Metadata-Version: 2.1
Name: burstlink
Version: 1.1.2
Summary: A user-friendly package for analyzing gene interactions and transcriptional bursting.
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: statsmodels>=0.12.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: pyarrow
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: decorator
Requires-Dist: seaborn
Requires-Dist: gseapy
Requires-Dist: rpy2

<img src="https://raw.githubusercontent.com/LiyingZhou12/burstlink/main/docs/_static/image/logo.png" alt="Logo" width="450"/>            

## Brief introduction
BurstLink is a Python package to infer the coupled dynamics of gene regulatory interactions and transcriptional bursting from single-cell transcriptomics or multi-omics data.
It contains many intuitive visualization and downstream analysis tools, providing a great practical toolbox for biomedical researchers.

### The coupled dynamics betweem gene regulatory interactions and transcriptional bursting
Transcriptional bursts are inherently dynamic and stochastic processes, which are influenced by gene-gene regulatory interactions through transcription factors from other genes, ultimately driving highly heterogeneous gene expression within cells.  A question is to explore how dynamic gene-gene interaction-constructed regulatory network dictates bursting on a genome-wide scale. 

<img src="https://raw.githubusercontent.com/LiyingZhou12/burstlink/main/docs/_static/image/question.png" alt="question.png" width="850"/>

### Inference workflow 
BurstLink is a user-friendly package without the hyperparameter tuning, which is mainly utilized to infer coupled dynamics of gene regulatory interactions and transcriptional bursting, given scRNA-seq data of any two genes (screened by scATAC-seq data, optional). BurstLink enables gene-pair inference and supports rapid whole-genome inference and a series of downstream analyses.

<img src="https://raw.githubusercontent.com/LiyingZhou12/burstlink/main/docs/_static/image/workflow.png" alt="workflow.png" width="850"/>

## Installation
### System requirements
Recommended operating systems: macOS or Linux. BurstLink was developed and tested on Linux and macOS.
### Python requirements
BurstLink was developed using python 3.8.
### Installation using `pip`
We suggest setting up BurstLink in a separate `mamba` or `conda` environment to prevent conflicts with other software dependencies. Create a new Python environment specifically for BurstLink and install the required libraries within it.

```bash
mamba create -n burstlink_env python=3.8 r-base=4.3.2
mamba activate burstlink_env
pip install burstlink
```
if you use `conda`, `r-base=4.3.2` may not included in the channels. Instead, you can `r-base=4.3.1` in `conda`.

## Documentation, and Tutorials

For more realistic and simulation examples, please see BurstLink documentation that is available through the link https://burstlink.readthedocs.io/en/latest/.

## Quick start
Let's get a quick start on using burstlink to infer gene regulatory interactions and transcriptional bursting by using simulation data generated from the genetic toggle switch dynamic model.

### Import packages
```python
import os, math
import numpy as np
import pandas as pd
import burstlink as bl
```

### Setting work dictionary
To run the examples, you'll need to download the some pre-existing files in `docs/tutorials/simulated_data` folder and change your working directory to the `simulated_data` folder.

```python
os.chdir("your_path/simulated_data")
```

### scRNA-seq data from simulation
```python
simul_data = np.asarray(pd.read_csv(os.path.abspath('data/simul_data_example1.csv')))[:, 1::]
```
You can also generate new simulation data with `_synthetic_data` :

```python
params = [2, 2, 1, 1, 15, 15, 1, 0.2, 1, 3, 0.01, 2000]
S_stable = bl._utils._synthetic_data.SSA_coexpression(params, verbose = 'no_burst', fig = False)
simul_data = S_stable.astype(int)
```
### Run the inference procedure

Import the scRNA-seq data and obatin the inference results, including (1) regulatory interactions: regulation types, regulation strengths, and regulation visualization; and (2) transcriptional bursting: burst frequencies, burst sizes, and expression variabilities.
```python
params = [2, 2, 1, 1, 15, 15, 1, 0.2, 1, 3, 0.01, 2000]
geneinfo = np.array(['X1', 'X2', -math.copysign(1, params[9]), -math.copysign(1, params[8])]).reshape([1, 4])
infer_results = bl.tools._burst_interactions.genepair_inference(simul_data, geneinfo, figflag = 1, verbose1 = True, verbose2 = False, verbose3 = 2, test = False)
```
<table>
  <tr>
    <td>
      <img src="https://raw.githubusercontent.com/LiyingZhou12/burstlink/main/docs/_static/image/example1-1.png" alt="Image 1" width="300">
    </td>
    <td>
      <img src="https://raw.githubusercontent.com/LiyingZhou12/burstlink/main/docs/_static/image/example1-2.png" alt="Image 2" width="300">
    </td>
    <td>
      <img src="https://raw.githubusercontent.com/LiyingZhou12/burstlink/main/docs/_static/image/example1-3.png" alt="Image 3" width="300">
    </td>
  </tr>
</table>



## Reference
BurstLink: a statistical mechanistic model to reveal global transcriptional dynamics with gene regulatory interactions from single-cell data.
