spatial-transcriptomics-deconvolution-cell2location
De la autor
Visium and other spot-based spatial transcriptomics (Slide-seq, ST) cell type deconvolution with cell2location: load Space Ranger output, get a tissue-matched scRNA-seq reference from CELLxGENE, learn cell type signatures, estimate cell type abundance per spot with posterior uncertainty, and check the maps against marker genes. Default route for 'annotate cell types' on Visium when a GPU is available. Limits: needs a tissue- and state-matched reference; the default 30000 epochs take hours on CPU, so without a GPU it runs only with fewer epochs (exploratory maps) and can exceed one execution call's time limit. For fast per-spot proportions on CPU use spatial-mapping-tangram (clusters mode); for single-cell-resolution data (Xenium, MERFISH, CosMx) use spatial-transcriptomics-cell-type-annotation.
Instalare
npx skills add jaechang-hits/SciAgent-Skills --skill spatial-transcriptomics-deconvolution-cell2location -a claude-code
Se instalează în .claude/skills/spatial-transcriptomics-deconvolution-cell2location/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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De ce 86
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- spatial-mapping-tangramjaechang-hits/SciAgent-SkillsTangram (tangram-sc) mapping of scRNA-seq onto spatial transcriptomics (Visium, Slide-seq, MERFISH, Xenium), one skill for every Tangram task: marker training genes, cells/clusters/constrained mode, per-spot cell type proportions, held-out gene validation (training scores, compare_spatial_geneexp, AUC, k-fold CV), imputation of unmeasured or dropout genes (project_genes; gimVI alternative), and Visium deconvolution into segmented nuclei with the tissue image (squidpy watershed, target_count, deconvolve_cell_annotations). Task-specific routes follow the main workflow. For probabilistic spot abundances with uncertainty use spatial-transcriptomics-deconvolution-cell2location.Documente★ 382
- spatial-transcriptomics-cell-type-annotationjaechang-hits/SciAgent-SkillsSpatial transcriptomics cell type annotation for single-cell-resolution platforms (Xenium, MERFISH, CosMx, SeqFISH): CELLxGENE reference download, Harmony label transfer, per-cluster marker evidence naming lineages then subtypes, spatial niches. Visium/Slide-seq spots: GPU -> spatial-transcriptomics-deconvolution-cell2location; CPU -> spatial-mapping-tangram (cell types: per-spot proportions); no reference -> spatial-domain-detection (regions: domains from markers). Beyond labels (microenvironment, arrangement, spatial patterns): read spatial-transcriptomics-analysis-squidpy before asking the user.Documente★ 382
- spatial-transcriptomics-cell-cell-communicationjaechang-hits/SciAgent-SkillsSpatial cell-cell communication / ligand-receptor (LR) interaction analysis, one skill for every route: LIANA consensus ranking between annotated cell types, CellPhoneDB v5 permutation p-values, DEG method and spatial microenvironments, squidpy neighbourhood enrichment to keep spatially adjacent sender-receiver pairs, LIANA bivariate local LR scores on the tissue (works on Visium spots without labels), pathway-focused candidate evidence tables (immune checkpoints, chemokines, Notch, Wnt, TGF-beta) with contact vs secreted classes and PubMed co-mentions, and multi-sample Tensor-cell2cell and MISTy models. Xenium/MERFISH/CosMx cells, Visium spots, scRNA-seq. Route-specific steps follow the main workflow. Needs cell type labels first (spatial-transcriptomics-cell-type-annotation or a deconvolution skill).Planificare★ 382
- spatial-domain-detectionjaechang-hits/SciAgent-SkillsSpatial domain detection (tissue regions / niches) for spatial transcriptomics: SpaGCN (expression + spatial graph + H&E histology, Visium/ST), GraphST (self-supervised graph contrastive embedding, spots or cells) and UTAG (spatial message passing for Xenium/MERFISH/CosMx/IMC cells). Outputs a domain label per spot or cell, boundary refinement, cross-method agreement (ARI) and domain marker genes. For niches defined by the cell type composition of annotated cells use Step 7 of spatial-transcriptomics-cell-type-annotation; for neighbourhood enrichment and spatial statistics use spatial-transcriptomics-analysis-squidpy.Altele★ 382
- spatial-panel-designjaechang-hits/SciAgent-SkillsSpatial transcriptomics gene panel design (Xenium, MERFISH, CosMx custom panels) for cell type identification: rank markers on a tissue-matched CELLxGENE scRNA-seq reference, add PanglaoDB and CellMarker 2.0 marker evidence, build a per-gene evidence table, allocate per-cell-type quotas, and check that the panel separates cell types in the reference. Output: final_gene_panel.csv with N genes, cell type and reason. For annotating data from an existing panel use spatial-transcriptomics-cell-type-annotation.Design★ 382
- spatial-transcriptomics-analysis-squidpyjaechang-hits/SciAgent-SkillsSquidpy spatial statistics on annotated spatial transcriptomics: build the spatial graph (Visium grid, kNN, radius, Delaunay), neighbourhood enrichment z-scores, distance-resolved co-occurrence, interaction matrix and centrality scores, Ripley's L/F/G point patterns, spatially variable genes by Moran's I / Geary's C, and permutation ligand-receptor tests (ligrec, OmniPath). Works on Visium spots and single-cell-resolution data (Xenium, MERFISH, CosMx, Slide-seq). Needs cluster or cell type labels for all but the spatially variable gene step. For an end-to-end ligand-receptor workflow use spatial-transcriptomics-cell-cell-communication.Testare★ 382
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