Transcriptome QGT Lab 2022 Setup

Published

June 14, 2022

The instructions in this blog were written to set up the lab in Rstudio cloud

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, which will install all the python modules needed for this analysis session
download data and software from Box. This will have copies of all the software repositories and the models
download software
- download metaxcan repo
download prediction models from predictdb.org
install R/RStudio/tidyverse package
install R packages
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

Code

#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")


#cTWAS install
install.packages("R.utils")
install.packages("remotes")
remotes::install_github("simingz/ctwas", ref = "develop")
library(ctwas)


## make sure these are installed
# library(data.table)
# library(BEDMatrix)
# library(Rfast)
# library(susieR)
# library(coloc)

#installing base miniconda
install.packages("keras")
reticulate::install_miniconda("miniconda")

Setting up Conda Environment

Code

#adding path
export PATH=$PATH:/cloud/project/miniconda/bin
# adding repos/data
git clone https://github.com/hakyimlab/QGT-Columbia-HKI-repo.git
git clone https://github.com/hakyimlab/MetaXcan.git
conda config --append channels conda-forge
conda config --append channels bioconda
conda env create -f /cloud/project/MetaXcan/software/conda_env.yaml
conda activate imlabtools

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.

Reuse

--- title: Transcriptome QGT Lab 2022 Setup author: '' date: '2022-06-14' slug: transcriptome-qgt-lab-2022-setup categories: [] tags: - lab - QGT - how to --- The instructions in this blog were written to set up the lab in Rstudio cloud ## 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 prediction models from predictdb.org - [ ] install R/RStudio/tidyverse package - [ ] install R packages - [ ] 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") #cTWAS install install.packages("R.utils") install.packages("remotes") remotes::install_github("simingz/ctwas", ref = "develop") library(ctwas) ## make sure these are installed # library(data.table) # library(BEDMatrix) # library(Rfast) # library(susieR) # library(coloc) #installing base miniconda install.packages("keras") reticulate::install_miniconda("miniconda") ``` # Setting up Conda Environment ```{bash, eval=FALSE} #adding path export PATH=$PATH:/cloud/project/miniconda/bin # adding repos/data git clone https://github.com/hakyimlab/QGT-Columbia-HKI-repo.git git clone https://github.com/hakyimlab/MetaXcan.git conda config --append channels conda-forge conda config --append channels bioconda conda env create -f /cloud/project/MetaXcan/software/conda_env.yaml conda activate imlabtools ``` 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.