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Updated CHANGELOG and README.
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LTLA committed Dec 15, 2024
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13 changes: 7 additions & 6 deletions CHANGELOG.md
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# Changelog

## Version 0.5.0
## Version 0.4.0

- Switch to using **pybind11** for the Python/C++ interface.
- Update to the latest **singlepp** C++ library.

## Version 0.4.0

- chore: Remove Python 3.8 (EOL).
- precommit: Replace docformatter with ruff's formatter.
- Store marker lists as lists of `StringList` objects in the metadata of the `BiocFrame` objects.
- Automatically remove `None` labels in the reference.
- Automatically remove duplicated feature names in the reference.
- Remove the `ref_names=` option in `train_integrated()` for simplicity.
- Remove support for auto-loading of references based on their names; users should extract references from **celldex** instead.
- Deprecate options to extract features/labels from a `SummarizedExperiment` based on the column name; users should explicitly pass the column contents to each function.

## Version 0.3.0

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4 changes: 2 additions & 2 deletions README.md
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[![Project generated with PyScaffold](https://img.shields.io/badge/-PyScaffold-005CA0?logo=pyscaffold)](https://pyscaffold.org/)
[![PyPI-Server](https://img.shields.io/pypi/v/singler.svg)](https://pypi.org/project/singler/)
[![Monthly Downloads](https://static.pepy.tech/badge/singler/month)](https://pepy.tech/project/singler)
![Unit tests](https://github.com/BiocPy/singler/actions/workflows/pypi-test.yml/badge.svg)
![Unit tests](https://github.com/SingleR-inc/singler-py/actions/workflows/pypi-test.yml/badge.svg)

# Tinder for single-cell data

## Overview

This package provides Python bindings to the [C++ implementation](https://github.com/SingleR-inc/singlepp) of the [SingleR algorithm](https://github.com/SingleR-inc/SingleR),
This package provides Python bindings to the [C++ implementation](https://github.com/SingleR-inc/singlepp) of the [SingleR method](https://github.com/SingleR-inc/SingleR),
originally developed by [Aran et al. (2019)](https://www.nature.com/articles/s41590-018-0276-y).
It is designed to annotate cell types by matching cells to known references based on their expression profiles.
So kind of like Tinder, but for cells.
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