Spacell Package
Project description
Introduction to SpaCell
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SpaCell program has been developed for spatial transcriptomics dataset, which include image data and RNA expression data.
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SpaCell implements (deep) neural network (NN) models like autoencoder, convolutional neural network (residual net), and pre-trained model for transfer-learning to train models for identifying cell types or predicting disease stages. The NN integrates millions of pixel intensity values with thousands of gene expression measurements from spatially-barcoded spots in a tissue.
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SpaCell has a comprehensive data preprocessing workflow to filter, combine, and normalise images and gene expression matrices
Installation
- Requirements:
[python 3.6+]
[TensorFlow 1.4.0]
[scikit-learn 0.18]
[keras 2.2.4]
[staintools ]
- Installation:
2.1 Download from GitHub
git clone https://github.com/BiomedicalMachineLearning/Spacell.git
2.2 Install from PyPi
pip install SpaCell
Usage
Configurations
config.py
- Specify the dataset directory and output directory.
- Specify model parameters.
1. Image Preprocessing
python image_normalization.py
2. Count Matrix PreProcessing
python count_matrix_normalization.py
3. Generate paired image and gene count training dataset
python dataset_management.py
4. Classification
python spacell_classification,py
5. Clustering
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/
-e
is number of training epochs-k
is number of expected clusters
Results
Classification of ALS disease stages
Clustering for finding prostate cancer region
Clustering for finding inflamed stromal
Dataset
For evaluating the algorithm, ALS (Amyotrophic lateral sclerosis) dataset and prostate cancer dataset can be used.
Citing Spacell
If you find Spacell useful in your research, please consider citing:
Xiao Tan, Andrew T Su, Quan Nguyen (2019). SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. (Manuscript is currently under-review)
The team
The software is under active development by the Biomedical Machine Learning group at Institute for Molecular Biology (IMB, University of Queensland).
Please contact Dr Quan Nguyen (quan.nguyen@uq.edu.au), Andrew Su (a.su@uq.edu.au), and Xiao Tan (xiao.tan@uq.edu.au) for issues, suggestion, and collaboration.
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