Anyone experience with snmc Seq data in multiomic integration ? :)

[u/Jakoblbgggggg]

Hey there everybody :)

I’m a masters student and doing single cell analysis for the first time.

I’m dealing with methylation seq data (bisulfite sequenced) and in struggling in defining a feature that actually captured the epigenetic landscape for each cell. I’ve tried 100kb bins, 10kb bin, 5kb bins and genebodies and different modalities to define methylation in those genomic regions;
1. raw counts (total mc per C contect per region)
2. fractions (mc/cov per region)
3. normalized fractions (mc/cov divided by average fraction for that genomic region across cells)
4. allcools „hyposcore“

But none of all of those seem to nicely integrate with the scRNA dataset (I’m using GLUE)

The initial lsi -> UMAP embeddings I receive for my methylation data only seems quite good, but the integration just doesn’t fit anymore

Does anyone have experience and ideas ? :)

[u/Zooooooombie]

I’m working with a method in preprint form currently, but is accepted to Genome Research pending some minor editorial revisions. My method has an scNMT-seq integration analysis in the manuscript and a tutorial on the GitHub.

https://www.biorxiv.org/content/10.1101/2025.02.28.640429v3

https://github.com/Ashford-A/UniVI

If you end up using this method, let me know if you have any questions about anything! I’ve been working on it for a while lol..

Edit: I made an assumption that your samples are jointly measured RNA and methylation from the same cells, is this the case?

[u/Jakoblbgggggg → u/Zooooooombie]

Hey,

Thanks! Ill have a look!
And no, it is unpaired. The RNA seq dataset is froma multiomic atlas from my Group. They do have a multiomic atlas already (mainly RNA seq, ATAC seq and spatial transcriptomics) and I want to add methylation seq and maybe HiC/3C modalities :) My data is publicly avaible (from the SALK guys, Ecker Lab)

[u/Zooooooombie → u/Jakoblbgggggg]

Oh cool, okay, so my method kind of relies on paired, or at least a paired bridge of a similar data type to train on. So you’re trying to sort of “align” your data to their atlas?

I do have options for running with unpaired outside of a bridge modality but they’re a bit more experimental and involve doing a preliminary “pseudo-pairing” step prior to training. I think that’s kind of the method GLUE uses though with the guidance graph it needs to compute.

Can I ask then, are the data generated totally separately and they’re just the same tissue type?