Interest in HTO/ADT demultiplexing and analysis?

I’m starting to regularly analyze samples multiplexed with HTOs a la Stoeckius et al and multimodal CITE-seq ADTs. I have some rough ports of Seurat’s HTODemux and some other functionality.

Is anyone else analyzing these kinds of experiments using scanpy and is there interest to bring functionality to do so into the main scanpy branch?

[njbernstein]

@wflynny on my to-do list is to add solo to scVI directly and then scanpy will be a nice play to do both types of doublet finding.

[wflynny]

@gokceneraslan Hey, sorry for my long silence on this.

I’ve been using @Hoohm’s https://github.com/Hoohm/CITE-seq-Count for ADT/HTO tag counting which produces (in recent versions) a 10X v3 style mtx directory for both reads and UMIs. In some cases, I’ll load these in as their own AnnData object with reads and counts as different layers which is helpful in computing per-cell or per-tag “sequencing saturation” and other metrics involving both reads and counts. This is especially helpful for investigating some pilot experiments (lipid tags, cholesterol tags, etc.) we’ve been doing.

However, most of the time I’ll just load the tags matrix in as a pandas dataframe and run them through a demuxing function that’ll modify adata.obs.

A couple challenges/ideas to consider:

• at our facility, we’re typically building the same Illumina i7 index (ATTACTCG) into all tag libraries. This leads to some tricky situations when using a NovaSeq for sequencing since the multiple tag libraries (with disjoint sets of tags) may be run on the same sequencing flowcell lane. This results in a single set of FASTQ files and thus a single barcode-tag matrix for all tag libraries on that lane. Therefore, the mapping between transcriptome AnnData objects <-> tag library matrices is not always 1-to-1.
• in my experience, HTO libraries have a large variance in quality, so for the most part I’ve been using the transcriptome as my “ground truth” as to what is a cell. However, I imagine others use HTOs to “rescue” cells that were not called by their pipeline of choice (and I hope to do this once I build enough trust in the data). In that case, one would want to intersect the HTO classifications with the raw cell-gene matrix.
• not all tags are antibody based, so I’d vote for naming all related functions *hashtags().

I’d therefore vote for something like the following design:

# htos is a AnnData object
htos = sc.read_hashtags(filename) 

# classify_hashtags adds a classification to the hto AnnData object
# kwargs might involve things like `use_tags=["tag1", "tag2", "tag3"]`
sc.pp.classify_hashtags(htos, **kwargs)
print(htos.obs.classification) 

# demuxing cell-gene matrix(es) could then be done like
rna1 = sc.read_10x_h5(...)
rna2 = sc.read_10x_h5(...)
# sc.pp.demux_by_hashtag(adata_hto, *adata_rna, tag_groups=None, ...)
sc.pp.demux_by_hashtag(
    htos, 
    rna1, rna2, 
    tag_groups=[("tag1", "tag3", "tag5"), ("tag2", "tag4", "tag6")]
)

@gokceneraslan This is more complex than what you suggested, but I think is sufficiently general to cover my needs as listed above. Let me know what you think—I’ll have some development time next week to possible contribute to this.