Autoencoder-based sequence embedding

Description of feature

IMO autoencoder-based sequence embedding has a huge potential for finding similar immune receptors, potentially improving both the speed and the accuracy compared to alignment-based metrics. In particular, finding similar sequences is important in two scirpy functions:

• defining clonotypes
• querying immune receptor databases.

For the database query, an online-update algorithm similar to scArches for gene expression would be nice: The autoencoder could be trained on the database (which might have millions of unique receptors) once. A new dataset (which might only have 10k-100k unique receptors), could be projected into the same latent space as the database, significantly improving query time.

An extension to this idea is to embed gene expression and TCR/BCR data into the same latent space.

Existing tools

• Trex by @ncborcherding. Based on keras.
• mvTCR by @b-schubert's lab. Combines receptor/Gex data. Based on pytorch.
• TESSA. Combines receptor/Gex data. Not even sure it's an autoencoder, need yet to check in detail, but it seems to use some clever sequence embeddings.
• There are likely more...

@drEast mentioned he is working on something like that a few months ago. Are you willing to share a few details and if you would be interested in integrating it with scirpy?
@adamgayoso, any chance there's AirrVI soon? 😜

Start by examining scirpy's existing clonotype-definition and immune-receptor database-query functions, then review the cited Trex, mvTCR, TESSA, and scArches projects. A complete contribution would need a defined embedding approach, integration points, and measurable speed or accuracy criteria, none of which this proposal specifies.