
Hi all,

We have data from RNA-seq experiment conducted on two siRNA knockdowns.

Three conditions - Control, si1 and si2.
Four doses - D0, D1, D2 and D3.
There were no replicates. Total 12 samples.

We are trying to use edgeR to identify differentially regulated genes.

Since edgeR doesn't give p-values without replicates, we took samples coming from D0 as replicates i.e. Control-D0, si1-Do and si2-D0 (This decision was taken based on our analysis with DE-seq. We didn't get any DE genes from 'Control-D0  vs. si1-D0' and 'Control-D0 vs. si2-D0').

After building the model, we performed pair-wise comparisons between Control and experimental conditions at each dose, excluding D0, and identified overlapping genes.

Now, we want to see the genes that have increasing or decreasing trends across the doses i.e. the genes should not have significant differences in Controls, but should show a trend in experimental conditions. For this, we have used two sets of contrasts-

control.contrasts <- makeContrasts(
Ctrl_2vs1 = (Ctrl_D2 - Ctrl_D1),
Ctrl_3vs2 = (Ctrl_D3- Ctrl_D2),
Ctrl_3vs1 = (Ctrl_D3- Ctrl_D1),
levels = design)

si.contrasts <- makeContrasts(
si_2vs1 = (si_D2 - si_D1),
si_3vs12 = (si_D3 - 0.5*si_D2 - 0.5*si_D1),
levels = design)

# idea taken from contr.helmert

lrt.Ctrl <- glmLRT(fit, contrast = control.contrasts)
lrt.si <- glmLRT(fit, contrast = si.contrasts)

We have taken genes that were not significant in lrt.Ctrl, but significant in lrt.si. Further filtered the genes based on fold changes from si.contrasts to get genes with upward/downward trends.

Are we doing it correctly? Is there an easy way of doing it that we are missing.

Please let me know your suggestions.


Thanks
Sandhya






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