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https://github.com/vanvalenlab/deepcell-spots
Deep Learning Library for Spot Detection
https://github.com/vanvalenlab/deepcell-spots
biological-data-analysis deep-learning spot-detection tensorflow
Last synced: about 2 months ago
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Deep Learning Library for Spot Detection
- Host: GitHub
- URL: https://github.com/vanvalenlab/deepcell-spots
- Owner: vanvalenlab
- License: other
- Created: 2020-05-19T04:32:12.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-03-15T23:22:39.000Z (10 months ago)
- Last Synced: 2024-09-19T10:47:31.927Z (4 months ago)
- Topics: biological-data-analysis, deep-learning, spot-detection, tensorflow
- Language: Python
- Homepage: https://deepcell-spots.readthedocs.io/
- Size: 211 MB
- Stars: 14
- Watchers: 3
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DeepCell Spots
[](https://github.com/vanvalenlab/deepcell-spots/actions)
[](https://deepcell-spots.readthedocs.io/en/latest/?badge=latest)
[](https://coveralls.io/github/vanvalenlab/deepcell-spots)
[](https://github.com/vanvalenlab/deepcell-spots/blob/master/LICENSE)
[](https://badge.fury.io/py/DeepCell-Spots)
[](https://pypistats.org/packages/deepcell-spots)
[](https://pypi.org/project/deepcell-spots/)
`deepcell-spots` is a deep learning package for analyzing spatial transcriptomics data sets. Its application, Polaris, allows you to apply pre-existing models and train new deep learning models for fluorescent spot detection. Polaris also contains a probabilistic method for combinatorial barcode assignment. It is written in Python and leverages a number of packages, including: [TensorFlow](https://github.com/tensorflow/tensorflow), [Keras](https://www.tensorflow.org/guide/keras), [PyTorch](https://pytorch.org), and [DeepCell](https://github.com/vanvalenlab/deepcell-tf). More detailed documentation of `deepcell-spots` is available [here](https://deepcell-spots.readthedocs.io/).
# 
# Getting Started
## Install with pip
The simplest way to install `deepcell-spots` is with `pip`:
```bash
pip install deepcell-spots
```
## Install with Docker
The `deepcell-spots` Docker container is also available on DockerHub. For more details for running DeepCell Docker containers, please see the [deepcell-tf README](https://github.com/vanvalenlab/deepcell-tf/blob/master/README.md).
# DeepCell Spots Applications
`deepcell-spots` contains an applications that greatly simplify the implementation of machine learning models for spot detection and decoding. `SpotDetection` contains a pre-trained model for fluorescent spot detection on images derived from assays such as RNA FISH and in-situ sequencing. This model returns a list of coordinate locations for fluorescent spots detected in the input image. `SpotDecoding` fit a probabilistic model for spot decoding with SVI. It returns a gene identity for each detected spot for multiplex FISH data sets. `Polaris` pairs these two applications with [DeepCell](https://github.com/vanvalenlab/deepcell-tf) models for nuclear and cytoplasmic segmentation. For example implementation, please see the [example notebooks](https://github.com/vanvalenlab/deepcell-spots/blob/master/notebooks).
# DeepCell Spots for Developers
Build and run a local docker container, similarly to the instructions for deepcell-tf. The relevant parts are copied here with modifications to work for deepcell-spots. For further instructions, see the [deepcell-tf README](https://github.com/vanvalenlab/deepcell-tf/blob/master/README.md).
### Build a local docker container, specifying the deepcell version with DEEPCELL_VERSION
The deepcell-spots Docker image is built using the deepcell-tf Docker images as a base image, so a ``DEEPCELL_VERSION`` must be specified in the build command.
```bash
git clone https://github.com/vanvalenlab/deepcell-spots.git
cd deepcell-spots
docker build --build-arg DEEPCELL_VERSION=0.12.9-gpu -t $USER/deepcell-spots .
```
### Run the new docker image
```bash
# '"device=0"' refers to the specific GPU(s) to run DeepCell-Spots on, and is not required
docker run --gpus '"device=0"' -it \
-p 8888:8888 \
$USER/deepcell-spots
```
It can also be helpful to mount the local copy of the repository and the notebooks to speed up local development.
```bash
# you can now start the docker image with the code mounted for easy editing
docker run --gpus '"device=0"' -it \
-p 8888:8888 \
-v $PWD/deepcell-spots/deepcell_spots:/usr/local/lib/python3.8/dist-packages/deepcell_spots \
-v $PWD/notebooks:/notebooks \
-v /$PWD:/data \
$USER/deepcell-spots
```
## Copyright
Copyright © 2019-2024 [The Van Valen Lab](http://www.vanvalen.caltech.edu/) at the California Institute of Technology (Caltech), with support from the Shurl and Kay Curci Foundation, Google Research Cloud, the Paul Allen Family Foundation, & National Institutes of Health (NIH) under Grant U24CA224309-01.
All rights reserved.
## License
This software is licensed under a modified [APACHE2](https://github.com/vanvalenlab/deepcell-spots/blob/master/LICENSE). See [LICENSE](https://github.com/vanvalenlab/deepcell-spots/blob/master/LICENSE) for full details.
## Trademarks
All other trademarks referenced herein are the property of their respective owners.
## Credits
[](http://www.vanvalen.caltech.edu/)