semvec/pypi-packages huggingface dataset:
lightspace 0.0.1
The lightspace package
Requires Python: >=3.6

# Lightspace

Lightspace is a Python SDK and CLI for creating streaming video inferencing applications.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install lightspace.

```bash
pip install lightspace
```

## Usage

```python
import lightspace

# initializes acquisition
lightspace.acquisition.something()

# initializes gateway
lightspace.gateway.something()

# initializes compute
lightspace.compute.something()
```

## Contributing

No contributions allowed at this time

## License

[MIT](https://choosealicense.com/licenses/mit/)
mtANN 1.0
Ensemble Multiple References for Single-cell RNA Seuquencing Data Annotation and Unseen Cells Identification
Requires Python: >=3.7

# Ensemble of multiple references for single-cell RNA sequencing data annotation and unseen cell-type identification

mtANN is a novel cell-type annotation framework
that integrates ensemble learning and deep learning
simultaneously, to annotate cells in a new query dataset with the help of multiple well-labeled reference datasets. It takes multiple well-labeled reference datasets and a query dataset that needs to be annotated as input. It begins with generating a series of subsets for each reference dataset by adopting various gene selection methods. Next, for each reference subset, a base classification model is trained based on neural networks. Then, mtANN annotates the cells in the query dataset by integrating the prediction results from all the base classification models. Finally, it identifies cells that may belong to cell types not observed in the reference datasets according to the uncertainty of the predictions.

![Figure1](Figure1.png)

## System Requirements

Python support packages: pandas, numpy, scanpy, scipy, sklearn, torch, giniclust3, rpy2

R support packages: limma, Seurat, parallel

## Versions the software has been tested on

### Environment 1
- System: Ubuntu 18.04.5
- Python: 3.8.8
- Python packages: pandas = 1.2.3, numpy = 1.19,2, scanpy=1.9.0, scipy = 1.6.1, sklearn = 0.24.1, torch = 1.9.1, giniclust3 = 1.1.0, rpy2 = 3.5.2
- R: 3.6.1
- R packages: limma = 3.42.2, Seurat = 3.1.1, parallel = 3.6.1

### Environment 2
- System: Windows 10
- Python: 3.7.6
- Python packages: pandas = 1.3.5, numpy = 1.21.6, scanpy=1.9.3, scipy = 1.7.3, scikit-learn = 1.0.2, torch = 1.13.0, giniclust3 = 1.1.2, rpy2 = 3.5.11
- R: 4.1.2
- R packages: limma = 3.50.3, Seurat = 4.2.0, parallel = 4.1.2

# Installation

`pip install mtANN==1.0`

To successfully use mtANN, please ensure that R is correctly installed and added to the environment variables. Additionally, you need to add a new user variable named `R_USER` that points to the installation path of the Python package `rpy2`.

# Useage
The mtANN repository includes the mtANN code files in the `mtANN` folder and provides a usage example `example` which specifically shows the format of the input data and the usage of the main function. The data used in the example can be downloaded at [https://doi.org/10.5281/zenodo.7922657](https://doi.org/10.5281/zenodo.7922657). 

The input data considered by the current version of mtANN is in `csv` format, where rows are samples and columns are features. In addition, its cell type information is stored in another csv file, and its naming format is the name of the dataset + `_label`.

 
# Contact

Please do not hesitate to contact Miss Yi-Xuan Xiong ([xyxuana@mails.ccnu.edu.cn](xyxuana@mails.ccnu.edu.cn)) or Dr. Xiao-Fei Zhang ([zhangxf@ccnu.edu.cn](zhangxf@ccnu.edu.cn)) to seek any clarifications regarding any contents or operation of the archive.