scaden
Deep Learning based cell composition analysis with Scaden.
Science Score: 10.0%
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1 of 3 committers (33.3%) from academic institutions -
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Low similarity (18.8%) to scientific vocabulary
Keywords
Repository
Deep Learning based cell composition analysis with Scaden.
Basic Info
- Host: GitHub
- Owner: KevinMenden
- License: mit
- Language: Python
- Default Branch: master
- Homepage: https://scaden.readthedocs.io
- Size: 1020 KB
Statistics
- Stars: 82
- Watchers: 2
- Forks: 29
- Open Issues: 19
- Releases: 0
Topics
Metadata Files
README.md
Single-cell assisted deconvolutional network
Scaden is a deep-learning based algorithm for cell type deconvolution of bulk RNA-seq samples. It was developed at the DZNE Tübingen and the ZMNH in Hamburg. The method is published in Science Advances: Deep-learning based cell composition analysis from tissue expression profiles
A complete documentation is available here
Scaden overview. a) Generation of artificial bulk samples with known cell type composition from scRNA-seq data. b) Training of Scaden model ensemble on simulated training data. c) Scaden ensemble architecture. d) A trained Scaden model can be used to deconvolve complex bulk mixtures.
Installation guide
Scaden can be easily installed on a Linux system, and should also work on Mac. There are currently two options for installing Scaden, either using Bioconda or via pip.
pip
To install Scaden via pip, simply run the following command:
pip install scaden
GPU
If you want to make use of your GPU, you will have to additionally install tensorflow-gpu.
For pip:
pip install tensorflow-gpu
For conda:
conda install tensorflow-gpu
Docker
If you don't want to install Scaden at all, but rather use a Docker container, we provide that as well. For every release, we provide two version - one for CPU and one for GPU usage. To pull the CPU container, use this command:
docker pull ghcr.io/kevinmenden/scaden/scaden
For the GPU container:
docker pull ghcr.io/kevinmenden/scaden/scaden-gpu
Webtool (beta)
Additionally, we now proivde a web tool:
It contains pre-generated training datasets for several tissues, and all you need to do is to upload your expression data. Please note that this is still in preview.
Usage
We provide a detailed instructions for how to use Scaden at our Documentation page
A deconvolution workflow with Scaden consists of four major steps:
- data simulation
- data processing
- training
- prediction
If training data is already available, you can start at the data processing step. Otherwise you will first have to process scRNA-seq datasets and perform data simulation to generate a training dataset. As an example workflow, you can use Scaden's function scaden example to generate example data and go through the whole pipeline.
First, make an example data directory and generate the example data:
bash
mkdir example_data
scaden example --out example_data/
This generates the files "examplecounts.txt", "examplecelltypes.txt" and "examplebulkdata.txt" in the "example_data" directory. Next, you can generate training data:
bash
scaden simulate --data example_data/ -n 100 --pattern "*_counts.txt
This generates 100 samples of training data in your current working directory. The file you need for your next step is called "data.h5ad". Now you need to perform the preprocessing using the training data and the bulk data file:
bash
scaden process data.h5ad example_data/example_bulk_data.txt
As a result, you should now have a file called "processed.h5ad" in your directory. Now you can perform training. The following command performs training for 5000 steps per model and saves the trained weights to the "model" directory, which will be created:
bash
scaden train processed.h5ad --steps 5000 --model_dir model
Finally, you can use the trained model to perform prediction:
bash
scaden predict --model_dir model example_data/example_bulk_data.txt
Now you should have a file called "scaden_predictions.txt" in your working directory, which contains your estimated cell compositions.
1. System requirements
Scaden was developed and tested on Linux (Ubuntu 16.04 and 18.04). It was not tested on Windows or Mac, but should also be usable on these systems when installing with Pip or Bioconda. Scaden does not require any special hardware (e.g. GPU), however we recommend to have at least 16 GB of memory.
Scaden requires Python 3. All package dependencies should be handled automatically when installing with pip or conda.
Owner
- Name: Kevin Menden
- Login: KevinMenden
- Kind: user
- Location: Tübingen, Germany
- Company: AIRAmed
- Repositories: 6
- Profile: https://github.com/KevinMenden
Research scientist interested in machine learning, data science and bioinformatics
GitHub Events
Total
- Watch event: 11
- Issue comment event: 1
- Pull request event: 1
- Fork event: 2
- Create event: 1
Last Year
- Watch event: 11
- Issue comment event: 1
- Pull request event: 1
- Fork event: 2
- Create event: 1
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| kevinmenden | k****n@t****e | 138 |
| Sergio Oller | s****r@g****m | 13 |
| eboileau | b****u@u****e | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 85
- Total pull requests: 22
- Average time to close issues: 3 months
- Average time to close pull requests: 1 day
- Total issue authors: 43
- Total pull request authors: 5
- Average comments per issue: 2.98
- Average comments per pull request: 0.23
- Merged pull requests: 19
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 2
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 2
- Pull request authors: 1
- Average comments per issue: 1.0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- KevinMenden (15)
- khkk378 (7)
- Zhaohui-Ruan (4)
- mHagiw (3)
- nagendraKU (3)
- idalarsson (3)
- hathawayxxh (3)
- vdet (2)
- bio-visualisation (2)
- sydneysue (2)
- Kai6662 (2)
- ThomasThaewel (2)
- HelloYiHan (2)
- yunners (2)
- zeehio (2)
Pull Request Authors
- KevinMenden (16)
- zeehio (4)
- alex-d13 (2)
- eboileau (1)
- dependabot[bot] (1)
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Packages
- Total packages: 1
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Total downloads:
- pypi 58 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 14
- Total maintainers: 1
pypi.org: scaden
Cell type deconvolution using single cell data
- Homepage: https://github.com/KevinMenden/scaden
- Documentation: https://scaden.readthedocs.io/
- License: MIT License
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Latest release: 1.1.2
published over 4 years ago
Rankings
Maintainers (1)
Dependencies
- anndata *
- click *
- h5py *
- numpy *
- pandas *
- rich *
- scikit-learn *
- tensorflow >=2.0