Hi all,
Apologies in advance if this has already been asked previously but I couldn't find a suitable answer.
Context: I am in the middle of analysing my RNAseq data for 80 samples (16 treatments x 5 biol. replicates each) from a marine alga. My treatments were: Temperature as a factor (3 levels), changing carbonate chemistry (manipulated via changing pH and total dissolved inorganic carbon concentration). Although many of carbonate chemistry parameters are highly colinear, I would like to obtain Log2FoldChanges in gene expression due to, for example, CO2 concentration.
Therefore, I thought that the best DESeq2 design model would be to do a PCA on my carbonate chemistry variables, (where PC1 + PC2 explained ~ 95% of variance). So, I used ~ Temp + PC1+ PC2 + Temp:PC1 + Temp:PC2 as my model design, and used the loadings to obtain LFCs for each of my carb. chem. variable.
L1 <- loadings_matrix$PC1[loadings_matrix$variable == var]
L2 <- loadings_matrix$PC2[loadings_matrix$variable == var]
composite_LFC = log2FC_PC1 * L1 + log2FC_PC2 * L2,
composite_LFC_SE = sqrt((lfcSE_PC1^2 * L1^2) + (lfcSE_PC2^2 * L2^2)),
padj_zscore = p.adjust(2 * pnorm(-abs(composite_LFC / composite_LFC_SE)), "BH")
where var is my centered and scaled carbonate chem. variable.
I am not a statistician, or a "hard core" Biologist, but by looking at the code snippet above, do you guys think I'm doing the "correct thing"? I pre-filtered my genes by FDR < 0.05 before running the above code, and I only use the genes which are present in both the wald-test FDR < 0.05 as well as LRT FDR < 0.05 (reduced model for PC1 = ~ Temp + PC2 + Temp:PC2).
Any help/advice/recommendations would be highly welcome and I am grateful for your help.
Nishant
Therefore, I thought that the best DESeq2 design model would be to do a PCA on my carbonate chemistry variables, (where PC1 + PC2 explained ~ 95% of variance). So, I used ~ Temp + PC1+ PC2 + Temp:PC1 + Temp:PC2 as my model design, who gave you this concept?
and what kind of feature values you are using? for deseq2? raw counts or normalized?