I have the following design matrix:
8 Samples, 475298 sites in matrix:
ID Factor Condition Replicate Reads FRiP
1 ep_293T_siNeg_R1 eprint siNeg 1 9951758 0.76
2 ep_293T_siNeg_R2 eprint siNeg 2 13656477 0.77
3 ep_293T_siFUS_R1 eprint siFUS 1 14988942 0.73
4 ep_293T_siFUS_R2 eprint siFUS 2 21025467 0.75
5 input_293T_siNeg_R1 input siNeg 1 8520645 0.78
6 input_293T_siNeg_R2 input siNeg 2 13948312 0.78
7 input_293T_siFUS_R1 input siFUS 1 10009573 0.77
8 input_293T_siFUS_R2 input siFUS 2 10541404 0.77
Factor has 2 leves (eprint,input), Condition has 2 leves (siNeg,siFUS). I then implement the design:
eprint_contrast_add <- dba.contrast(eprint_norm,design = '~Factor+Condition',
reorderMeta=list(Factor="input",Condition="siNeg"))
So reference levels are input and siNeg. Then I add contrasts:
eprint_contrast_add <- dba.contrast(eprint_contrast_add,contrast = c('Condition','siFUS','siNeg'))
eprint_contrast_add <- dba.contrast(eprint_contrast_add,contrast = c('Factor','eprint','input'))
My understanding is that in this additive model I'm looking for the overall Condition effect controlling for differences due to Factor (input and eprint). Is that correct ?
I then proceed by adding and retrieving coefficients:
eprint_contrast_add <- dba.contrast(eprint_contrast_add,contrast = c('Condition','siFUS','siNeg'))
eprint_contrast_add <- dba.contrast(eprint_contrast_add,contrast = c('Factor','eprint','input'))
eprint_contrast.model.coeffs <- dba.contrast(eprint_contrast_add, bGetCoefficients=TRUE )
eprint_contrast.model.coeffs
> eprint_contrast.model.coeffs
[1] "Intercept" "Factor_eprint_vs_input" "Condition_siFUS_vs_siNeg"
I don't fully understand the meaning of these coefficients here, I believe Intercept is the log2 fold mean of siNeg and input (reference levels) and Factor_eprint_vs_input is the difference in log2 fold mean between eprint and input and Condition_siFUS_vs_siNeg being the log2 fold mean difference between siFUS and siNeg. Is this statement correct ?
