semvec/pypi-packages huggingface dataset:
sciPENN 0.9.6
A package for integrative and predictive analysis of CITE-seq data
Requires Python: >=3.7

# sciPENN

sciPENN (**s**ingle **c**ell **i**mputation **P**rotein **E**mbedding **N**eural **N**etwork) is a deep learning computational tool that is useful for analyses of CITE-seq data. sciPENN can be used to:

1. Predict proteins in a query scRNA-seq dataset from a reference CITE-seq dataset.
2. Integrate the scRNA-seq and CITE-seq data into a shared latent space.
3. Combine multiple CITE-seq datasets with different protein panels by imputing missing proteins for each CITE-seq dataset.
4. Transfer cell-type labels from a reference CITE-seq dataset to a query scRNA-seq dataset.

## Reproducibility

To find code to reproduce the results we generated in that paper, please visit this separate [github repository](https://github.com/jlakkis/sciPENN_codes), which provides all code (including that for other methods) necessary to reproduce our results.

## Installation

Recomended installation procedure is as follows. 

1. Install [Anaconda](https://www.anaconda.com/products/individual) if you do not already have it. 
2. Create a conda environment, and then activate it as follows in terminal.

```
$ conda create -n scipennenv
$ conda activate scipennenv
```

3. Install an appropriate version of python.

```
$ conda install python==3.7
```

4. Install nb_conda_kernels so that you can change python kernels in jupyter notebook.

```
$ conda install nb_conda_kernels
```

5. Finally, install sciPENN.

```
$ pip install sciPENN
```

Now, to use sciPENN, always make sure you activate the environment in terminal first ("conda activate scipennenv"). And then run jupyter notebook. When you create a notebook to run sciPENN, make sure the active kernel is switched to "scipennenv"

## Usage

A [tutorial jupyter notebook](https://drive.google.com/drive/folders/1iY4s76UYNMFvF6v3XN4JxD9gM77NIxoH?usp=sharing), together with a dataset, is publicly downloadable.

## Software Requirements

- Python >= 3.7
- torch >= 1.6.1
- scanpy >= 1.7.1
- pandas >= 1.1.5
- numpy >= 1.20.1
- scipy >= 1.6.1
- tqdm >= 4.59.0
- anndata >= 0.7.5
- numba <= 0.50.0
slg-ff-helpers 0.2.0
This is a description.
Requires Python: >=3.6.3

# Python Package Boilerplate

This is an minimal example of how to publish a python package.

You can follow along with the [guide](https://nrempel.com/how-to-publish-a-python-package-to-pypi).


### WARNING: do not run apply_custom_naming.py from another directory. It will delete things (potentially).
### INFO: If no script name is entered, it will delete both the scripts directory and scripts line from setup.py
To apply a custom name to your package, run:

python3 apply_custom_naming.py
PreProcessingNinja 0.0.1
A data preprocessing helper consists of your basic preprocessing needs
Requires Python: >=3.6
Keywords: preprocessing,preprocessing ninja

# PreProcessing Ninja

A PreProcessing library for your basic PreProcessing needs

[![forthebadge](https://forthebadge.com/images/badges/made-with-python.svg)](https://www.python.org)

[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)  
## Features of the package

 - impute_missing_value: Helps to impute missing values
 - scale_data: Scales the data
 - encode_categorical_data: Helps in encoding categorical variables
 - remove_outliers: Helps in removing outliers
## Use
```cmd
pip install PreProcessingNinja
```


  
## Example

```python
from preprocessing_ninja import PreProcessingNinja
ninja = PreProcessingNinja()

#creating column dictionary
d = {
    'column1':'mean',
    'column2':'mean',
    'column3':'most_frequent'
}

#calling method
df = ninja.impute_missing_value(datafra,e, d)
```

  
## Note

There might be bugs.

  
## Authors

- [@BijoyKumarRoy](https://www.linkedin.com/in/bijoy-kumar-roy-4b0975189/)