hello i have a question ,
i integrated two data ( 2 samples of B cells) with cell ranger aggr and with filtered matrix data from cell ranger aggr , i apply the seurat script
but i dont know how to distingue between my two samples after clustering
i can't use split.by because there is no row to use
Are the two samples two different conditions/lines/...? If so, you don't need to run aggregate from cellranger. You can just go through the normal seurat integration workflow.
the samples are B cells, first condition is a subpopulation of the second condition. so i have to campare them to see what distingue this subpopulation from the other b cells, if i use the seurat integration workflow, the clustering will be the same for both
Regardless of the clustering, the sample identity will be stored as a meta-data variable. You can then use this with the FindMarkers (or related functions) to find transcriptional differences.
What software are you trying to use? Either combine with cellranger, or seurat, you don't use both. The Loupe output will know what library each barcode came from.
The barcodes of the aggregated data should all have a number appended to them to indicate which library they came from.
When you integrate with Seurat, you tell it what project name to assign to each sample, and that gets stored in the metadata when you integrate under "orig.ident"
exactly i would like to add this information in seurat , to tell him baracode with tail-1 are for exemple sample 1 and the other sample 2 , so i can after see which cluser belong to which sample but i cant find how to do it with seurat
Well, sure, if you read in the data from aggr and assign the project only once, it will all have one project name. You can figure out from the barcode suffixes which barcodes came from which samples, add your own column to the meta data.
Are the two samples two different conditions/lines/...? If so, you don't need to run aggregate from cellranger. You can just go through the normal seurat integration workflow.
the samples are B cells, first condition is a subpopulation of the second condition. so i have to campare them to see what distingue this subpopulation from the other b cells, if i use the seurat integration workflow, the clustering will be the same for both
Regardless of the clustering, the sample identity will be stored as a meta-data variable. You can then use this with the
FindMarkers
(or related functions) to find transcriptional differences.