Hi all.
In my lab, we have some old RNA-seq data that were produced by other users in the past. The experimental design was completely wrong, but I would like to analyze them in order to decide whether I should repeat the experiments or drop the whole idea altogether. I have already tried a few things, but I am afraid that all I see is technical noise.
I have performed DE analysis of KD vs ctrl and I got 1200 DE genes. Using contrasts, I have check wt vs ctrl and I got 70 DE genes. However, the general profile of both comparisons seems very similar (through heatmaps). Is there a way to correct for the different preps? I have seen in my recent data that the batch effect can be huge, but is there a way to extract some meaningful information from this set of data? Or it’s not worth trying to make sense of them at all?
When I tried ~ Treatment*Condition or ~ Prep + Condition, I got the error that the model matrix is not full rank (obviously).
Any suggestions will be greatly appreciated, or any experiences with problematic experimental designs!
Cheers
In my lab, we have some old RNA-seq data that were produced by other users in the past. The experimental design was completely wrong, but I would like to analyze them in order to decide whether I should repeat the experiments or drop the whole idea altogether. I have already tried a few things, but I am afraid that all I see is technical noise.
Code:
Sample Condition Prep Treatment 1 KD A shRNA 2 KD A shRNA 3 KD A shRNA 4 ctrl B shRNA 5 ctrl B shRNA 6 ctrl B shRNA 7 ctrl B shRNA 8 wt C no 9 wt C no 10 wt C no 11 wt C no 12 wt C no 13 wt C no 14 wt C no
When I tried ~ Treatment*Condition or ~ Prep + Condition, I got the error that the model matrix is not full rank (obviously).
Any suggestions will be greatly appreciated, or any experiences with problematic experimental designs!
Cheers
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