Dear all,

referring to scRNA-seq analysis, many thanks to Aaron Lun and Helena Crowell, for writing the following tutorials on OSCA and MUSCAT :

https://osca.bioconductor.org/multi-sample-comparisons.html

http://biocworkshops2019.bioconductor.org.s3-website-us-east-1.amazonaws.com/page/muscWorkshop__vignette/

'd appreciate also having your suggestions on the following case of scRNAseq analysis (that includes Seurat 3.1, beside SimpleSingleCell/BioC packages) ; the question is : what analysis strategy would you recommend ?

Shall we have 4 batches of scRNA-seq data of these experiments :

WTbatch1, WTbatch2, Abatch1, Abatch2

WTbatch3, WTbatch4, Bbatch3, Bbatch4

What is the optimal way to analyze the data-sets ? Several analysis strategies are possible :

STRATEGY A : using MUSCAT (as outlined above)

STRATEGY B :

1. to use CELLRANGER AGGR (with NORMALIZATION = TRUE) on :

WTbatch1, WTbatch2 : to produce WTbatch1_2

Abatch1, Abatch2 : to produce Abatch1_2

WTbatch3, WTbatch4 : to produce WTbatch3_4

Bbatch3, Bbatch4 : to produce Bbatch3_4

1. and, to follow the descriptions of SimpleSingleCell/fastMNN or SEURAT pipelines with fastMNN (or with LIGER, HARMONY, CONOS) on WTbatch12, WTbatch34, Abatch12, Bbatch34 :

https://htmlpreview.github.io/?https://github.com/satijalab/seurat.wrappers/blob/master/docs/fast_mnn.html

or,

STRATEGY C.

1. to join all the data (WTbatch1, WTbatch2, Abatch1, Abatch2, WTbatch3, WTbatch4, Bbatch3, Bbatch4) in a large MATRIX

2. and to follow the "pseudo-bulk" approach described in https://osca.bioconductor.org/multi-sample-comparisons.html

or,

Any other analysis strategy ? Any suggestions, comments would be very welcome ! Thanks a lot !

-- bogdan

Unless you have a specific problem with one of the strategies that you've linked to, I don't think there's much more to say here. I must say that I don't appreciate these vague, open-ended questions; are you expecting someone to write a review for you?