Spacell 0.0.1

SpaCell Package

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

Clustering for finding prostate cancer region

Clustering for finding inflamed stromal

Dataset

For evaluating the algorithm, ALS 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.

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 ([e-mail]), Andrew Su ([e-mail]), and Xiao Tan ([e-mail]) for issues, suggestion, and collaboration.