Poor self-mapping with SMART-seq data

[a-solovyev12]

Hello,

Thank you for developing scmap.

I was running a self-projection of Tabula Muris dataset to test the method and found that on the SMART-seq version of the dataset the proportion of 'unassigned' cells is very high - up to 50%. This is apparently caused by similarity metrics falling below the specified threshold. The same analysis on the Tabula Muris UMI-based dataset yields much higher assignment rate, over 90% (data from this publication: https://doi.org/10.1186/s13059-019-1795-z).
I was wondering what could be a reason for this difference between technologies? Any suggestions would be appreciated.

[mhemberg]

Hi,

and thanks for your comments. I am not entirely sure why this happens. The best advice we have for the situation when there are many unassigned is to lower the threshold parameter from the default value of .7.