alterlab-squidpy-spatial
SolidAnalyzes spatial transcriptomics with squidpy (1.8.x) on AnnData and SpatialData objects, routing platforms correctly: Visium spots use spatial_neighbors(coord_type='grid') and pair with deconvolution, while Xenium/MERFISH single-cell data use coord_type='generic'/Delaunay neighbors and spatialdata-io readers (xenium, visium_hd, merscope). Runs sq.gr.spatial_neighbors, nhood_enrichment, co_occurrence, spatial_autocorr (Moran's I for spatially variable genes), ripley, and ligrec. Use when the user wants spatial transcriptomics, squidpy, Visium/Xenium/MERFISH analysis, neighborhood enrichment, co-occurrence, or spatially variable genes; QC/clustering uses alterlab-scanpy and spot deconvolution (destVI/Tangram) uses alterlab-scvi-tools. Part of the AlterLab Academic Skills suite.
Install
Quality Score: 87/100
Skill Content
Details
- Author
- AlterLab-IEU
- Repository
- AlterLab-IEU/AlterLab-Academic-Skills
- Created
- 2 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
spatial-transcriptomics
Orchestrator skill for spatial transcriptomics analysis. Routes to one of three personality modes (aristotle/plato/socrates) based on user intent. Covers platforms: Visium, Xenium, MERFISH, Slide-seq, Stereo-seq, CosMx, SpatialDB, Seq-Scope, DBiT-seq, STARmap. Frameworks: Scanpy, Squidpy, Giotto, Seurat, SpatialDE, BayesSpace, SPARK-X, SpaGCN, Cell2location, RCTD, Tangram. Trigger keywords: spatial transcriptomics, Visium, Xenium, MERFISH, Slide-seq, spatial gene expression, spatial omics, single-cell spatial, spatial analysis, spot deconvolution, spatial clustering, spatially variable genes, 空间转录组, 空间组学, 空间基因表达, 空间分析, 空间聚类, 空间差异表达, 10x Visium, Stereo-seq, CosMx, seqFISH, FISSEQ.
alterlab-scanpy
Run the standard single-cell RNA-seq analysis pipeline with Scanpy on AnnData — QC filtering, normalization, dimensionality reduction (PCA, UMAP, t-SNE), Leiden/Louvain clustering, marker/differential expression, and plotting. Use for exploratory scRNA-seq analysis with established workflows — for deep generative models use scvi-tools, for data-format and .h5ad questions use anndata. Part of the AlterLab Academic Skills suite.
alterlab-scvi-tools
Train deep generative models for single-cell omics with scvi-tools — probabilistic batch correction and integration (scVI), reference-mapping transfer learning (scArches), differential expression with uncertainty, and multimodal models (totalVI for CITE-seq, MultiVI for multiome). Use when correcting batch effects, integrating multimodal data, or doing advanced probabilistic single-cell modeling — for standard analysis pipelines use scanpy. Part of the AlterLab Academic Skills suite.