condition-specific regulation
Project description
ConSReg
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 (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.
# Download R 3.6.1
wget https://cran.r-project.org/src/base/R-3/R-3.6.1.tar.gz
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
make
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))
install.packages("BiocManager")
BiocManager::install("ChIPseeker")
For older version of R, type the following commands in R:
source("https://bioconductor.org/biocLite.R")
biocLite("ChIPseeker")
Please refer to the instructions described here for more details.
To install CoReg
pakcage from GitHub, type the following commands in R environment:
install.packages("devtools")
library(devtools)
install_github("LiLabAtVT/CoReg")
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:
install.pacakges("gglasso")
Please refer to the link here for more details.
To install RRF
package from CRAN, type the following commands in R environment:
install.pacakges("RRF")
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 https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html 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)
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