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Merge pull request #32 from quadbio/docs/update
Update to the docs
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CHANGELOG.md

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## [Unreleased]
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## [v0.1.4]
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### Changed
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- Rename mapping methods to `map_obs`, `map_obsm`, and `map_layers`, and improve support for numerical `.obs` annotations {pr}`30`.
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## [v0.1.3]
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### Added

README.md

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[![Pre-commit.ci][badge-pre-commit]][pre-commit]
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[![PyPI][badge-pypi]][pypi]
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[![Documentation][badge-docs]][docs]
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[![Downloads][badge-downloads]][downloads]
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[badge-tests]: https://github.com/quadbio/cellmapper/actions/workflows/test.yaml/badge.svg
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[badge-coverage]: https://codecov.io/gh/quadbio/cellmapper/branch/main/graph/badge.svg
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[badge-pre-commit]: https://results.pre-commit.ci/badge/github/quadbio/cellmapper/main.svg
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[badge-pypi]: https://img.shields.io/pypi/v/cellmapper.svg
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[badge-docs]: https://img.shields.io/readthedocs/cellmapper
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[badge-downloads]: https://static.pepy.tech/badge/cellmapper
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k-NN-based mapping of cells across representations to tranfer labels, embeddings and expression values. Works for millions of cells, on CPU and GPU, across molecular modalities, between spatial and non-spatial data, for arbitrary query and reference datasets. Using [faiss][] to compute k-NN graphs, CellMapper takes about 30 seconds to transfer cell type labels from 1.5M cells to 1.5M cells on a single RTX 4090 with 60 GB CPU memory.
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[coverage]: https://codecov.io/gh/quadbio/cellmapper
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[pre-commit]: https://results.pre-commit.ci/latest/github/quadbio/cellmapper/main
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[pypi]: https://pypi.org/project/cellmapper/
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[downloads]: https://pepy.tech/project/cellmapper
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[faiss]: https://github.com/facebookresearch/faiss
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[pynndescent]: https://github.com/lmcinnes/pynndescent

docs/_static/images/cellmapper_logo_dark.svg

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docs/_static/images/cellmapper_logo_light.svg

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docs/conf.py

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"use_repository_button": True,
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"path_to_docs": "docs/",
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"navigation_with_keys": False,
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"logo": {
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"image_light": "_static/images/cellmapper_logo_light.svg", # Logo for light mode
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"image_dark": "_static/images/cellmapper_logo_dark.svg", # Logo for dark mode
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},
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}
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pygments_style = "default"

src/cellmapper/model/embedding.py

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)
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# Concatenate with inner join on genes
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joint = ad.concat([self.reference, self.query], join="inner", label="batch", keys=["reference", "query"])
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joint = ad.concat(
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[self.reference, self.query], join="inner", label="batch", keys=["reference", "query"], merge="same"
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)
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# Potentially subset along the features
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mask_var = _check_mask(joint, mask_var, "var")

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