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
Description: # 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).
        
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
