Metadata-Version: 2.1
Name: ConSReg
Version: 1.0.4
Summary: condition-specific regulation
Home-page: https://github.com/LiLabAtVT/ConSReg
Author: Qi Song
Author-email: alexsong@vt.edu
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=2.7
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas (==0.21.1)
Requires-Dist: joblib
Requires-Dist: rpy2 (==2.8.6)
Requires-Dist: networkx (>=2)
Requires-Dist: sklearn
Requires-Dist: intervaltree

# ConSReg
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
Condition-specific regulations

# Getting Started
## 1. Installation
### 1.1 Python installation
ConSReg can be installed from pip:
```shell
pip install --user ConSReg
```
### 1.2 R installation
ConSReg requires several R packages: `ChIPseeker`, `CoReg`, `gglasso` and `RRF`.

To install `ChIPSeeker` from bioconductor, type the following commands in R environment:
```R
source("https://bioconductor.org/biocLite.R")
biocLite("ChIPseeker")
```
Please refer to the instructions described [here](https://bioconductor.org/packages/release/bioc/html/ChIPseeker.html) for more details.

To install `CoReg` pakcage from GitHub, type the following commands in R environment:
```R
install.packages("devtools")
library(devtools)
install_github("LiLabAtVT/CoReg")
```
Please refer to the GitHub page of `CoReg` project for more details: 
[link](https://github.com/LiLabAtVT/CoReg)

To install `gglasso` package from CRAN, type the following commands in R environment:
```R
install.pacakges("gglasso")
```
Please refer to the link [here](https://cran.r-project.org/web/packages/gglasso/index.html) for more details.

To install `RRF` package from CRAN, type the following commands in R environment:
```R
install.pacakges("RRF")
```
Please refer to the link [here](https://cran.r-project.org/web/packages/RRF/index.html) for more details.

## 2. Sample datasets
Sample datasets can be found in `data` folder.

## 3. Analysis
We provide code for analyzing the sample datasets in two jupyter notebooks located in the root folder of this project: **bulk_analysis.ipynb** (for bulk RNA-seq data) and **single_cell_analysis.ipynb** (for single cell RNA-seq data).


