Linux is the operating system of choice to run bioinformatics software. You will need either a computer running linux or or mac os, which has a linux-like environment.
download metaxcan repo
download torus repo
#R setup
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#install R packages install.packages("remotes")library(remotes)remotes::install_github("stephenslab/susieR") #gives the most up to date SusieR install.packages("coloc")install.packages("tidyverse")#install.packages("R.utils")#install.packages("remotes")# remotes::install_github("simingz/ctwas", ref = "develop")## make sure these are installed# library(data.table)# library(BEDMatrix)# library(Rfast)# library(susieR)# library(coloc)#installing base minicondainstall.packages("keras")reticulate::install_miniconda("miniconda")
Set the Ram to 5gb in order to create the environment.
#Box Data To upload the data. I first downloaded the box folder onto a local machine and then compressed the folders in the box folder to upload onto the server.
Notes: For some reason setting up the environment and uploading the data will continue to use a lot of background RAM set sure to close the project and reopen to close extraneous programs.
---title: "Lab Setup"output: html_documentdate: '2022-05-06'---copied from QGT 2023 posit## Setting up your own system Linux is the operating system of choice to run bioinformatics software. You will need either a computer running linux or or mac os, which has a linux-like environment. - [ ] install anaconda/miniconda - [ ] define imlabtools conda environment [how to here](https://github.com/hakyimlab/MetaXcan/blob/master/README.md#example-conda-environment-setup), which will install all the python modules needed for this analysis session- [ ] download data and software [from Box](https://uchicago.box.com/s/zhapf2zfxcpj7thvq4sjnqale3emleum). This will have copies of all the software repositories and the models- [ ] download software - download metaxcan repo - download torus repo- [ ] download prediction models from predictdb.org - [ ] install R/RStudio/tidyverse package- [ ] git clone https://github.com/hakyimlab/QGT-Columbia-HKI.git- [ ] start Rstudio (if you installed workflowr, you can just open the QGT-Columbia-HKI.Rproj)#R setup```{r eval=FALSE}#install R packages install.packages("remotes")library(remotes)remotes::install_github("stephenslab/susieR") #gives the most up to date SusieR install.packages("coloc")install.packages("tidyverse")#install.packages("R.utils")#install.packages("remotes")# remotes::install_github("simingz/ctwas", ref = "develop")## make sure these are installed# library(data.table)# library(BEDMatrix)# library(Rfast)# library(susieR)# library(coloc)#installing base minicondainstall.packages("keras")reticulate::install_miniconda("miniconda")```#Setting up Conda Environment```{bash, eval=FALSE}#adding pathexport PATH=$PATH:/cloud/project/miniconda/bin# adding repos/datagit clone https://github.com/hakyimlab/QGT-Columbia-HKI-repo.gitgit clone https://github.com/hakyimlab/MetaXcan.gitconda config --append channels conda-forgeconda config --append channels biocondaconda env create -f /cloud/project/MetaXcan/software/conda_env.yamlconda activate imlabtools```Set the Ram to 5gb in order to create the environment. #Box DataTo upload the data. I first downloaded the box folder onto a local machine and then compressed the folders in the box folder to upload onto the server. Notes:For some reason setting up the environment and uploading the data will continue to use a lot of background RAM set sure to close the project and reopen to close extraneous programs.