1. I'm trying to use the code below to shrink the LFCs in my model but I keep generating an error.
resLFCint <- lfcShrink(dds, res = res, name="Cov1.Conditioncase", type="apeglm")) using 'apeglm' for LFC shrinkage Error: !missing(coef) is not TRUE design ~ Age + Gender + Cov3 + Cov2 + Cov1 + Condition + Cov1:Condition
where Cov1, Cov2 and Cov3 are all continuous variables and levels of Condition are treated and untreated. As this is the final coefficient in resultNames(dds), res generates the same results whether or not I actually name the interaction, and gives me around 50 significant genes but this becomes a problem when I try to shrink the LFCs. I'd like to look see if these genes are significant with LFC 1 and -1 and from reading the userguide, it appears that using lfcshrink is recommended over filtering results from res by whatever LFC I set as my cutoff of interest. My aim is to determine whether there is any effect of Cov1 ( and similarly Cov2 and Cov3 - when I get the command to work) that differs between treated and untreated whilst controlling for Age and Gender. I have 24 samples in each set. Please let me know if I'm forming my model correctly given the question I want to answer.
2. I'd like to also analyze the situation where I'm adding the effects of Cov1, Cov2 and Cov3 and looking at their additive effects between treated and untreated. I'm not sure if that's the right approach. I can prove that CovX individually have (or do not have) an effect in treated vs untreated if I look at each interaction separately, as above, but is there a way to look at all 3 in one contrast, or naming all in the coefficients?
Thanks in advance,