Appropriate statistical test for comparing two samples in single-cell RNAseq

1

I have the following two genes with expression taken from single-cell
RNAseq:

enter image description here

I am conducting pairwise comparison between sample (S1, S2...) on those data,
using t.test. The data has been normalized using Monocle3 normalization function.

My question is whether this is a correct approach? If not what are the better statistical test to use?

And also, if it's also acceptable to perform statistical test for raw count?
And the need of multiple comparison test like Bonferroni, FDR?


monocle3


expression


differential


single-cell


rna-seq

• 99 views

Yes, the t-test has been shown to perform well, probably due to the large number of data points when treating each single cell as a "replicate", see Soneson & Robinson (2018) Nat Methods. This paper also covers alternative testing regimes and discusses/benchmarks up/downsides. The big advantage of t- or wilcox tests is speed and simplicity. This is (iirc) for single-sample comparisons e.g. comparing sample1 vs sample2 in the absence of true biological replicates. With true replicates I would often prefer pseudobulk-level DE analysis.

And also, if it's also acceptable to perform statistical test for raw count?

No, raw counts are confounded by sequencing depth.

And the need of multiple comparison test like Bonferroni, FDR?

Yes, FDR correction is needed when conducting multiple tests, there is no magic in single-cell analysis, it is has to obey the basic rules of statistics.


Login
before adding your answer.

Traffic: 1534 users visited in the last hour



Source link