Metadata-Version: 2.1
Name: SpaCell
Version: 0.0.1
Summary: SpaCell Package
Home-page: https://github.com/BiomedicalMachineLearning/Spacell.git
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
License: UNKNOWN
Description: <p align="center">
        <img width="1000" height="600" src=https://github.com/BiomedicalMachineLearning/Spacell/blob/master/figure/logo.png>
        
        ## Introduction to SpaCell
        
        * **SpaCell** program is being developed for spatial transcriptomics dataset which include image data and RNA expression data.  
        
        * **SpaCell** implements (deep) neural network models like autoencoder, convolutional neural network to find cell types or predict disease stages.  
        
        ## Installation
        
        1. Requirements:  
        
        ```
        [python 3.6+]
        [TensorFlow 1.4.0]
        [scikit-learn 0.18]
        [keras 2.2.4]
        [staintools ]
        ```
        2. Installation:    
        
        2.1 Download from GitHub   
        
        ```git clone https://github.com/BiomedicalMachineLearning/Spacell.git```
        
        2.1 Install from PyPi  
        
        ```pip install ....```
        
        ## Usage
        
        ### Configurations
        
        ```config.py```
        
        1. Specify the dataset directory and output directory.
        2. Specify model parameters.
        
        ### 1. Image Preprocessing
        
        ```python image_normalization.py```
        
        ### 2. Count Matrix PreProcessing
        
        ```python count_matrix_normalization.py```
        
        ### 3. Generate 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
        <p align="center">
        <img width="400" height="350" src=https://github.com/BiomedicalMachineLearning/Spacell/blob/master/figure/age_roc_combine.png> 
        <img width="400" height="350" src=https://github.com/BiomedicalMachineLearning/Spacell/blob/master/figure/age_confusion_matrix_combine.png> 
         
         ### Clustering for finding prostate cancer region
        
        <p align="center">
        <img src=https://github.com/BiomedicalMachineLearning/Spacell/blob/master/figure/clustering_1.png> 
        
         ### Clustering for finding inflamed stromal 
         
        <p align="center">
        <img src=https://github.com/BiomedicalMachineLearning/Spacell/blob/master/figure/clustering_2.png> 
        
         
        ## Dataset 
        For evaluating the algorithm, <a href="https://als-st.nygenome.org">ALS</a> dataset can be used.
        
        ## Citing Spacell 
        If you find Spacell useful in your research, please consider citing:
        
        <a href=" ">Xiao Tan, Andrew T Su, Quan Nguyen (2019). SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells.</a>
        
        ## 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.
         
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
