https://github.com/menchelab/bioprofilingnotebooks
https://github.com/menchelab/bioprofilingnotebooks
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/menchelab/bioprofilingnotebooks
- Owner: menchelab
- License: mit
- Created: 2021-02-02T17:46:15.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2022-10-14T13:15:42.000Z (over 3 years ago)
- Last Synced: 2025-03-30T17:46:24.604Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 47.9 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# BioProfiling.jl analysis notebooks
[](https://doi.org/10.5281/zenodo.5659932)
Demonstration of [_BioProfiling.jl_](https://github.com/menchelab/BioProfiling.jl) and robust statistics for morphological cell profiling using high-content imaging. Analyses described in the article ["BioProfiling.jl: Profiling biological perturbations with high-content imaging in single cells and heterogeneous populations"](https://doi.org/10.1093/bioinformatics/btab853) by Loan Vulliard, Joel Hancock, Anton Kamnev, Christopher W. Fell, Joana Ferreira da Silva, Joanna I. Loizou, Vanja Nagy, Loïc Dupré and Jörg Menche.
## Summary
This collection of notebooks includes the analysis of a high-content imaging chemical screen, the comparison of the morphological activity of compounds on cells seeded at two densities, as well as the comparison of genetic overexpression in two plates in a genetic screen following the Cell Painting assay. This demonstrates how to curate morphological profiles and perform common tasks for downstream analyses using _BioProfiling.jl_. All details can be found [in the corresponding publication](https://doi.org/10.1093/bioinformatics/btab853). For the content of each notebook, see the section "Reproducing example analyses" below, and the header of each notebook.
## Prerequisites
* Create a subfolder called `fig`, where the generated figures will be saved.
* Create a subfolder called `data`, where the input and intermediate files will be stored.
* Download the input data [from Figshare](https://doi.org/10.6084/m9.figshare.14784678.v2). Only the file `data.zip` is necessary to reproduce the core results of the analysis described in this notebook.
* Copy the transfer list and morphological measurement files in the `data` folder.
* (Optional) Mount or copy the raw images in a folder if you want to use the visual diagnostics features.
* (Optional) If you want to run the minimal example in the *FigS3_LUAD*, you will need additional external data. The instructions are provided in the notebook itself.
* (Optional) If you only want to reproduce parts of the analysis, you can copy the intermediate files [from Figshare](https://doi.org/10.6084/m9.figshare.14784678.v2) into the `data` folder.
## Running the code using Docker
### General instructions
This repository compiles a collection of scripts and Jupyter notebooks. For reproducibility, it is designed to run in a Docker container based on the [jupyter/datascience-notebook image](https://hub.docker.com/r/jupyter/datascience-notebook). The following steps describe how to run the code in the same development environment as intended:
#### Set up your directory
* [Install and run Docker Desktop](https://www.docker.com/get-started) on your machine (the Community Edition is available for free).
* Clone this repository and set its root folder as your working directory.
* Make sure you followed the _Prerequisites_ section and that the input data is in the repository, in the `data` subfolder.
#### Option 1 - Pulling the image from DockerHub [fast]
* You can obtain a pre-built image to run this notebooks from the DockerHub image repository:
docker pull koalive/bioprofilingnotebooks:v4
#### Option 2 - Building the image from the Dockerfile [robust]
* Alternatively, run the following command the first time you want to run code from this repository - which might take some time to download all requirements:
docker build --rm -t bioprofilingnotebooks .
docker tag bioprofilingnotebooks koalive/bioprofilingnotebooks:v4
#### Start a docker container running a Jupyter server
* Run the following each time you want to start a notebook server to run code from this repository:
docker run -p 9999:8888 -v `pwd`:/home/jovyan koalive/bioprofilingnotebooks:v4
* (Optional) If you wish to test the visual diagnostic features, you need to specify the folder in which the images can be found:
docker run -p 9999:8888 -v `pwd`:/home/jovyan -v /Local/Path/To/Images/:/images/ koalive/bioprofilingnotebooks:v4
* Find the token needed to connect to the Jupyter notebook in the console output and go to the corresponding address in your browser:
http://127.0.0.1:9999/?token=\BioProfilingNotebooks:/home/jovyan koalive/bioprofilingnotebooks:v4
### Note for Linux users
You can follow the general instructions. You might need to run Docker with super-user privileges depending on your setup, *i.e.* using *sudo docker* in all calls to Docker.
If the following happens when you try to run the notebook server: `PermissionError: [Errno 13] Permission denied: '/home/jovyan/.local'`, try the following options:
`sudo docker run --user $(id -u):$(id -g) --group-add users -p 9999:8888 -v `pwd`:/home/jovyan koalive/bioprofilingnotebooks:v4`
### Note for MacOS users
You can follow the general instructions.
### Note for Singularity users
You can use `singularity pull docker://koalive/bioprofilingnotebooks:v4` to get the image from DockerHub, then convert it to a sandbox with `singularity build --sandbox sandbox/ bioprofilingnotebooks_v4.sif` and run the sandbox with the --no-home and --writable flags on a given port (*6789* in this example), while binding your current directory to `/home/jovyan/`:
```singularity exec -B /your/working/directory/:/home/jovyan --writable --no-home sandbox/ jupyter-notebook --port 6789```
We recommend commenting out the following lines in the notebooks, which requires to load temporary fonts used for plotting results, which will not be available in the singularity sandbox:
```
ttf_import(paths = "/tmp/.fonts/")
loadfonts()
```