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    "info": {
        "author": "Qi Song",
        "author_email": "alexsong@vt.edu",
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            "License :: OSI Approved :: MIT License",
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        "description": "# ConSReg\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\nCondition-specific regulations\n\n# Getting Started\n## 1. Installation\n### 1.1 Required packages\n#### 1.1.1 Python\n- python = 3.6\n- numpy == 1.16.2\n- scipy == 1.1.0\n- pandas == 0.21.1\n- joblib >= 0.11\n- rpy2==2.8.6\n- networkx >= 2\n- sklearn >= 0.19.1\n- intervaltree == 2.1.0\n#### 1.1.2 R\n- ChIPSeeker == 1.16.1\n- CoReg == 1.0.1\n- gglasso == 1.4\n- RRF == 1.9\n- R >= 3.5.1\n### 1.2 R installation\n#### 1.2.1 install R\nIf R is not already installed\uff0c you may follow these steps to build R from source code. Otherwise, you may skip this section and start from 1.2.2\n\nFirst, disable any conda environment, if there is an active one.\n```shell\nconda deactivate\n```\nDownload R source code from CRAN (https://cran.r-project.org/). You may use any version you like. It is recommended to use R version > 3.0.0. This ensures that rpy2 works correctly with R.\n```shell\n# Download R 3.6.1\nwget https://cran.r-project.org/src/base/R-3/R-3.6.1.tar.gz\n```\nDecompress the downloaded file\n```shell\ntar -zvxf R-3.6.1\n```\nIn the decompressed folder, configure R by:\n```shell\n./configure prefix=path_to_install_R --enable-R-shlib\n```\n`--prefix=` specifies a writeable directory to install R into. `--enable-R-shlib` flag was added to build R shared libraries.\n\nIn the decompressed folder, compile R\n```shell\nmake\n```\nInstall R into the specified directory:\n```shell\nmake install\n```\nAdd a line to ~/.bashrc to tell the OS where to look for R \n```shell\nexport PATH=path_to_R_bin_directory:$PATH\n```\nAdd the following line to ~/.bashrc. This is for telling rpy2 where to look for dynamic libraries.\n```shell\nexport LD_LIBRARY_PATH=/home/alexsong/R/3.6.1/lib64/R/lib:$LD_LIBRARY_PATH\n```\nApply the changes to environment variables `PATH` and `LD_LIBRARY_PATH`:\n```shell\nsource ~/.bashrc\n```\n#### 1.2.2 install R packages\nConSReg requires several R packages: `ChIPseeker`, `CoReg`, `gglasso` and `RRF`.\n\nIt is recommended to deactivate any conda environment when installing R packages, as it may add the environment-specific path which may fail the installation. If any conda environment is active, you may deactivate it by:\n```shell\nconda deactivate\n```\nTo install `ChIPSeeker` from bioconductor, type the following commands in R (for R 3.6 or higher version):\n```R\nif (!requireNamespace(\"BiocManager\", quietly = TRUE))\n    install.packages(\"BiocManager\")\n\nBiocManager::install(\"ChIPseeker\")\n```\nFor older version of R, type the following commands in R:\n```R\nsource(\"https://bioconductor.org/biocLite.R\")\nbiocLite(\"ChIPseeker\")\n```\nPlease refer to the instructions described [here](https://www.bioconductor.org/packages/release/bioc/html/ChIPseeker.html) for more details.\n\nTo install `CoReg` pakcage from GitHub, type the following commands in R environment:\n```R\ninstall.packages(\"devtools\")\nlibrary(devtools)\ninstall_github(\"LiLabAtVT/CoReg\")\n```\nPlease refer to the GitHub page of `CoReg` project for more details: \n[link](https://github.com/LiLabAtVT/CoReg)\n\nTo install `gglasso` package from CRAN, type the following commands in R environment:\n```R\ninstall.pacakges(\"gglasso\")\n```\nPlease refer to the link [here](https://cran.r-project.org/web/packages/gglasso/index.html) for more details.\n\nTo install `RRF` package from CRAN, type the following commands in R environment:\n```R\ninstall.pacakges(\"RRF\")\n```\nPlease refer to the link [here](https://cran.r-project.org/web/packages/RRF/index.html) for more details.\n### 1.3 Python installation\nWe recommend the users to create a new Python environment for ConSReg using Anaconda and install ConSReg in this environment. This can guarantee ConSReg work with correct dependencies. However, installing ConSReg without conda environment is also welcome.\n\nTo create a new environment using conda:\n```shell\nconda create --name consreg python=3.6\n```\nActivate the new environment\n```shell\nconda activate consreg\n```\nThen ConSReg can be then installed using pip:\n```shell\npip install ConSReg\n```\nSometime rpy2 may throw out error message when imported in Python. This problem may arise because rpy2 was built with the R version that is different from the one it is linked to when imported in Python. To fix this, you may remove rpy2 package then reinstall it with 'no-cache-dir' flag:\n\n```shell\npip install ConSReg --no-cache-dir\n```\nYou may refer to https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html for more information about installation and usage of Anaconda.\n\n## 2. Sample datasets\nSample datasets can be found in `data` folder.\n\n## 3. Analysis\nWe 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).\n\n## 4. Publication\nConSReg is currently in review at **Genome Research**. We will soon provide a pre-print version of our manuscript. \n\nQi Song, Jiyoung Lee, Shamima Akter, Ruth Grene, Song Li.  Accurate prediction of condition-specific regulatory maps in Arabidopsis using integrated genomic data (in review)\n\n\n",
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