csangara / DSTG

Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence

Deconvoluting Spatial Transcriptomics data through Graph-based convolutional networks (DSTG)

This is a TensorFlow implementation of DSTG for decomposing spatial transcriptomics data, which is described in our paper:

Installation

python setup.py install

Requirements

• tensorflow (>0.12)
• networkx

Run the demo

load the example data using the convert_data.R script In the example data, we provide two synthetic spatial transcriptomics data generated from scRNA-seq data (GSE72056). Each synthetic data consists of 1,000 spots, which can be found in folder synthetic_data.

cd DSTG
Rscript convert_data.R # load example data 
python train.py # run DSTG

Predicted compositions within each spot are saved in will be shown in the DSTG_Result folder.

Performance of JSD score will be shown if you run

Rscript evaluation.R

If you want to use your own scRNA-seq data to deconvolute your spatail transcriptomcis data, provide you data to script below:

Run your own data

When using your own scRNA-seq data to deconvolute your spatail transcriptomcis data, you have to provide

• the raw scRNA-seq data matrix and label, which are saved as .RDS format (e.g. 'scRNAseq_data.RDS' & 'scRNAseq_label.RDS')
• the raw spatial transcriptomics data matrix saved as .RDS format (e.g. 'spatial_data.RDS')

cd DSTG
Rscript  convert_data.R  scRNAseq_data.RDS  spatial_data.RDS  scRNAseq_label.RDS
python train.py # run DSTG

Then you will get your results in the DSTG_Result folder.

Cite

Please cite our paper if you use this code in your own work:

Qianqian Song, Jing Su, DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence, Briefings in Bioinformatics, 2021;, bbaa414, https://doi.org/10.1093/bib/bbaa414