Question: Using limma for scRNAseq data
0
gravatar for annadv
5 days ago by
annadv0
Canada
annadv0 wrote:

Hello All,

I would like to ask for advice on using limma for differential expression analysis of scRNAseq data. I usually use Seurat, but I would really like to know whether limma is also a suitable tool for scRNAseq data. I've tried to search for papers and I couldn't find a stable answer to this question.

Thank you very much for your help!

Regards, Anna

scrnaseq limma • 86 views
ADD COMMENTlink modified 5 days ago by igor10k • written 5 days ago by annadv0
1
gravatar for igor
5 days ago by
igor10k
United States
igor10k wrote:

Soneson et al compared a few different methods, including limma, so it's certainly possible:

enter image description here

ADD COMMENTlink written 5 days ago by igor10k
1

(I haven't read the paper in full) If I read this figure correctly, it's curious that methods specific for scRNA-seq are not better, if not worse, than bulk RNAseq methods. Notably, the good old t-test and Wilcoxon tests are not bad at all!

ADD REPLYlink written 4 days ago by dariober11k
1

The Wilcoxon test is the default DE method in many tools as it apparently performs well if sample sizes (so clusters per cell) is large enough, and because it scales well. Say you have 20 clusters and want to find markers so you have to perform DE for every cluster against every other cluster, that is a lot of computation time if you use tools like edgeR which have to estimate dispersion and fit models for dozens/hundreds/thousands of cells.

ADD REPLYlink modified 4 days ago • written 4 days ago by ATpoint34k

Maybe you don't really need to reinvent the wheel. Something like the t-test or the Wilcoxon test have been used for all sorts of data over many years. The advantage of a generic test is that it is generic.

ADD REPLYlink modified 3 days ago • written 3 days ago by igor10k

I guess for single-sample data it is fine given that you have hundreds of cells per cluster and simply want to get the top marker genes. I personally feel safer though (if I have at least n=2 replicates per condition) to use edgeR, maybe after pseudobulk aggregation.

ADD REPLYlink modified 2 days ago • written 2 days ago by ATpoint34k

You could also treat the replicates as a covariate.

ADD REPLYlink written 2 days ago by igor10k
0
gravatar for ATpoint
5 days ago by
ATpoint34k
Germany
ATpoint34k wrote:

Yes, the limma-trend and limma/voom pipelines can be used.

See for a more detailed discussion this benchmarking paper => https://www.nature.com/articles/nmeth.4612 which ranks limma-trend among the better approaches for scRNA-seq DE analysis. It is the paper that the figure igor links is from.

The code that this study used is deposited at GitHub: https://github.com/csoneson/conquer_comparison/blob/master/scripts/apply_limmatrend.R

ADD COMMENTlink modified 4 days ago • written 5 days ago by ATpoint34k
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 1222 users visited in the last hour