### Run GSEA with toy data

Source: https://decoupler.readthedocs.io/en/latest/api/generated/decoupler.mt.gsea.html

This example demonstrates how to run the GSEA method using toy data. Ensure you have the decoupler library installed and toy data loaded.

```python
import decoupler as dc
adata, net = dc.ds.toy()
dc.mt.gsea(adata, net, tmin=3)
```

--------------------------------

### Install Minimal Decoupler from PyPI

Source: https://decoupler.readthedocs.io/en/latest/index.html

Installs the latest stable release of decoupler with minimal dependencies from the Python Package Index.

```bash
pip install decoupler

```

--------------------------------

### Install Full Decoupler from PyPI

Source: https://decoupler.readthedocs.io/en/latest/index.html

Installs the latest stable release of decoupler including extra dependencies from the Python Package Index.

```bash
pip install decoupler[full]

```

--------------------------------

### Install Development Version of Decoupler

Source: https://decoupler.readthedocs.io/en/latest/index.html

Installs the latest development version of decoupler directly from the GitHub repository.

```bash
pip install git+https://github.com/scverse/decoupler.git@main

```

--------------------------------

### Load Packages and Set Up

Source: https://decoupler.readthedocs.io/en/latest/notebooks/spatial/rna_visium.html

Imports necessary libraries like scanpy and decoupler, and sets up plotting parameters. Ignores future warnings for cleaner output.

```python
import warnings

import scanpy as sc
import decoupler as dc

warnings.filterwarnings("ignore", category=FutureWarning)
sc.set_figure_params(figsize=(3, 3), frameon=False)
```

--------------------------------

### Load RNA Data

Source: https://decoupler.readthedocs.io/en/latest/_sources/notebooks/bulk/rna.ipynb

Example of loading RNA sequencing data using a common bioinformatics library. Ensure the library is installed and data is in the correct format.

```python
from rna_tools import load_rna_data

rna_data = load_rna_data("path/to/your/rna_data.csv")
```

--------------------------------

### Run MDT with toy data

Source: https://decoupler.readthedocs.io/en/latest/api/generated/decoupler.mt.mdt.html

Demonstrates how to load toy data and run the MDT method with a specified minimum number of targets per source. Ensure data is normalized if using raw counts.

```python
import decoupler as dc

adata, net = dc.ds.toy()
dc.mt.mdt(adata, net, tmin=3)

```

--------------------------------

### Load Toy Dataset

Source: https://decoupler.readthedocs.io/en/latest/_sources/notebooks/example.ipynb

Loads a small, sample dataset and a corresponding network for demonstration purposes. This dataset is suitable for quick testing and understanding the package's functionality.

```python
adata, net = dc.ds.toy()
```

--------------------------------

### Example Leading Edge Genes Output

Source: https://decoupler.readthedocs.io/en/latest/notebooks/scell/rna_psbk.html

This is an example output showing the first five leading edge genes for a pathway.

```text
leading edge: ['NR4A2' 'JUN' 'SQSTM1' 'JUNB' 'IER5']
```

--------------------------------

### toy_bench()

Source: https://decoupler.readthedocs.io/en/latest/api/generated/decoupler.ds.toy_bench.html

Generates a toy AnnData and network for testing the benchmark pipeline. It accepts optional parameters for shuffling, seeding, and verbosity.

```APIDOC
## toy_bench()

### Description
Generate a toy adata and net for testing the benchmark pipeline.

### Parameters
#### Arguments
- **shuffle_r** (`float`, optional, default=`0.25`) – Percentage of the ground truth to randomize.
- **seed** (`int`, optional, default=`42`) – Random seed to use.
- **verbose** (`bool`, optional, default=`False`) – Whether to display progress messages and additional execution details.
- **kwargs** – All other keyword arguments are passed to `decoupler.ds.toy`.

### Returns
AnnData and net examples.

### Example
```python
import decoupler as dc

adata, net = dc.ds.toy_bench()
adata, net
```
```

--------------------------------

### Basic RNA Data Loading and Preprocessing

Source: https://decoupler.readthedocs.io/en/latest/_sources/notebooks/scell/rna_psbk.ipynb

Demonstrates loading an AnnData object and performing basic preprocessing steps like calculating QC metrics and normalizing counts.

```python
import scanpy as sc

# Load the dataset
adata = sc.read_h5ad('adata.h5ad')

# Compute quality control metrics
adata.obs['percent_mt'] = sc.pp.calculate_qc_metrics(adata, percent_top=None, log1p=False, inplace=True)

# Normalize counts per cell
sc.pp.normalize_total(adata, target_sum=1e4)

# Logarithmize the data
sc.pp.log1p(adata)
```

--------------------------------

### Install Decoupler from Conda-Forge

Source: https://decoupler.readthedocs.io/en/latest/index.html

Installs the latest stable version of decoupler from the conda-forge channel using mamba or conda. Ensure to use the '-py' suffix.

```bash
mamba create -n=dcp conda-forge::decoupler-py

```

--------------------------------

### Toy Datasets

Source: https://decoupler.readthedocs.io/en/latest/_sources/api/ds.md

Functions to access toy datasets for testing and examples.

```APIDOC
## ds.toy

### Description
Access the toy dataset.

### Method
`ds.toy()`

## ds.toy_bench

### Description
Access the toy_bench dataset.

### Method
`ds.toy_bench()`
```

--------------------------------

### Run WAGGR with toy data

Source: https://decoupler.readthedocs.io/en/latest/api/generated/decoupler.mt.waggr.html

This snippet demonstrates how to use the `waggr` function with toy data. It first loads toy data and then applies the `waggr` method with a specified minimum number of targets per source.

```python
import decoupler as dc

ada, net = dc.ds.toy()
dc.mt.waggr(adata, net, tmin=3)
```