Metadata-Version: 2.4
Name: BacSC
Version: 0.1.0
Summary: A general workflow for bacterial single-cell RNA sequencing data analysis
Project-URL: Homepage, https://github.com/bio-datascience/BacSC
Project-URL: Issues, https://github.com/bio-datascience/BacSC/issues
Author-email: Johannes Ostner <johannes.ostner@online.de>
License: MIT License
        
        Copyright (c) 2024 johannesostner
        
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License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.11
Requires-Dist: anndata
Requires-Dist: kdepy
Requires-Dist: leidenalg
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scanpy
Requires-Dist: scipy
Requires-Dist: seaborn
Requires-Dist: statsmodels
Requires-Dist: venn
Description-Content-Type: text/markdown

# BacSC: A general workflow for bacterial single-cell RNA sequencing data analysis

![BacSC](misc/BacSC-logo-modern.png)

This repository contains the BacSC pipeline for bacterial single-cell sequencing data analysis (Ostner et al., 2024). 
It also contains all code needed to reproduce the analyses in the publication (TODO).

For usage info, please refer to the tutorials in the respective directory.

Raw and intermediate data objects can be downloaded on [zenodo](https://zenodo.org/records/12189002). 
Simply download the `data` directory from there and move it into the BacSC directory.

![BacSC](misc/concept_fig_full.png)

## Installation

## Usage

The `tutorials` directory contains two tutorial notebooks. 
The easiest way to use BacSC on your data is to copy these notebooks and replace the respective paths at the top of the notebooks.
  - `BacSC_tutorial_general_pipeline.ipynb` shows how to perform Variance stabilization, dimension reduction and clustering.
  - `BacSC_cluster_analysis_tutorial.ipynb` gives a template for differential expression testing with BacSC.

## Repository structure

This repository is structured as follows:

- The `tools` directory contains all functions and code used throughout the BacSC pipeline.
- The `tutorials` directory contains two tutorial notebooks. 
The easiest way to use BacSC on your data is to copy these notebooks and replace the respective paths at the top of the notebooks.
  - `BacSC_tutorial_general_pipeline.ipynb` shows how to perform Variance stabilization, dimension reduction and clustering.
  - `BacSC_cluster_analysis_tutorial.ipynb` gives a template for differential expression testing with BacSC.
- The `analysis` directory provides notebooks for reproduction of the results in the publication (TODO). 
Grouped by dataset source, there are 3 notebooks for each dataset:
  - `<dataset>_final_pipeline` (or `<dataset>_final_pipeline_2`) performs the general BacSC analsis up to cell type clustering.
  - `<dataset>_cluster_analysis` performs DA testing on the clusters determined before.
  - `<dataset>_summary_figures` generates the figures for each dataset in the manuscript.
