Spacell 0.9.9

Spacell Package

Introduction to SpaCell

• SpaCell program has been developed for spatial transcriptomics dataset, which include image data and RNA expression data.

• 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.

• SpaCell has a comprehensive data preprocessing workflow to filter, combine, and normalise images and gene expression matrices

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

2.2 Install from PyPi

pip install SpaCell

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