semvec/pypi-packages huggingface dataset:
sc-var 1.3.0
An approch for interpreting disease-associated human variants using single-cell epigenomics
Requires Python: <4.0,>=3.11

# SC-VAR

Here we report a human variants interpreter, named SC-VAR. 


# What can sc-var do?

This tool can interpret disease-related risks with whole genome wide (including both coding and non-coding regions) variants from GWAS studies and single-cell data on four layers: risk CREs,risk genes, gene sets, and cell types.




# How to install?

pip install sc-var







## About



![Fig1](https://github.com/user-attachments/assets/ccb0e726-225b-4cfa-a71e-2714c92c76b9)






## Usage

Check https://github.com/gefeiZ/sc_var/tree/main/Vignettes for details



Data request: 

Single cell data &

Peak co-accessibility Data or Peak to gene linkage Data (which could obtained from single cell data using cicero or signac) &

GWAS data
upfe 0.1.0
A package for universal pathological feature extraction.
Requires Python: >=3.8

# Universal Pathological Features Extractor

A package for universal pathological feature extraction.

## Installation

You can install MyAwesomePackage using pip:

```bash
pip install upfe
```

## Quickstart

Here's a simple example of how to use UPFE:

```python
tissues = ["BACK","DEB","LIN","LYM","STR","TUM"]
NUM_CLASSES = len(tissues)
label_dict = {i:label for label,i in zip(tissues,range(0,NUM_CLASSES))}
print(label_dict)

### initiate
csv_path = "./patch_predict.csv"

extractor = feature_extractor(
    output_df=csv_path,
    label_dict=label_dict)

### get original matrix
extractor.get_matrix()

### dilate for pericancerous area
extractor.dilate()

### feature extractor
extractor.calculate_first_order()
extractor.feature["First_order"].head()

extractor.calculate_interaction()
extractor.feature["Interaction"].head()

extractor.calculate_spatial(method = "DBSCAN",verbose = True)
extractor.feature["Spatial"].head()
df_points = extractor.points_cluster

### output
re = extractor.output()
re.head()
```

## Documentation

For more detailed information, check out the [official documentation](https://myawesomepackage.readthedocs.io).

## License

This project is released under Apache License 2.0. You can view the complete license text through the following link:

[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)

This project relies on multiple open-source libraries, which may have different licenses. The following is an overview of the main dependencies and their licenses:

- **pyvips**: Using [LGPL 2.1](https://libvips.github.io/libvips/Licensing.html) License. Due to the dynamic linking of this project to `pyvips`, users need to ensure compliance with the terms of LGPL 2.1, especially when distributing or modifying the software.

- Other dependencies (such as NumPy, Pandas, OpenCV, etc.) are licensed under a permissive license (such as BSD or MIT) that allows for free use and distribution.

## Important Note

Although this project itself adopts the Apache 2.0 license, due to the dynamic linking of pyvips, users need to ensure compliance with the requirements of its LGPL 2.1 license.
adversarialpytorchattack 0.1.0
A PyTorch package for adversarial attacks (FGSM, PGD, CW, MIFGSM, AutoAttack) with visualization.
Requires Python: >=3.7

## 📦 Installation
Install the package via pip:
```sh
pip install adversarialpytorchattack

## 📦 Or installation using Github 
Or install from source
```sh
git clone https://github.com/santhoshatwork17@gmail.com/adversarial_pytorch_attackers.git
cd adversarial_pytorch_attack


## 🛠️ Usage
```python
import torch
import torchvision.models as models
from adversarialpytorchattack.attacks import FGSM, PGD, CW
from adversarialpytorchattack.utils import visualize_perturbation, save_image

# pre-trained model
model = models.resnet18(pretrained=True).eval()

# input image
image = torch.rand((1, 3, 224, 224))  # Example input
label = torch.tensor([0])  # Example label

# Apply attack
fgsm_attack = FGSM(model, epsilon=0.03)
adv_image = fgsm_attack(image, label)

# Save the adversarial image
save_image(adv_image, "adv_fgsm.jpg")