celltypist-cell-annotation
De la autor
Automated scRNA-seq cell type annotation via pre-trained logistic regression. 45+ models: immune, gut, lung, brain, fetal, cancer microenvironments. Input normalized AnnData; outputs per-cell labels, majority-vote cluster labels, confidence scores. Use for fast, reference-backed annotation without manual marker inspection; read cell-type-annotation first.
Instalare
npx skills add jaechang-hits/SciAgent-Skills --skill celltypist-cell-annotation -a claude-code
Se instalează în .claude/skills/celltypist-cell-annotation/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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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.
- cell-type-annotationjaechang-hits/SciAgent-SkillsscRNA-seq cell type annotation - read before any annotation skills. Chooses and runs the route: name clusters manually from canonical markers (scanpy, runnable code here), transfer labels with CellTypist or a popV/scANVI on a reference, or do both and validate one against other. Covers package availability, tissue- and state-matched references, marker panels, confidence and marker validation of transferred labels, doublets, unresolved clusters. Tool-specific code lives in celltypist/popv-cell-annotation and scvi-tools-single-cell.Planificare★ 382
- popv-cell-annotationjaechang-hits/SciAgent-SkillsConsensus cell type annotation: runs 10+ algorithms (KNN-Harmony/BBKNN/Scanorama/scVI, CellTypist, ONCLASS, Random Forest, SCANVI, SVM, XGBoost) on a labeled reference and transfers labels via majority voting. Outputs per-method labels, consensus, agreement score. Use when single-method annotation is insufficient or you need ensemble uncertainty for novel states; to choose between manual markers, CellTypist and popV, read cell-type-annotation first.Altele★ 382
- single-cell-annotationjaechang-hits/SciAgent-SkillsBest practices for single-cell RNA-seq cell type annotation including marker-based, reference-based, and automated classification approaches.Altele★ 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
- scgptaipoch/open-scienceEmbed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For probabilistic single-cell models (scVI etc.), use the scvi-tools library.Știință★ 5.4K
- 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
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