- Statistical and machine learning methods for spatially resolved transcriptomics data analysis. first author Zexian was my colleague when I was at DFCI.
- Deconvolution vs Clustering Analysis for Multi-cellular Pixel-Resolution Spatially Resolved Transcriptomics Data A blog post by Jean Fan.
In situ polyadenylation enables spatial mapping of the total transcriptome
- DestVI identifies continuums of cell types in spatial transcriptomics data. DestVI is available as part of the open-source software package scvi-tools (https://scvi-tools.org).
- SpaGene: Scalable and model-free detection of spatial patterns and colocalization
- Palo: Spatially-aware color palette optimization for single-cell and spatial data
- squidpy paper - code: Squidpy: a scalable framework for spatial omics analysis
- ncem paper - code: Learning cell communication from spatial graphs of cells
- Spatially informed cell-type deconvolution for spatial transcriptomics Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. https://github.com/YingMa0107/CARD
- Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace
- SpatialCorr: Identifying Gene Sets with Spatially Varying Correlation Structure