Is it possible to obtain raw read counts from UMI count
I was using Monocle to find differentially expressed genes in different cell types. As my dataset is quite large, the below code
full_model_fits <- fitModel(ara_cds, modelFormulaStr = "~cell_type")
always failed as it utilized a lots of memory. I was thinking to do this using edgeR as I know which cell belongs to which cell-type. But for this I need raw counts data not UMI I think. So is it possible to obtain raw read counts from UMI counts from cell ranger kit? Or is there any other way to do this?
Thanks for help!
Re: Is it possible to obtain raw read counts from UMI count
This is Andrew with 10x software support.
Cell Ranger outputs a variety of output files, please see this page for an overview: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/overvie...
If you want raw read counts, you may wish to begin working with the bam file. Please note that all reads are in the bam file, even those that were not mapped or counted in the gene-barcode matrix. In addition to standard tags, there are several custom 10x tags that deal with the barcode, UMI, etc. So with a bit of bioinformatics savvy you can get raw read counts. Please see: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/bam
Please note that Monocle is not supported by 10x, and cellrangerRkit has been deprecated and is unsupported as well. We often point customers to use Seurat, although this is not supported either. However v3 of Seurat has been updated to handled new feature barcoding data. Please see: https://satijalab.org/seurat/
There is a large and growing list third party scRNAseq tools you may find useful, again please note we are not endorsing or supporting any of these officially: https://www.scrna-tools.org/
Hope this helps. If you have further questions or concerns, for the fastest service please open a ticket with us at firstname.lastname@example.org and reference this community post. Thank you for being a 10x customer.
Best, Andrew Gottscho