Spacell: A Jupyter Notebook repository from nimisis

Introduction to SpaCell

SpaCell has been developed for analysing spatial transcriptomics (ST) data, which include imaging data of tissue sections and RNA expression data across the tissue sections. The ST data add a novel spatial dimension to the traditional gene expression data, which derive from dissociated cells. The ST data also add molecular information to a typical histological image. Spacell is desinged to integrates the two histopathological imaging and sequencing fields, with the ultimate aim to discover novel biology and to improve histopathological diagnosis.
SpaCell implements (deep) neural network (NN) models likea multi-input and multi-output autoencoder, transfer learning with or without fine tuning and residual and separable convolutional NN architectures to identify cell types or to predict disease stages. The NN integrates millions of pixel intensity values with thousands of gene expression measurements from spatially-barcoded spots in a tissue. Prior to model training, SpaCell enables users for implement 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]
[seaborn 0.9.0]
[opencv 4.1.1]
[pandas 0.25.0]
[pillow 6.1.0]
[python-spams 2.6.1]
[staintools 2.1.2]

Installation:

2.1 Download from GitHub

git clone https://github.com/BiomedicalMachineLearning/Spacell.git

2.2 Install from PyPi

pip install SpaCell

To meet the requirements, we recommend user to use conda environment:

conda create -y -name spacell python==3.7
conda install -y -name spacell -c conda-forge --file requirements.txt
conda activate 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

6. Clustering Validation and Quantification

python spacell_validation.py -d /path/to/data -a annotation.png -w wsi.jpeg -m affine_tranformation_matrix.txt -o output_folder -k clustering_predictions.tsv -c annotation_colour_range

-c is annotation colour range thresholds - blue_low green_low red_low blue_upper green_upper red_low
-t indicates that annotations are not closed paths, so spacell with try to close the paths
-f downscale factor if the input whole slide image has already been downscaled
-s spot size, optional, usually set automatically

Results

Classification of ALS disease stages

Clustering for finding prostate cancer region

Clustering for finding inflamed stromal

Clustering for anatomical regions in mouse olfactory bulb (High density ST dataset)

Dataset

For evaluating the algorithm, ALS (Amyotrophic lateral sclerosis) dataset, prostate cancer dataset, and a high density spatial transcriptomic HDST dataset were used.

Citing Spacell

If you find Spacell useful in your research, please consider citing:

Xiao Tan, Andrew T Su, Minh Tran, 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 Lab at the Institute for Molecular Bioscience (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, suggestions, and we very welcome collaboration opportunities.

nimisis/Spacell