semvec/pypi-packages huggingface dataset:
SportStatIQ-DataCollectors 0.0.0
Collects data from various sports websites
License: MIT

# DataCollector

This Python class provides a data collection functionality through the `NbaDataCollector` library.

## Class: `DataCollector`

### Initialization
```python
def __init__(self)
```
This function initializes the `DataCollector` class.

### Method: `NbaDataCollector`
```python
def NbaDataCollector(self)
```
This function returns the `NbaDataCollector` library.

Returns:
- `NbaDataCollector`: The `NbaDataCollector` library.

## Usage Example

```python
# Instantiate the DataCollector class
collector = DataCollector()

# Access the NbaDataCollector library
nba_collector = collector.NbaDataCollector()

# Use the NbaDataCollector library for data collection
# ...
```

Please note that the `NbaDataCollector` library is not defined within the provided code snippet, and it is assumed to be a separate module or library that is imported by the `DataCollector` class. Make sure to import or define the `NbaDataCollector` library accordingly for the code to work properly.
devcellpy 1.1.2
DevCellPy is a Python package designed for hierarchical multilayered classification of cells based on single-cell RNA-sequencing (scRNA-seq). It implements the machine learning algorithm Extreme Gradient Boost (XGBoost) (Chen and Guestrin, 2016) to automatically predict cell identities across complex permutations of layers and sublayers of annotation.
License: MIT
Keywords: python,cardiology,scRNA,genetics,machine learning,biology,bioinformatics,devcellpy

## DevCellPy
DevCellPy is a Python package designed for hierarchical multilayered classification of cells based on single-cell RNA-sequencing (scRNA-seq). It implements the machine learning algorithm Extreme Gradient Boost (XGBoost) (Chen and Guestrin, 2016) to automatically predict cell identities across complex permutations of layers and sublayers of annotation.

Given DevCellPy's highly customizable classification scheme, users can input the annotation hierarchy of their scRNA-seq datasets into DevCellPy to guide the automatic classification and prediction of cells according to the provided hierarchy. DevCellPy allows users to designate any identity at each layer of classification and is not constrained by cell type——for example, assigning timepoint as one of the annotation layers allows for cell identity predictions at that layer to be conditioned on the age of the cells. In addition to hierarchical cell classification, DevCellPy implements the SHapley Additive exPlanations (SHAP) package (Lundberg etal, 2020), which provides the user with interpretability methods for the model and determines the positive and negative gene predictors of cell identities across all annotation layers.

We provide a comprehensive tutorial on DevCellPy's usage as well as overall concepts in its design in the tutorial folder of the DevCellPy GitHub.

Paper: Galdos Xu etal. 2022

## Installation
``` pip install devcellpy```

## How to use it?
The tutorial can be found on our GitHub: https://github.com/DevCellPy-Team/DevCellPy

## License

Copyright (c) 2021 Stanford University

This repository is licensed under the MIT license.
See LICENSE for details.