perturbvi 0.2.4
Infer regulatory modules through informative latent component model in the single-cell Perturb-seq data
Requires Python: >=3.9

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# perturbVI
`perturbvi` is a scalable approach to infer regulatory modules through informative latent component model in the single-cell Perturb-seq data.

  [**Installation**](#installation)
  | [**Example**](#get-started-with-example)
  | [**Notes**](#notes)
  | [**Version**](#version-history)
  | [**Support**](#support)
  | [**Other Software**](#other-software)

------------------

## Installation

``` bash
# install perturbvi
uv pip install perturbvi

# help
perturbvi --help
```

## Get Started with `perturbvi`

Perform inference using SuSiE PCA to find the regulatory modules from CRISPR perturbation data
``` bash
perturbvi <matrix> <guide> <z_dim> <l_dim> <tau> -o=<out_dir> --verbose
```

#### Arguments
- `matrix`: Path to the experiment CSV file.
- `guide`: Path to the guide CSV file.
- `z_dim`: Number of latent factors, Z dim (12).
- `l_dim`: Number of single effects, L dim (400).
- `tau`: Residual precision, Tau (800).
- `out_dir`: Specifies the output directory path.
- `--verbose`: For logging (Optional).

#### Example Usage
```bash
perturbvi luhmes_exp.csv luhmes_G.csv 12 400 800 -o=results --verbose
```

This will save all the files (including `params.pkl`) in `results` folder.

We can analyze results:
```python
import perturbvi

guide_path="luhmes_G.csv"
gene_symbol_path = "luhmes_gene_symbol.csv"

OUTPUT_DIR = "results"

G = pd.read_csv(guide_path, index_col=0)
G_reduce = G.drop(columns=["Nontargeting"])
# 14 genes perturbed
perturbed = G_reduce.columns.to_list()
# 6000 gene symbols (background genes)
genes = pd.read_csv(gene_path, header=None)[0].to_list()

results = perturbvi.utils.analyze_output(
  OUTPUT_DIR, 
  perturb_genes=perturbed, 
  background_genes=genes
)

# number of degs per w from PIP
print(results["num_deg_per_w"])

# number of degs per perturbed gene from LFSR
print(results["num_deg_per_perturbed_gene"])
```


## Notes

-   `perturbvi` uses [JAX](https://github.com/google/jax) with [Just In
    Time](https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html)
    compilation to achieve high-speed computation. However, there are
    some [issues](https://github.com/google/jax/issues/5501) for JAX
    with Mac M1 chip. To solve this, users need to initiate conda using
    [miniforge](https://github.com/conda-forge/miniforge), and then
    install `perturbvi` using `pip` in the desired environment.

## Version History

TBD

## Support

Please report any bugs or feature requests in the [Issue
Tracker](https://github.com/mancusolab/perturbvi/issues). If users have
any questions or comments, please contact Dong Yuan (<dongyuan@usc.edu>)
and Nicholas Mancuso (<nmancuso@usc.edu>).

## Other Software

Feel free to use other software developed by [Mancuso
Lab](https://www.mancusolab.com/):

-   [SuShiE](https://github.com/mancusolab/sushie): a Bayesian
    fine-mapping framework for molecular QTL data across multiple
    ancestries.
-   [MA-FOCUS](https://github.com/mancusolab/ma-focus): a Bayesian
    fine-mapping framework using
    [TWAS](https://www.nature.com/articles/ng.3506) statistics across
    multiple ancestries to identify the causal genes for complex traits.
-   [SuSiE-PCA](https://github.com/mancusolab/susiepca): a scalable
    Bayesian variable selection technique for sparse principal component
    analysis
-   [twas_sim](https://github.com/mancusolab/twas_sim): a Python
    software to simulate [TWAS](https://www.nature.com/articles/ng.3506)
    statistics.
-   [FactorGo](https://github.com/mancusolab/factorgo): a scalable
    variational factor analysis model that learns pleiotropic factors
    from GWAS summary statistics.
-   [HAMSTA](https://github.com/tszfungc/hamsta): a Python software to
    estimate heritability explained by local ancestry data from
    admixture mapping summary statistics.

------------------------------------------------------------------------

`perturbvi` is distributed under the terms of the
[MIT](https://spdx.org/licenses/MIT.html) license.


------------------------------------------------------------------------

This project has been set up using Hatch. For details and usage
information on Hatch see <https://github.com/pypa/hatch>.