How to use a pre-trained Region2Vec model

Region2Vec is an unsupervised method for creating embeddings of genomic regions and genomic region sets. This tutorial discusses how to use pre-trained models. To learn how to train a new model see the region2vec training documentation We make available several pre-trained models available on our huggingface repo. These models can be used to create embeddings of genomic regions and region sets without having to train a new model.

Get the model

To use one of our pre-trained models, simply import the Region2VecExModel and download the model from huggingface:

from geniml.io import Region
from geniml.region2vec import Region2VecExModel

model = Region2VecExModel("databio/r2v-encode-hg38")

Note: We use the Region2VecExModel class to load the model because it is an extension of the Region2Vec class that comes with its own tokenizer. This ensures that the model and tokenizer are compatible.

Encode regions

Now we can encode a genomic region or a list of regions:

region = Region("chr1", 160100, 160200)
regions = [region] * 10

embedding = model.encode(region) # get one region embedding
embeddings = model.encode(regions) # or, get many embeddings

We can also encode an entire bed file, which will return region embeddings for each region in the file:

bed = "/path/to/bed/file.bed"

embeddings = model.encode(bed)

Note: It is possible that a region can not be tokenized by the tokenizer. This is because the tokenizer was instantiated with a specific set of regions. If this is the case, the model simply returns the unknown token (chrUNK-0:0). If you find that this is happening often, you may want to ensure that your regions are a good fit for the universe of regions that the model was trained on. The unknown token will indeed have an embedding, but it will not be a meaningful representation of the region.

Region pooling

It is often the case that we want a single embedding that represents a set of regions. For example, we may want to encode a patient by taking the average embedding of all the SNPs in the patient's genome. We can do this by simply averaging across the embeddings of the regions:

patient_snps = "/path/to/bed/file.bed"

embeddings = model.encode(patient_snps) 
patient_embedding = np.mean(embeddings, axis=0)