S-BrainXcan takes GWAS as input and return the association between GWAS phenotype and a list of brain image-derived phenotypes.
BrainXcan manuscript link.
Software documentation (Quick start) link.
BrainXcan database link.
Analysis scripts link.
https://github.com/liangyy/brainxcan is deprecated. Please go to https://github.com/hakyimlab/brainxcan for the latest BrainXcan software.
The software is built upon both Python and R scripts along with some standalone executables.
Here we provide a conda environment containing all the Python dependencies and snakemake.
conda env create -f environment.yml
Also, install plink 1.9 which will be used for LD clumping in MR analysis.
By default, the pipeline call python, Rscript, and plink as is.
And you can provide the path to the desired executables in the configuration file. For instance,
# in config.[name].yaml
rscript_exe: 'path-to/Rscript'
python_exe: 'path-to/python'
plink_exe: 'path-to/plink'
R dependencies are: ggplot2, dplyr, optparse, logging, rmarkdown, pdftools, patchwork, oro.nifti, data.table, pander, arrow, TwoSampleMR.
Below, we provide an example for installing R dependencies as a conda environment. Any standalone R installation with these dependent packages being installed should work just fine.
$ conda create -n r_36 -y
$ conda activate r_36
(r_36) $ conda install -c r r
(r_36) $ conda install -c conda-forge r-arrow
(r_36) $ conda install -c conda-forge r-pdftools
(r_36) $ conda install -c conda-forge r-gmp
(r_36) $ conda install -c conda-forge r-rio
(r_36) $ conda install -c conda-forge r-pander
(r_36) $ conda install -c conda-forge r-sf
(r_36) $ conda install -c conda-forge r-stars
(r_36) $ conda install -c conda-forge r-plotly
(r_36) $ conda install -c conda-forge r-ggnewscale
(r_36) $ R
# inside R
> install.packages(c('ggplot2', 'dplyr', 'logging', 'optparse', 'rmarkdown', 'patchwork', 'oro.nifti', 'data.table', 'remotes', 'raster', 'rgeos'))
> remotes::install_github("MRCIEU/TwoSampleMR")