MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance (MUSTANG) is a computaional framework, which is capable of performing multi-sample spatial transcriptomics spot cellualar deconvolution by allowing both cross-sample expression based similarity information sharing as well as spatial correlation in gene expression patterns within samples.
If you use this code, please cite our Patterns journal paper:
Niyakan, S., Sheng, J., Cao, Y., Zhang, X., Xu, Z., Wu, L., Wong, S. T. C., & Qian, X. (2024). MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance. Patterns (New York, N.Y.), 5(5), 100986. https://doi.org/10.1016/j.patter.2024.100986
In order to analyze your multi-sample spatial transcriptional (ST) data with MUSTANG, 4 main steps should be performed:
- Spots Spatial Graph: The adjacency matrix of spots spatial graph should be extracted based on the layout.
- Spots Transcriptional Graph: The adjacency matrix of spots transcriptional graph in which spots that are transcriptionally similar to eachother are connected with an edge should be extracted.
- Spots Similarity Graph: The adjacency matrix of spots similarity graph should be constructed based on adjacency matrices of spots spatial and transcriptional graphs.
- Bayesian Deconvolution Analysis: The Poisson discrete deconvolution model should be applied to extract the deconvolution parameters.