Introduction to SpaCell
SpaCell has been developed for analysing spatial transcriptomics (ST) data, which include imaging data of tissue sections and RNA expression data across the tissue sections. The ST data add a novel spatial dimension to the traditional gene expression data, which derive from dissociated cells. The ST data also add molecular information to a typical histological image. Spacell is desinged to integrates the two histopathological imaging and sequencing fields, with the ultimate aim to discover novel biology and to improve histopathological diagnosis.
SpaCell implements (deep) neural network (NN) models likea multi-input and multi-output autoencoder, transfer learning with or without fine tuning and residual and separable convolutional NN architectures to identify cell types or to predict disease stages. The NN integrates millions of pixel intensity values with thousands of gene expression measurements from spatially-barcoded spots in a tissue. Prior to model training, SpaCell enables users for implement a comprehensive data preprocessing workflow to filter, combine, and normalise images and gene expression matrices.
[python 3.6+] [TensorFlow 1.4.0] [scikit-learn 0.18] [keras 2.2.4] [seaborn 0.9.0] [opencv 4.1.1] [pandas 0.25.0] [pillow 6.1.0] [python-spams 2.6.1] [staintools 2.1.2] ...
2.1 Build from sources
To meet the requirements, we recommend user to use either (1) conda environment:
# Download SapCell from GitHub and install all required packages: git clone https://github.com/BiomedicalMachineLearning/Spacell.git cd Spacell conda env create -f requirements.yml
# To activate environment: conda activate SpaCell
# To exit environment: conda deactivate
Or (2) Docker container:
# Downlaod Docker image docker pull biomedicalmachinelearning/spacell:latest
# Run Docker container docker run \ -it \ -v /path/to/your/data:/home/Spacell/dataset/ \ # mount your local data directory to container biomedicalmachinelearning/spacell:latest
2.2 Install from PyPi
pip install SpaCell
|Ubuntu Linux 18.04||Conda, Docker, PyPI|
|Centos Linux 7||Conda, Docker, PyPI|
|MacOS(Mojave)||Conda, Docker, PyPI|
|MacOS(Catalina)||Conda, Docker, PyPI|
|Windows 10||Docker, PyPI|
python-spams 2.6.1, a dependence of
staintools 2.1.2, is currently not avaliable on Windows 10 platform. We recommend Windows 10 user to use Docker container instead of Conda environment.
- Specify the dataset directory and output directory.
- Specify model parameters.
1. Image Preprocessing
2. Count Matrix PreProcessing
3. Generate paired image and gene count training dataset
python spacell_clustering.py -i /path/to/one/image.jpg -l /path/to/iamge/tiles/ -c /path/to/count/matrix/ -e 100 -k 2 -o /path/to/output/
-eis number of training epochs
-kis number of expected clusters
6. Clustering Validation and Quantification
python spacell_validation.py -d /path/to/data -a annotation.png -w wsi.jpeg -m affine_tranformation_matrix.txt -o output_folder -k clustering_predictions.tsv -c annotation_colour_range
-cis annotation colour range thresholds - blue_low green_low red_low blue_upper green_upper red_low
-tindicates that annotations are not closed paths, so spacell with try to close the paths
-fdownscale factor if the input whole slide image has already been downscaled
-sspot size, optional, usually set automatically
Classification of ALS disease stages
Clustering for finding prostate cancer region
Clustering for finding inflamed stromal
Clustering for anatomical regions in mouse olfactory bulb (High density ST dataset)
If you find Spacell useful in your research, please consider citing:
Xiao Tan, Andrew T Su, Minh Tran, Quan Nguyen (2019). SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. (Manuscript is currently under-review)
The software is under active development by the Biomedical Machine Learning Lab at the Institute for Molecular Bioscience (IMB, University of Queensland).
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