spotiflow 0.2.0

Accurate and efficient spot detection for microscopy data

Spotiflow - accurate and efficient spot detection with stereographic flow

Spotiflow is a deep learning-based, threshold-agnostic, and subpixel-accurate spot detection method for fluorescence microscopy. It is primarily developed for spatial transcriptomics workflows that require transcript detection in large, multiplexed FISH-images, although it can also be used to detect spot-like structures in general fluorescence microscopy images. A more detailed description of the method can be found in our paper.

The documentation of the software can be found here.

Installation

Create and activate a fresh conda environment (we currently support Python 3.9 to 3.11):

conda create -n spotiflow python=3.9
conda activate spotiflow

For Linux/Windows with a CUDA device, install PyTorch using conda/mamba (one might need to change the cuda version accordingly):

conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia # Might need to change the cuda version accordingly

Note (for MacOS users): if using MacOS, there is a known bug causing the installation of PyTorch with conda to sometimes break OpenMP. You can avoid installing PyTorch and let spotiflow install it automatically via pip.

Note (for Windows users): if using Windows, please install the latest Build Tools for Visual Studio (make sure to select the C++ build tools during installation) before proceeding to install Spotiflow.

Finally, install spotiflow:

pip install spotiflow

Usage

Training

See the example training script or the example notebook for an example of training. For finetuning an already pretrained model, please refer to the finetuning example notebook.

Inference (CLI)

You can use the CLI to run inference on an image or folder containing several images. To do that, you can use the following command:

spotiflow-predict PATH

where PATH can be either an image or a folder. By default, the command will use the general pretrained model. You can specify a different model by using the --pretrained-model flag. Moreover, spots are saved to a subfolder spotiflow_results created inside the input folder (this can be changed with the --out-dir flag). For more information, please refer to the help message of the CLI ($ spotiflow-predict -h).

Inference (API)

The API allows detecting spots in a new image in a few lines of code! Please check the corresponding example notebook and the documentation for a more in-depth explanation.

from spotiflow.model import Spotiflow
from spotiflow.sample_data import test_image_hybiss_2d

# Load sample image
img = test_image_hybiss_2d()
# Or any other image
# img = tifffile.imread("myimage.tif")

# Load a pretrained model
model = Spotiflow.from_pretrained("general")
# Or load your own trained model from folder
# model = Spotiflow.from_folder("./mymodel")

# Predict
points, details = model.predict(img) # points contains the coordinates of the detected spots, the attributes 'heatmap' and 'flow' of `details` contains the predicted full resolution heatmap and the prediction of the stereographic flow respectively (access them by `details.heatmap` or `details.flow`).

Napari plugin

Our napari plugin allows detecting spots directly with an easy-to-use UI. See napari-spotiflow for more information.

For developers

We are open to contributions, and we indeed very much encourage them! Make sure that existing tests pass before submitting a PR, as well as adding new tests/updating the documentation accordingly for new features.

Testing

First, clone the repository:

git clone [e-mail]:weigertlab/spotiflow.git

Then install the testing extras:

cd spotiflow
pip install -e ".[testing]"

then run the tests:

pytest -v --color=yes --cov=spotiflow

Docs

Install the docs extras:

pip install -e ".[docs]"

and then cd into the docs folder of the cloned repository and build them:

cd spotiflow/docs
sphinx-build -M html source build

How to cite

If you use this code in your research, please cite the Spotiflow paper (currently preprint):

@article {dominguezmantes24,
	author = {Albert Dominguez Mantes and Antonio Herrera and Irina Khven and Anjalie Schlaeppi and Eftychia Kyriacou and Georgios Tsissios and Can Aztekin and Joachim Ligner and Gioele La Manno and Martin Weigert},
	title = {Spotiflow: accurate and efficient spot detection for imaging-based spatial transcriptomics with stereographic flow regression},
	elocation-id = {2024.02.01.578426},
	year = {2024},
	doi = {10.1101/2024.02.01.578426},
	publisher = {Cold Spring Harbor Laboratory},
 URL = {https://www.biorxiv.org/content/early/2024/02/05/2024.02.01.578426},
	eprint = {https://www.biorxiv.org/content/early/2024/02/05/2024.02.01.578426.full.pdf},
	journal = {bioRxiv}
}