https://github.com/fabilab/scquill
Approximate any single cell data set, saving >99% of memory and runtime.
https://github.com/fabilab/scquill
Last synced: 6 months ago
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Approximate any single cell data set, saving >99% of memory and runtime.
- Host: GitHub
- URL: https://github.com/fabilab/scquill
- Owner: fabilab
- License: mit
- Created: 2023-11-06T16:27:26.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-17T03:27:10.000Z (over 1 year ago)
- Last Synced: 2024-12-17T04:27:13.692Z (over 1 year ago)
- Language: Python
- Size: 540 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# 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'])
```
To show a neighborhood plot:
```
scquill.pl.neighborhoodplot(app, ['gene1', 'gene2', 'gene3'])
```
To show embeddings of cell neighborhoods, similar to single-cell UMAPs:
```
scquill.pl.embedding(app, ['gene1', 'gene2', 'gene3'])
```
**MORE TO COME**
## Authors
Fabio Zanini @[fabilab](https://fabilab.org)