pairOT: Identifying similar cell types and states across single-cell transcriptomic studies

Align cell annotations across two datasets through annotation-informed optimal transport.

Getting started

Please refer to the documentation, in particular, the API documentation.

Installation

For detailed installation instructions, please refer to the installation guide.

Tutorial

Please take a look at the following tutorials for detailed examples on how to use pairOT:

Detailed explanation

For a detailed tutorial, please see the in depth tutorial.

Speed up pairOT computations

For details on how to speed up pairOT model fits and reduce compute requirements, see the reduce compute requirements tutorial

Basics of using pairOT

import scanpy as sc
import pairot as pr

# 1. Preprocess input data
adata_query, adata_ref = pr.pp.preprocess_adatas(
    sc.read_h5ad("path/to/query.h5ad"),
    sc.read_h5ad("path/to/reference.h5ad"),
    n_top_genes=750,
    cell_type_column_adata1="cell_type_column_query",
    cell_type_column_adata2="cell_type_column_ref",
    sample_column_adata1="sequencing_sample_column_query",
    sample_column_adata2="sequencing_sample_column_ref",
)

# 2. Initialize pairOT model
dataset_map = pr.tl.DatasetMap(adata_query, adata_ref)
dataset_map.init_geom(batch_size=512, epsilon=0.05)
dataset_map.init_problem(tau_a=1.0, tau_b=1.0)

# 3. Fit pairOT model
dataset_map.solve()
mapping = dataset_map.compute_mapping()
distance = dataset_map.compute_distance()

# 4. Visualize results
pr.pl.mapping(mapping)  # similarity matrix
distance = distance.loc[
    mapping.max(axis=1).sort_values(ascending=False).index.tolist(),
    mapping.max().sort_values(ascending=False).index.tolist(),
]  # order cluster distance matrix the same way as similarity matrix
pr.pl.distance(distance)  # cluster distance matrix

Release notes

See the changelog.

Contact

If you found a bug, please use the issue tracker.

Citation

t.b.a

References

pairOT was written by Felix Fischer <felix.fischer@helmholtz-munich.de>

Support for software development, testing, modeling, and benchmarking provided by the Cell Annotation Platform team (Roman Mukhin)