whether SEVtras is suitable for 10x Genomics Spatial Transcriptomics?

[xiaojjia]

Thank you for the great method!

Regarding this algorithm, I'd like to ask whether it is suitable for spatial transcriptomic dataset, such as 10x Genomics Spatial Transcriptomics. If applicable, do the analysis processes and input file formats need to be adjusted?

Thanks for your help!
Best,
xiaoj

[RuiqiaoHe]

This is the one limitation of SEVtras. It is only suitable for droplet-based single cell transcriptome data, e.g. 10X scRNA-seq. I believe that sEVs and other types of EVs can be captured by the spatial transcriptome methods. However, SEVtras is not able to resolve them currently. We are trying to make it possible, which we think that is the future direction as told in the Research Briefing. Feel free to discuss any good ideas with me!

[xiaojjia]

Thanks for your reply, it's a great algorithm!
Recently I encountered a new problem: when I was preparing to extract the sEV secretion activity of each cell from the SEVtras output SEVtras_combined.h5ad, I found that there were two related results ESAI_c and ESAI_cS in the adata.obs. I guess ESAI_c is what I need, but I didn't find an explanation about ESAI_cS, so Come seek your help.
Thanks in advance for your help!

Best!
xiaojjia

[RuiqiaoHe]

Thanks for your testing. ESAI_c is the sEV secretion activity at the cell type level in all your samples, and ESAI_cS means the sEV secretion activity at the cell type level resolved sample by sample. I have added it in https://sevtras.readthedocs.io/en/latest/Part%20II%20ESAI%20calculating.html.

[xiaojjia]

Thanks sincerely, I got it!