https://github.com/keller-mark/miqc-py
Python implementation of miQC: probablistic quality control of scRNA-seq data
https://github.com/keller-mark/miqc-py
Last synced: 4 months ago
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Python implementation of miQC: probablistic quality control of scRNA-seq data
- Host: GitHub
- URL: https://github.com/keller-mark/miqc-py
- Owner: keller-mark
- License: mit
- Created: 2024-07-27T19:22:35.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-08T14:37:15.000Z (10 months ago)
- Last Synced: 2024-08-08T17:07:19.687Z (10 months ago)
- Language: Python
- Homepage:
- Size: 1.69 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# miqc-py
[](https://pypi.org/project/miqc_py)
Python implementation of [miQC](https://github.com/greenelab/miQC) ([paper](https://doi.org/10.1371/journal.pcbi.1009290)).
## Installation
```sh
pip install miqc_py
```
## Usage
Usage follows the scverse API conventions.
Parameter names follow the R implementation of `miQC`.
```python
import miqc_py
# ...
miqc_py.calculate_miqc(adata)
miqc_py.filter_miqc(adata)
```
### Plotting
Optionally, we can plot the results with `altair` (not a dependency of `miqc_py` - may need to install first).
```python
import altair as alt
alt.Chart(adata.obs).mark_circle().encode(
x="total_genes_by_counts:Q",
y="pct_counts_mito:Q",
color="prob_compromised:Q"
)
```
## Development
```sh
conda env create -f environment.yml
conda activate miqc-py
```
```sh
jupyter lab
```