TBEL Data Science Resource List

Provided by Liang Li and Lulu Shang.

If you need further assistance, contact TBEL-CDMC@mdanderson.org.

Task	Approach	Tools	Applications
	Quality control	SpotClean	Quality control of spatial transcriptomics data, removing ambient RNA contamination.
	Batch effect removal	Harmony, ComBat, BBKNN	Correcting batch effects across datasets.
	DE analysis	Wilcoxon test in Seurat, RegionalST	Identifying differentially expressed genes across spatial regions or cell types.
	Gene set enrichment analysis	GSEA, gProfiler, fgsea, clusterProfiler, EnrichR	Functional enrichment analysis for spatially resolved genes to identify biological pathways.
	Gene regulatory network	SCENIC, GRNBoost2, GENIE3	Inferring regulatory networks to identify regulatory relationships.
	Deconvolution	Cell2location, RCTD, CARD, SpatialDWLS, BayesPrism	Deconvolution of SRT data using paired scRNA-seq data.
	CNV inference	SpatialInferCNV, SPATA	Inference of CNVs from SRT data
	Cell-cell interaction	COMMOT, NCEM, NicheDE, NicheNet	Inference of cellular interactions from SRT data
	Cell type annotation	SingleR, scType, Seurat	Annotating cell types based on reference datasets or marker gene expression.
	Neighborhood analysis	Squidpy, Giotto, SpaOTsc	Understanding spatial relationships and clustering between cell types and tissue regions.
	Dimension reduction	SpatialPCA, PRECAST, PCA, NMF	Spatially aware dimension reduction and embedding for downstream analysis
	Spatial domain detection	BayesSpace, Louvain (Seurat), SpaGCN, BASS	Identifying spatial domains and tissue structures using spatial relationships and gene expression.
	Spatially variable genes detection	SPARK, SPARK-X, nnSVG, CELINA	Detecting genes with significant spatial expression patterns, including cell type-specific patterns.
	Multi-omics integration	SpatialGLUE, MOFA+, StabMap	Integrating spatial multi-omics data for comprehensive biological insights.
	Trajectory analysis	Monocle3, Slingshot, STREAM	Analyzing developmental trajectories or pseudotime.
	Data visualization	SpatialView, Seurat, Scanpy, Squidpy, Circos	Visualization of spatial transcriptomics data in 2D/3D contexts.
	Imputation and Alignment	CytoSPACE, Tangram, CellTrek, PASTA	Mapping single-cell RNA-seq data onto spatial data for alignment and high-resolution insights.
	Power analysis and sample size	PoweREST	Calculating statistical power and determining sample size for spatial transcriptomics studies.
	Cell segmentation	Baysor, SCS, DeepCell, Cellpose, StarDist	Identifying and segmenting individual cells or nuclei from spatial images.
	Morphology integration	SpaGCN, iStar, METI	Integrating morphological information from H&E images with spatial transcriptomics.