De la autor
Performs RNA velocity analysis with scVelo on single-cell RNA-seq AnnData objects that have spliced and unspliced layers (from velocyto, STARsolo, kallisto|bustools, or alevin-fry). Covers stochastic and dynamical velocity models, velocity graphs and embedding arrows, latent time, PAGA trajectory graphs, and driver gene ranking. Use when inferring differentiation direction from snapshot data, estimating latent time from splicing kinetics, finding driver genes of a trajectory, or adding velocity arrows to a Scanpy UMAP. Not for fate probability modeling (use CellRank) or datasets without unspliced counts.
Instalare
npx skills add KalarisLabs/research-agent-skills --skill scvelo -a claude-code
Se instalează în .claude/skills/scvelo/ · 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.
Vrei ca agentul să-ți propună skill-uri în timp ce scrii? Vezi cum folosești skill-urile din IDE.
Verificări de siguranță pe SKILL.md
- Include scripturi pe care nu le-am verificatScăzut
De ce 77
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.
- alterlab-scveloAlterLab-IEU/AlterLab-Academic-SkillsRun RNA velocity analysis with scVelo on single-cell RNA-seq data — estimate cell-state transitions from spliced/unspliced mRNA dynamics, infer trajectory direction, compute latent time, and identify driver genes. Use when adding directionality to trajectories or studying differentiation dynamics from spliced/unspliced layers (velocyto/STARsolo output); for the general QC, clustering, UMAP, and differential-expression analysis pipeline prefer alterlab-scanpy instead, and for .h5ad data-structure I/O and layer wrangling prefer alterlab-anndata instead. Part of the AlterLab Academic Skills suite.Altele★ 84
- trajectory-inferencejaechang-hits/SciAgent-SkillsTrajectory 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.Productivitate★ 384
- trajectory-analysisawslabs/hcls-agent-skillsSingle-cell trajectory inference pipeline covering diffusion pseudotime (DPT), PAGA, RNA velocity with scVelo, and fate mapping with CellRank. Use when the user mentions pseudotime, trajectory, lineage, differentiation, RNA velocity, scVelo, CellRank, PAGA, diffusion map, fate probabilities, terminal states, spliced/unspliced, velocyto, order cells by development, cell differentiation path, cell fate, branching analysis, monocle, developmental trajectory, stem cell differentiation, progenitor to mature, how do cells differentiate, cell lineage tree, velocity arrows, fate mapping, transition probabilities, root cells, terminal states, absorption probabilities, or latent time.Altele★ 71
- alterlab-scanpyAlterLab-IEU/AlterLab-Academic-SkillsRun 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, PAGA trajectories, and plotting. Use when analyzing scRNA-seq data through clustering, cell-type annotation, DE, or pseudotime workflows; for building or reading the .h5ad data structure itself (layers, obs/var, concatenation, backed mode) prefer alterlab-anndata instead, and for RNA velocity from spliced/unspliced counts prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.Altele★ 84
- scanpyK-Dense-AI/scientific-agent-skillsPerforms Scanpy single-cell RNA-seq QC, normalization, HVG selection, PCA/UMAP/t-SNE, clustering, exploratory marker ranking, pseudobulk preparation, visualization, and Seurat or SingleCellExperiment RDS conversion to h5ad. Applies to established exploratory scRNA-seq workflows with explicit count and expression provenance; complementary skills cover scvi-tools models and AnnData format details.Marketing★ 55.8K
- scvi-toolsanthropics/knowledge-work-pluginsDeep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.OficialAltele★ 34.4K
Note și recenzii
—
nicio recenzie
Ai încercat acest skill? Notează-l ca să-i ajuți pe alții. Autentifică-te
Discuție
Intră în discuție. Autentifică-te