semvec/pypi-packages huggingface dataset:
rgevolve 0.1.1
rgevolve metapackage: install all rgevolve components at once
License: MIT
Requires Python: >=3.8

# rgevolve metapackage

## Install all rgevolve packages at once

`pip install rgevolve` installs the `rgevolve-core` package containing the core functionality and all available subpackages containing Renormalization Group Evolution matrices in various EFTs and bases.

This is an empty metapackage for user convenience.
scdrakex 1.1.5
scGPT embedding extension for scDrake
License: MIT
Requires Python: >=3.8

# scDrake's scGPT Extension

This code is fork from [scGPT](https://github.com/bowang-lab/scGPT), which is a single-cell GPT model for gene expression data [^1]. This extension adds embedding calculation support for the [scDrake framework](https://github.com/bioinfocz/scdrake)[^2], which is a framework for building and deploying single-cell data analysis.
This extension is designed to work with the scDrake framework and trying to provide a set of tools for building and deploying single-cell models.

## Installation

## Dependencies

- Python >= 3.8
- PyTorch >= 2.3.0

## install

```{bash}
$ python -m pip install torch scdrakex
```

## install for development

```{bash}
$ python -m pip install -e .
```

## Pretrained scGPT Model

available at [scGPT](https://github.com/bowang-lab/scGPT?tab=readme-ov-file#pretrained-scgpt-model-zoo)

## Usage

```python
from scdrakex.task.rembedding import r_embed_data

# count_matrix: a sparse matrix of shape (n_cells, n_genes)
# genes: a list of gene symbols
# model_dir: the path to the Pretrained scGPT Model directory
# output: ndarray of shape (n_cells, n_embedding)

embeding = r_embed_data(
    count_matrix = count_matrix,
    genes = genes,
    model_dir = "scGPT_human",
)
```

## References

[^1]: [Cui, H., Wang, C., Maan, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 21, 1470–1480 (2024).](https://www.nature.com/articles/s41592-024-02201-0)

[^2]: [Kubovčiak, J., Kolář, M., & Novotný, J. (2023). Scdrake: a reproducible and scalable pipeline for scRNA-seq data analysis. Bioinformatics Advances, 3(1). https://doi.org/10.1093/bioadv/vbad089](https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad089/7220500)