correlation would suffice, but I would like to adjust for several covariates of interest (ex. age).

I'm thinking a straightforward way is to do as follows:

design <- model.matrix(~1+pathogen_gene_exp+Age)

disp <- estimateDisp(y, design, robust = TRUE)

fit <- glmFit(disp, design, robust = TRUE)

lrt <- glmLRT(fit, coef = 2)

topTags(lrt)

This would compare the expression of one pathogen gene against all host genes. However, I can't think of a good way to represent the expression of pathogen gene - under edgeR, would it be acceptable in this scenario to have the expression of pathogen gene to be logCPM? Or is there another (better/more straightforward) way to perform co-expression analysis with adjustment for covariates? Thank you.

hi, im not entirely sure what your trying to do with this code, but you say 'I thought a simple pearson correlation would suffice, but I would like to adjust for several covariates of interest (ex. age).'. You can do a partial correlation in R and just loop over the data set, so you can adjust for continuous covariates e.g. I do that sometimes.

#m[z,2] <- abs(pcor(c("gene", "DAS28.6M", "DAS28.0M"), var(df))) # partial correlation of gene expression with my clinical variable, adjusted for another clinical variable

You can do this to look at co expression with adjustment. You need to make a data frame first for this code.

Log-CPMs seem fine to me. edgeR uses a log-link function to fit each GLM, so you're effectively correlating the log-CPM of your pathogen gene to the log-(library size-adjusted-)count of each of your host genes. The log-transformations cancel out, so you just get a linear correlation between the pathogen and host expression. The estimated value of coefficient for the pathogen expression covariate represents the gradient of the log-log line; larger values correspond to a greater effect size. If you want to be more sophisticated, you can play around with splines by using the pathogen expression as a covariate; this will provide a more flexible fit in case the pathogen-host expression association is not linear, but at the cost of reducing the interpretability of your model.