https://github.com/berenslab/elephant-in-the-room
Companion repository to our Lause, Berens & Kobak (2024) paper "The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense" (PLOS Computational Biology)
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.5%) to scientific vocabulary
Repository
Companion repository to our Lause, Berens & Kobak (2024) paper "The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense" (PLOS Computational Biology)
Basic Info
- Host: GitHub
- Owner: berenslab
- License: gpl-3.0
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: http://dx.doi.org/10.1371/journal.pcbi.1012403
- Size: 17.4 MB
Statistics
- Stars: 6
- Watchers: 4
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
The art of seeing the elephant in the room:
2D embeddings of single-cell data do make sense
This repository holds the code to reproduce the analysis in our Lause, Berens & Kobak (2024) PLOS CB paper and contains the main and supplementary figures.
To reproduce our analysis, follow the steps below. We assume you have git and conda installed.
Install
Clone this repository.
git clone git@github.com:berenslab/elephant-in-the-room.gitInside the main folder of the repo, go to the
srcfolder.In
src, clone our Picasso fork.Go back to main folder.
cd src git clone git@github.com:berenslab/picasso.git cd ..Install the conda environment for Picasso.
conda env create -f src/picasso/env/env3.7_LINUX.ymlFrom the main folder of the
elephant-in-the-roomrepo, activate thepicasso_envenvironment and install our Picasso fork withpip.conda activate picasso_env pip install -e .Install our conda analysis environment.
conda env create -f environment.yml
Run the analysis
- Activate our conda analysis environment.
Start jupyter lab to run notebooks 1&2 in the
scriptsfolder. This will download the data, run preprocesing and compute PCA, t-SNE and UMAP embeddings.conda activate elephant_analysis_env jupyter labAfter that, activate the Picasso anvironment and start jupyter notebook to run notebook 3. This will run Picasso and create the elephant embeddings.
conda activate picasso_env jupyter notebookAfter that, again activate our analysis environment.
Start jupyter lab to run the remaining notebooks 4&5. This will run the evaluations and prepare the plots.
conda activate elephant_analysis_env jupyter lab
System information
We used conda 23.11.0 on a recent laptop with 16GB RAM running LINUX 6.5.0-18-generic #18~22.04.1-Ubuntu. See environment.yml for more information on the analysis environment, and env3.7_LINUX.yml for more information on the Picasso environment.
Copyright information
Notebook 1 and notebook 3 use code adapted from the github repository CP_2023 by Chari & Pachter, which is subject to the following licence:
``` BSD 2-Clause License
Copyright (c) 2021, Pachter Lab All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ```
Owner
- Name: Berens Lab @ University of Tübingen
- Login: berenslab
- Kind: organization
- Email: philipp.berens@uni-tuebingen.de
- Location: Tübingen, Germany
- Website: https://hertie.ai/data-science
- Repositories: 60
- Profile: https://github.com/berenslab
Department of Data Science at the Hertie Institute for AI in Brain Health, University of Tübingen
GitHub Events
Total
- Watch event: 1
- Push event: 1
Last Year
- Watch event: 1
- Push event: 1