If there are stray reads in "mouse" cells which aligned to human for some reason, reliagning to mouse alone will force those reads to be assigned to a mouse gene.
Hi everyone,
I'm analyzing 10x scRNA-seq data generated from xenografts (mouse + human tissues). I have the following workflow to label cells as either mouse or human:
- Align 10x scRNA-seq data to mouse+human combined genome using cellranger count.
- Use the file generated by cellranger count (gem_classification.csv) which assigns mouse, human, or multiplet to each cell to classify each cell type.
- Realign the fastq files to the mouse genome ( we are interested in the mouse cells) using cellranger count
- Use the classification from Step 2 to filter out human cells in Seurat.
Would this be the right approach or is there a better alternative that you would recommend?
Thanks a lot!
1 answer
This approach is fine, but is there a reason you aren't just using the output from step 2 and limiting to the mouse cells/genes within it?
I guess I don't have a good grasp on how much of a difference it'd make, would be interesting to know if there are specific genes where this is a particular issue.
Thank you to both for your input. I also thought that alignment may improve if only a single ref genome is present, perhaps only for a small fraction of genes - though this is just an assumption. I may try both ways just to compare.
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For the third step I get the error
How should it be modified so that it doesn't baulk about the sample already being processed once?
Change the ID. Or output in a different folder.