Discovery and characterization of variance QTLs in human induced pluripotent stem cells
OTHER License
A workflowr project.
Now published in
Sarkar AK, Tung PY, Blischak JD, Burnett JE, Li YI, et al. (2019) Discovery and characterization of variance QTLs in human induced pluripotent stem cells. PLOS Genetics 15(4): e1008045. https://doi.org/10.1371/journal.pgen.1008045
To ensure all contributors are using the same computational environment, we use conda to manage software dependencies (made possible by the bioconda and conda-forge projects). Please complete the following steps to replicate the computing environment. Note that this is only guaranteed to work on a Linux-64 based architecture, but in theory should be able to work on macOS as well. All commands shown below are intended to be run in a Bash shell from the root of the project directory.
Install Git and register for an account on GitHub
Download and install Miniconda (instructions)
Clone this repository (or your personal fork) using git clone
Create the conda environment "scqtl" using environment.yaml
conda env create --file environment.yaml
To use the conda environment, you must first activate it by running source activate scqtl
. This will override your default settings for R, Python, and
various other software packages. When you are done working on this project, you
can either logout of the current session or deactivate the environment by
running source deactivate
.
Initialize git-lfs and download latest version of large data files
git lfs install
git lfs pull
If there are updates to environment.yaml
, you can update the "scqtl"
environment by running conda env update --file environment.yaml
.
Warning: If you are using RStudio, you need to ensure that it recognizes
your conda environment. If you launch RStudio by clicking on an icon, it
doesn't use the current environment you have configured in your shell. On a
Linux-based system, the solution is to launch RStudio directly from the shell
with rstudio
. On macOS, running open -a rstudio .
from the shell causes
RStudio to recognize most of the environment variables, but strangely it does
not set the correct library path to the conda R packages. Suggestions for how
to fix this are welcome.