Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

Autoencoder-based sequence embedding

See original GitHub issue

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? 😜

Issue Analytics

State:
Created a year ago
Comments:7

Top GitHub Comments

1reaction

b-schubertcommented, Oct 18, 2022

Re future directions:

theoretically, in mvTCR you also have independent transcriptomic and TCR spaces, though we have not fully explored their structures yet.
I am teaching a single-cell analysis course this winter semester with student projects where we are going to explore VDJ encodings and pre-trained TCR models in mvTCR, we could also explore the options of how to integrate HLA types (more than just as confounding)

0reactions

FFinotellocommented, Oct 18, 2022

Hello everyone, and thanks for this great exchange! 😃

For the HLA types, it would be great to keep track somehow of their sequence similarity. We could also consider their level of expression, at least broadly assigned to HLA-A, HLA-B, and HLA-C genes, whereas allele-specific expression would be hard to derive from 10x data. But @b-schubert you are definitely the expert here 😃

Top Results From Across the Web

AIME: Autoencoder-based integrative multi-omics data ... - PLOS

Autoencoder is a deep learning–based nonlinear embedding approach that is typically used to achieve sparse data representation from a single ...

A Gentle Introduction to LSTM Autoencoders

An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture.

Analyzing autoencoder-based acoustic word embeddings

Here we analyze basic properties of AWE spaces learned by a sequence-to-sequence encoder-decoder model in six typologically diverse ...

Lenny #2: Autoencoders and Word Embeddings

Sequence -to-sequence autoencoders work the same way as traditional autoencoders, except both the encoder and decoder are RNNs. So instead of converting a ......

Feature Extraction by Sequence-to-sequence Autoencoder

Given an action sequence, our encoder first associates each of the N unique actions with a K-dimensional numeric vector, called the embedding of...