In this project are the code and results associated with the manuscript entitled: “Tau filaments are tethered within brain extracellular vesicles in Alzheimer's disease”.
This file is part of AD-EV-characterisation.
Copyright (C) 2022-2024 Emir Turkes, Stephanie Fowler, UK DRI at
UCL, Columbia University Medical Center
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Emir Turkes can be contacted at [email protected]
Several supplemental data figures, results, and explanations of methodology can be found in additional files hosted on Dropbox.
They can be found in the supplemental-data folder in the following link:
https://www.dropbox.com/scl/fo/5ojghapoyaaua8q4oqmmi/ACat2UowmxcQjYl2Y8XMHAw?rlkey=06ez1w18q7isv45jy0pg9uygz&st=fa7tdhmp&dl=0
Reproduction of figures and results from the manuscript can be found in the results folder of the same Dropbox link.
Inside the folder are HTML files that can be opened in any web browser; they will have all information neccessary to view the figues and the code used to produce them.
Beyond here is information for reproducing or performing your own analysis.
As in the Quick Start, visit the Dropbox link, this time downloading both raw-data and results.
These should both be placed in a directory called storage and placed in the top-level of this project after downloading it from Github.
Note that the project structure currently assumes a Unix environment as there are the top-level symbolic links data and results pointing to those respective directories within storage.
These may have to be recreated on non-Unix systems such as Windows.
We provide two methods below for setting up an analysis environment and running the code. Alternatively, a user may wish to use their own R install and packages, however this approach may require troubleshooting and does not guarantee reproduction of our results.
Docker is virtualisation software that allows the distribution of reproducible operating system environments, including userland software like R packages.
See here to install and set up Docker https://www.docker.com/.
Once installed, the simplest option is to run run_all.R using the command below:
docker-compose up all
This will compile all R Markdown files non-interactively, replacing the existing files in the results dir and creating a tmp dir containing intermediate data objects.
For more in-depth exploration, RStudio Server is provided within the Docker image and can readily be accessed through a web browser either on one's local machine or through a remote server.
To get started, first create a .env file with the port that RStudio should connect to, as below:
PORT=8787
Any available port can be used and ports in the 8000 range are generally always open. More details can be found at https://github.com/rocker-org/rocker.
RStudio Server can then be started by running:
docker-compose up rstudio
You can then visit http://localhost:8787/ in a web browser to use RStudio.
If one wants to run RStudio Server on a remote machine, it can be accessed through a local web browser by first running an SSH tunnel like below:
ssh -N -L 8787:localhost:8787 user@ip-address
The left-most port specifies the port desired locally, whereas the right-most is the port on the server.
The right-most argument is the user@ip-address details used to log into the server.
Once connected, visit http://localhost:8787/ as before, assuming 8787 is the local port in the tunnel.
More details can be found here: https://divingintogeneticsandgenomics.rbind.io/post/run-rstudio-server-with-singularity-on-hpc/.
Apptainer (previously named Singularity) is similar to Docker except with a different security structure that makes it easier to integrate with high-performance computing (HPC) clusters.
Installation instructions can be found at https://apptainer.org/.
Similar to before, run_all.R is called with:
sh ./apptainer.sh all
To use RStudio, no .env file is needed, it is instead included in the command:
sh ./apptainer.sh rstudio 8787
In the same way as using Docker, RStudio Server will be accessible through a local web browser on localhost at the desired port. An SSH tunnel can also be identically configured for accessing remote servers.