kchen-lab / bindsc Goto Github PK

Hi, Thanks for developing such a great tool. I have used it to integrate my scRNA-seq and scATAC-seq datasets and project cells from two modalites in co-embedding spaces. The co-embedding is great and matches biological function, so I'd like to use this co-embedding for my scATAC-seq downstream analysis. It's possible to find clusters based on this co-embedding?

Hi, thank you for developing this very useful package.
Request you to kindly suggest if you have any vignettes/guidance for integrating multiple CITE-seq samples, where the mRNA and protein (TotalSeq A) have been sequenced for each of set of samples.
Thank you so much.

Sincerely,
Archana

Hi everyone,

Thanks for the nice package. I wanted to give it a try, but got the above error message, which is likely arising from a orphan 'ff' in the code for preCheck, see here:

Best,

Michael

Hi,I am preparing to use this tool to integrate spatial transcriptome data.
Do you have relevant examples for reference？
Thanks!

Hi,
Thank you for this great package! I have a question about how to get the feature pairs like what is illustrated in additional Figure 5 in your paper? I don't see this in the tutorials but I am quite curious about how to perform that.

Thanks,
Siqi Shen

Hi bindSC team,

Thanks for the great package.

I'm following the mouse retina tutorial .

I realized the input to dimensional reduction for X and Z0 only contain a selected number of genes (highly variable in both RNA and gene activity score calculated from scATAC cells) and the subsequent gene expression imputation was also just on these 'gene.use' genes. If I want to impute the all transcriptome, could I modify the imputeZ like this to impute the all genes present in the original rna dataset for the scATAC cell? (see below)

code:
impuZ(X=rna[["RNA"]], bicca = res) # even though the BiCCA was run using reduced dimensions calculated from 'gene.use' genes.

Would greatly appreciate your thoughts on this. Thank you!

Best regards,
Michelle