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condition-specific regulation

Project description


License: MIT Condition-specific regulations

Getting Started

1. Installation

1.1 Required packages

1.1.1 Python

  • python = 3.6
  • numpy == 1.16.2
  • scipy == 1.1.0
  • pandas == 0.21.1
  • joblib >= 0.11
  • rpy2==2.8.6
  • networkx >= 2
  • sklearn >= 0.19.1
  • intervaltree == 2.1.0

1.1.2 R

  • ChIPSeeker == 1.16.1
  • CoReg == 1.0.1
  • gglasso == 1.4
  • RRF == 1.9
  • R >= 3.5.1

1.2 R installation

1.2.1 install R

If R is not already installed, you may follow these steps to build R from source code. Otherwise, you may skip this section and start from 1.2.2

First, disable any conda environment, if there is an active one.

conda deactivate

Download R source code from CRAN ( 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.

# Download R 3.6.1

Decompress the downloaded file

tar -zvxf R-3.6.1

In the decompressed folder, configure R by:

./configure prefix=path_to_install_R --enable-R-shlib

--prefix= specifies a writeable directory to install R into. --enable-R-shlib flag was added to build R shared libraries.

In the decompressed folder, compile R


Install R into the specified directory:

make install

Add a line to ~/.bashrc to tell the OS where to look for R

export PATH=path_to_R_bin_directory:$PATH

Add the following line to ~/.bashrc. This is for telling rpy2 where to look for dynamic libraries.

export LD_LIBRARY_PATH=/home/alexsong/R/3.6.1/lib64/R/lib:$LD_LIBRARY_PATH

Apply the changes to environment variables PATH and LD_LIBRARY_PATH:

source ~/.bashrc

1.2.2 install R packages

ConSReg requires several R packages: ChIPseeker, CoReg, gglasso and RRF.

It 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:

conda deactivate

To install ChIPSeeker from bioconductor, type the following commands in R (for R 3.6 or higher version):

if (!requireNamespace("BiocManager", quietly = TRUE))


For older version of R, type the following commands in R:


Please refer to the instructions described here for more details.

To install CoReg pakcage from GitHub, type the following commands in R environment:


Please refer to the GitHub page of CoReg project for more details: link

To install gglasso package from CRAN, type the following commands in R environment:


Please refer to the link here for more details.

To install RRF package from CRAN, type the following commands in R environment:


Please refer to the link here for more details.

1.3 Python installation

We 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.

To create a new environment using conda:

conda create --name consreg python=3.6

Activate the new environment

conda activate consreg

Then ConSReg can be then installed using pip:

pip install ConSReg

Sometime 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:

pip install ConSReg --no-cache-dir

You may refer to for more information about installation and usage of Anaconda.

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).

4. Publication

ConSReg is currently in review at Genome Research. We will soon provide a pre-print version of our manuscript.

Qi Song, Jiyoung Lee, Shamima Akter, Ruth Grene, Song Li. Accurate prediction of condition-specific regulatory maps in Arabidopsis using integrated genomic data (in review)

Project details

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