I'm analyzing a smart-seq dataset with samples from 9 different people with 4 runs and 17 plates. The samples were first treated and then sorted after stimulated with another condition, later were filtered in flow cytometry and then plated. In total, there are 14 different conditions.
I'm following the OSCA guidelines and I found
scran::testLinearModel to make comparisons while providing a design that would allow to block or control for other variables.
I was checking the output of the histogram of the p-values of a comparison (without blocking or controlling just
~vaccination) and I am surprised by some cells having very similar p-value at specific ranges: around 0.16 and close to 0.46:
While the experiment wasn't designed for scRNA-seq it is the first time analyzing Smart-seq2 data and I'm not sure if I missed any step or why could this be happening. Is this normal/expected (due to normalization effects sequencing depth or other know scRNA-seq procedure) or there is something wrong I can address?
The dataset was filtered by % of mitochondrial percentage, number of reads and number of features and later normalized via
The total number of cells after filtering is low ~1135 and while the 1st PCA showed a low % of variance (3%) I didn't observe much batch effect and if any it was at the second dimension (2%).