{ "info": { "author": "Xiao Tan, Andrew Su, Quan Nguyen", "author_email": "xiao.tan@uq.edu.au, a.su@uq.net.au, quan.nguyen@imb.uq.edu.au", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "
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\n\n## Introduction to SpaCell\n\n* **SpaCell** program has been developed for spatial transcriptomics dataset, which include image data and RNA expression data. \n\n* **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.\n\n* **SpaCell** has a comprehensive data preprocessing workflow to filter, combine, and normalise images and gene expression matrices\n\n## Installation\n\n1. Requirements: \n\n```\n[python 3.6+]\n[TensorFlow 1.4.0]\n[scikit-learn 0.18]\n[keras 2.2.4]\n[staintools ]\n```\n2. Installation: \n\n2.1 Download from GitHub \n\n```git clone https://github.com/BiomedicalMachineLearning/Spacell.git```\n\n2.2 Install from PyPi \n\n```pip install SpaCell```\n\n## Usage\n\n### Configurations\n\n```config.py```\n\n1. Specify the dataset directory and output directory.\n2. Specify model parameters.\n\n### 1. Image Preprocessing\n\n```python image_normalization.py```\n\n### 2. Count Matrix PreProcessing\n\n```python count_matrix_normalization.py```\n\n### 3. Generate paired image and gene count training dataset\n\n```python dataset_management.py```\n\n### 4. Classification\n\n```python spacell_classification,py```\n\n### 5. Clustering\n\n```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/```\n\n* `-e` is number of training epochs\n* `-k` is number of expected clusters\n\n## Results\n\n### Classification of ALS disease stages\n
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\n\n ### Clustering for finding prostate cancer region\n\n
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\n\n ### Clustering for finding inflamed stromal \n\n
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\n\n\n## Dataset \nFor evaluating the algorithm, ALS (Amyotrophic lateral sclerosis) dataset and prostate cancer dataset can be used.\n\n## Citing Spacell \nIf you find Spacell useful in your research, please consider citing:\n\nXiao Tan, Andrew T Su, Quan Nguyen (2019). SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. (Manuscript is currently under-review)\n\n## The team\nThe software is under active development by the Biomedical Machine Learning group at Institute for Molecular Biology (IMB, University of Queensland). \n\nPlease 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.\n\n\n\n",
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