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My dataset contains single-cell data with the following 6 treatments:
A
A+B
A+B+C
A+C
A+C+D
A+B+C+D
I would like to know the genes specifically induced by B. I wonder if it is okay and more robust to perform pairwise comparisons as below and get the aggregated output of significant genes induced by B in all conditions?
A vs A+B
A+C vs A+C+B
A+C+D vs A+C+D+B
Thanks!
The text was updated successfully, but these errors were encountered:
Hi Constantin,
My concern is including C in the 2nd group and C&D in the 3rd group might
lead to the presence of different sets of differential expressing genes.
By pseudobulking the 3 pairs as separate conditions, is it possible to get
a more consistent (robust, regardless the confounding 'irrelevant' factors)
ones responding to +B?
Best,
Po
On Tuesday, November 15, 2022, Constantin ***@***.***> wrote:
Hi,
I wonder if it is okay and *more* robust [...]
'More' compared with what?
A vs A+B
A+C vs A+C+B
A+C+D vs A+C+D+B
All these three comparisons are equal to just testing for the effect of B
(assuming you have a one-hot encoding for your conditions).
Best,
Constantin
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Hi,
My dataset contains single-cell data with the following 6 treatments:
A
A+B
A+B+C
A+C
A+C+D
A+B+C+D
I would like to know the genes specifically induced by B. I wonder if it is okay and more robust to perform pairwise comparisons as below and get the aggregated output of significant genes induced by B in all conditions?
A vs A+B
A+C vs A+C+B
A+C+D vs A+C+D+B
Thanks!
The text was updated successfully, but these errors were encountered: