Align cell annotations across two datasets through annotation-informed optimal transport.
Please refer to the documentation, in particular, the API documentation.
For detailed installation instructions, please refer to the installation guide.
Please take a look at the following tutorials for detailed examples on how to use pairOT:
For a detailed tutorial, please see the in depth tutorial.
For details on how to speed up pairOT model fits and reduce compute requirements, see the reduce compute requirements tutorial
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 matrixSee the changelog.
If you found a bug, please use the issue tracker.
t.b.a
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)