| Name | scquill JSON |
| Version |
0.3.2
JSON |
| download |
| home_page | None |
| Summary | Approximate any single cell data set, saving >99% of memory and runtime. |
| upload_time | 2024-10-22 02:28:15 |
| maintainer | None |
| docs_url | None |
| author | Fabio Zanini |
| requires_python | <3.13,>=3.11 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://badge.fury.io/py/scquill)
<img src="https://raw.githubusercontent.com/fabilab/scquill/main/logo.png" width="150" height="150">
# scquill
Approximate any single cell data set, saving >99% of memory and runtime.
It's pronounced /ˈskwɪɹl̩//, like the [animal](https://en.wiktionary.org/wiki/squirrel).
## Approximating a single cell data set
```python
import scquill
q = scquill.Compressor(
filename='myscdata.h5ad',
output_filename='myapprox.h5',
celltype_column="cell_annotation",
)
q()
```
## Exploring an approximation
To load an approximation:
```
import scquill
app = scquill.Approximation(
filename='myapprox.h5',
)
```
To show a dot plot:
```
scquill.pl.dotplot(app, ['gene1', 'gene2', 'gene3'])
```
<img src="https://raw.githubusercontent.com/fabilab/scquill/main/dotplot.png" width="200">
To show a neighborhood plot:
```
scquill.pl.neighborhoodplot(app, ['gene1', 'gene2', 'gene3'])
```
<img src="https://raw.githubusercontent.com/fabilab/scquill/main/neighborhoodplot.png" width="350">
To show embeddings of cell neighborhoods, similar to single-cell UMAPs:
```
scquill.pl.embedding(app, ['gene1', 'gene2', 'gene3'])
```
<img src="https://raw.githubusercontent.com/fabilab/scquill/main/embeddings.png" width="750">
**MORE TO COME**
## Authors
Fabio Zanini @[fabilab](https://fabilab.org)
Raw data
{
"_id": null,
"home_page": null,
"name": "scquill",
"maintainer": null,
"docs_url": null,
"requires_python": "<3.13,>=3.11",
"maintainer_email": null,
"keywords": null,
"author": "Fabio Zanini",
"author_email": "fabio.zanini@unsw.edu.au",
"download_url": "https://files.pythonhosted.org/packages/4f/e2/d4cd0baf39e2c6a1df2743014ad0c0b0368ac910206c896bddb263a42e3f/scquill-0.3.2.tar.gz",
"platform": null,
"description": "[](https://badge.fury.io/py/scquill)\n\n<img src=\"https://raw.githubusercontent.com/fabilab/scquill/main/logo.png\" width=\"150\" height=\"150\">\n\n# scquill\nApproximate any single cell data set, saving >99% of memory and runtime.\n\nIt's pronounced /\u02c8skw\u026a\u0279l\u0329//, like the [animal](https://en.wiktionary.org/wiki/squirrel).\n\n\n## Approximating a single cell data set\n```python\nimport scquill\n\nq = scquill.Compressor(\n filename='myscdata.h5ad',\n output_filename='myapprox.h5',\n celltype_column=\"cell_annotation\",\n)\n\nq()\n```\n\n## Exploring an approximation\nTo load an approximation:\n```\nimport scquill\n\napp = scquill.Approximation(\n filename='myapprox.h5',\n)\n```\n\nTo show a dot plot:\n```\nscquill.pl.dotplot(app, ['gene1', 'gene2', 'gene3'])\n```\n<img src=\"https://raw.githubusercontent.com/fabilab/scquill/main/dotplot.png\" width=\"200\">\n\n\nTo show a neighborhood plot:\n```\nscquill.pl.neighborhoodplot(app, ['gene1', 'gene2', 'gene3'])\n```\n<img src=\"https://raw.githubusercontent.com/fabilab/scquill/main/neighborhoodplot.png\" width=\"350\">\n\nTo show embeddings of cell neighborhoods, similar to single-cell UMAPs:\n\n```\nscquill.pl.embedding(app, ['gene1', 'gene2', 'gene3'])\n```\n<img src=\"https://raw.githubusercontent.com/fabilab/scquill/main/embeddings.png\" width=\"750\">\n\n**MORE TO COME**\n\n## Authors\nFabio Zanini @[fabilab](https://fabilab.org)\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Approximate any single cell data set, saving >99% of memory and runtime.",
"version": "0.3.2",
"project_urls": null,
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "921918354d299523c86c633618127f961cf8add240aaad06eb28be7cb2c33e1e",
"md5": "850e683dfd8525cda8053ced88f1e4fa",
"sha256": "ce310266fe7bc80460faaa48b3cecf2ad2a67979366f8703c6b444022c99a52e"
},
"downloads": -1,
"filename": "scquill-0.3.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "850e683dfd8525cda8053ced88f1e4fa",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.13,>=3.11",
"size": 25992,
"upload_time": "2024-10-22T02:28:13",
"upload_time_iso_8601": "2024-10-22T02:28:13.384739Z",
"url": "https://files.pythonhosted.org/packages/92/19/18354d299523c86c633618127f961cf8add240aaad06eb28be7cb2c33e1e/scquill-0.3.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4fe2d4cd0baf39e2c6a1df2743014ad0c0b0368ac910206c896bddb263a42e3f",
"md5": "f7da46ad1e811f473d4b3fda1e4254e7",
"sha256": "b4212a2be9a39c84a64bd4dbdc53ac492acd0c2f839f7c4333ae90f52b67237b"
},
"downloads": -1,
"filename": "scquill-0.3.2.tar.gz",
"has_sig": false,
"md5_digest": "f7da46ad1e811f473d4b3fda1e4254e7",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.13,>=3.11",
"size": 19613,
"upload_time": "2024-10-22T02:28:15",
"upload_time_iso_8601": "2024-10-22T02:28:15.373512Z",
"url": "https://files.pythonhosted.org/packages/4f/e2/d4cd0baf39e2c6a1df2743014ad0c0b0368ac910206c896bddb263a42e3f/scquill-0.3.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-22 02:28:15",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "scquill"
}
