trajectory-inference
De la autor
Trajectory inference and cell fate mapping for single-cell and spatial transcriptomics: PAGA graph + diffusion pseudotime (no velocity needed), scVelo RNA velocity (stochastic / dynamical, from spliced/unspliced counts), CellRank 2 terminal states, fate probabilities and lineage drivers, then pseudotime and fates mapped on tissue with a spatial autocorrelation check. Outputs pseudotime, velocity streams, fate probability tables. Imaging panels (Xenium, MERFISH) have no unspliced counts: use PAGA/DPT or the CellRank pseudotime kernel. For a basic PAGA recipe inside a standard scRNA-seq workflow see scanpy-scrna-seq.
Instalare
npx skills add jaechang-hits/SciAgent-Skills --skill trajectory-inference -a claude-code
Se instalează în .claude/skills/trajectory-inference/ · 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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Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- scveloK-Dense-AI/scientific-agent-skillsPerforms RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.Altele★ 54.1K
- 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-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-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-deconvolution-cell2locationjaechang-hits/SciAgent-SkillsVisium 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.Documente★ 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
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