[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MB3T1J7WAFN64D5ANNZRKY":3,"skill-same:01M6MB3T1J7WAFN64D5ANNZRKY":87},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6MB3T1J7WAFN64D5ANNZRKY","scvelo",null,"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.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1566,"BSD-3-Clause","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fscvelo\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"probability","when","gene","embedding","transcriptomics","direction","differentiation","starsolo","ranking","modeling","pandas","RNA","life","time","velocity","objects","releases","model","scanpy","analysis","2026-11-10 10:12:30",false,"---\nname: scvelo\ndescription: 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.\nlicense: BSD-3-Clause\ncompatibility: Requires Python 3.10+ with scvelo, scanpy, and anndata. Verified against scvelo 0.3.4, whose dynamical model and pl.scatter need pandas\u003C3 and whose stochastic estimator needs numpy\u003C2; the deterministic estimator works on current releases.\nmetadata:\n  version: '1.2'\n  category: life-sciences\n  maintainer: Kalaris Labs\n  contributor: Kuan-lin Huang\n  tags: RNA velocity, single-cell, cell trajectories, transcriptomics\n---\n\n# scVelo — RNA Velocity Analysis\n\n## Overview\n\nscVelo is the leading Python package for RNA velocity analysis in single-cell RNA-seq data. It infers cell state transitions by modeling the kinetics of mRNA splicing — using the ratio of unspliced (pre-mRNA) to spliced (mature mRNA) abundances to determine whether a gene is being upregulated or downregulated in each cell. This allows reconstruction of developmental trajectories and identification of cell fate decisions without requiring time-course data.\n\n**Installation:** `uv pip install scvelo`\n\n**Key resources:**\n- Documentation: https:\u002F\u002Fscvelo.readthedocs.io\u002F\n- GitHub: https:\u002F\u002Fgithub.com\u002Ftheislab\u002Fscvelo\n- Paper: Bergen et al. (2020) Nature Biotechnology. PMID: 32747759\n\n## When to Use This Skill\n\nUse scVelo when:\n\n- **Trajectory inference from snapshot data**: Determine which direction cells are differentiating\n- **Cell fate prediction**: Identify progenitor cells and their downstream fates\n- **Driver gene identification**: Find genes whose dynamics best explain observed trajectories\n- **Developmental biology**: Model hematopoiesis, neurogenesis, epithelial-to-mesenchymal transitions\n- **Latent time estimation**: Order cells along a pseudotime derived from splicing dynamics\n- **Complement to Scanpy**: Add directional information to UMAP embeddings\n\n## Prerequisites\n\nscVelo requires count matrices for both **unspliced** and **spliced** RNA. These are generated by:\n1. **STARsolo** or **kallisto|bustools** with `lamanno` mode\n2. **velocyto** CLI: `velocyto run10x` \u002F `velocyto run`\n3. **alevin-fry** \u002F **simpleaf** with spliced\u002Funspliced output\n\nData is stored in an `AnnData` object with `layers[\"spliced\"]` and `layers[\"unspliced\"]`.\n\n## Standard RNA Velocity Workflow\n\n### 1. Setup and Data Loading\n\n```python\nimport scvelo as scv\nimport scanpy as sc\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Configure settings\nscv.settings.verbosity = 3       # Show computation steps\nscv.settings.presenter_view = True\nscv.settings.set_figure_params('scvelo')\n\n# Load data (AnnData with spliced\u002Funspliced layers)\n# Option A: Load from loom (velocyto output)\nadata = scv.read(\"cellranger_output.loom\", cache=True)\n\n# Option B: Merge velocyto loom with Scanpy-processed AnnData\nadata_processed = sc.read_h5ad(\"processed.h5ad\")  # Has UMAP, clusters\nadata_velocity = scv.read(\"velocyto.loom\")\nadata = scv.utils.merge(adata_processed, adata_velocity)\n\n# Verify layers\nprint(adata)\n# obs × var: N × G\n# layers: 'spliced', 'unspliced' (required)\n# obsm['X_umap'] (required for visualization)\n```\n\n### 2. Preprocessing\n\n```python\n# Filter and normalize. As of scVelo 0.3, filter_and_normalize() only filters\n# genes and normalizes per cell -- it no longer takes n_top_genes and no longer\n# log-transforms, so the log step and HVG selection come from Scanpy.\nscv.pp.filter_and_normalize(\n    adata,\n    min_shared_counts=20    # Minimum counts in spliced+unspliced\n)\nsc.pp.log1p(adata)\nsc.pp.highly_variable_genes(adata, n_top_genes=2000, subset=True)\n\n# Compute first and second order moments (means and variances)\n# knn_connectivities must be computed first\nsc.pp.neighbors(adata, n_neighbors=30, n_pcs=30)\nscv.pp.moments(\n    adata,\n    n_pcs=30,\n    n_neighbors=30\n)\n```\n\n### 3. Velocity Estimation — Stochastic Model\n\nThe stochastic model is fast and suitable for exploratory analysis:\n\n```python\n# Stochastic velocity (faster, less accurate)\nscv.tl.velocity(adata, mode='stochastic')\nscv.tl.velocity_graph(adata)\n\n# Visualize\nscv.pl.velocity_embedding_stream(\n    adata,\n    basis='umap',\n    color='leiden',\n    title=\"RNA Velocity (Stochastic)\"\n)\n```\n\n### 4. Velocity Estimation — Dynamical Model (Recommended)\n\nThe dynamical model fits the full splicing kinetics and is more accurate:\n\n```python\n# Recover dynamics (computationally intensive; ~10-30 min for 10K cells)\nscv.tl.recover_dynamics(adata, n_jobs=4)\n\n# Compute velocity from dynamical model\nscv.tl.velocity(adata, mode='dynamical')\nscv.tl.velocity_graph(adata)\n```\n\n### 5. Latent Time\n\nThe dynamical model enables computation of a shared latent time (pseudotime):\n\n```python\n# Compute latent time\nscv.tl.latent_time(adata)\n\n# Visualize latent time on UMAP\nscv.pl.scatter(\n    adata,\n    color='latent_time',\n    color_map='gnuplot',\n    size=80,\n    title='Latent time'\n)\n\n# Identify top genes ordered by latent time\ntop_genes = adata.var['fit_likelihood'].sort_values(ascending=False).index[:300]\nscv.pl.heatmap(\n    adata,\n    var_names=top_genes,\n    sortby='latent_time',\n    col_color='leiden',\n    n_convolve=100\n)\n```\n\n### 6. Driver Gene Analysis\n\n```python\n# Identify genes with highest velocity fit\nscv.tl.rank_velocity_genes(adata, groupby='leiden', min_corr=0.3)\ndf = scv.DataFrame(adata.uns['rank_velocity_genes']['names'])\nprint(df.head(10))\n\n# Speed and coherence\nscv.tl.velocity_confidence(adata)\nscv.pl.scatter(\n    adata,\n    c=['velocity_length', 'velocity_confidence'],\n    cmap='coolwarm',\n    perc=[5, 95]\n)\n\n# Phase portraits for specific genes\nscv.pl.velocity(adata, ['Cpe', 'Gnao1', 'Ins2'],\n               ncols=3, figsize=(16, 4))\n```\n\n### 7. Velocity Arrows and Pseudotime\n\n```python\n# Arrow plot on UMAP\nscv.pl.velocity_embedding(\n    adata,\n    arrow_length=3,\n    arrow_size=2,\n    color='leiden',\n    basis='umap'\n)\n\n# Stream plot (cleaner visualization)\nscv.pl.velocity_embedding_stream(\n    adata,\n    basis='umap',\n    color='leiden',\n    smooth=0.8,\n    min_mass=4\n)\n\n# Velocity pseudotime (alternative to latent time)\nscv.tl.velocity_pseudotime(adata)\nscv.pl.scatter(adata, color='velocity_pseudotime', cmap='gnuplot')\n```\n\n### 8. PAGA Trajectory Graph\n\n```python\n# PAGA graph with velocity-informed transitions\nscv.tl.paga(adata, groups='leiden')\ndf = scv.get_df(adata, 'paga\u002Ftransitions_confidence', precision=2).T\ndf.style.background_gradient(cmap='Blues').format('{:.2g}')\n\n# Plot PAGA with velocity\nscv.pl.paga(\n    adata,\n    basis='umap',\n    size=50,\n    alpha=0.1,\n    min_edge_width=2,\n    node_size_scale=1.5\n)\n```\n\n## Complete Workflow Script\n\n```python\nimport scvelo as scv\nimport scanpy as sc\n\ndef run_rna_velocity(adata, n_top_genes=2000, mode='dynamical', n_jobs=4):\n    \"\"\"\n    Complete RNA velocity workflow.\n\n    Args:\n        adata: AnnData with 'spliced' and 'unspliced' layers, UMAP in obsm\n        n_top_genes: Number of top HVGs for velocity\n        mode: 'stochastic' (fast) or 'dynamical' (accurate)\n        n_jobs: Parallel jobs for dynamical model\n\n    Returns:\n        Processed AnnData with velocity information\n    \"\"\"\n    scv.settings.verbosity = 2\n\n    # 1. Preprocessing (scVelo 0.3 dropped log\u002FHVG from filter_and_normalize)\n    scv.pp.filter_and_normalize(adata, min_shared_counts=20)\n    sc.pp.log1p(adata)\n    sc.pp.highly_variable_genes(adata, n_top_genes=n_top_genes, subset=True)\n\n    if 'neighbors' not in adata.uns:\n        sc.pp.neighbors(adata, n_neighbors=30)\n\n    scv.pp.moments(adata, n_pcs=30, n_neighbors=30)\n\n    # 2. Velocity estimation\n    if mode == 'dynamical':\n        scv.tl.recover_dynamics(adata, n_jobs=n_jobs)\n\n    scv.tl.velocity(adata, mode=mode)\n    scv.tl.velocity_graph(adata)\n\n    # 3. Downstream analyses\n    if mode == 'dynamical':\n        scv.tl.latent_time(adata)\n        scv.tl.rank_velocity_genes(adata, groupby='leiden', min_corr=0.3)\n\n    scv.tl.velocity_confidence(adata)\n    scv.tl.velocity_pseudotime(adata)\n\n    return adata\n```\n\n## Key Output Fields in AnnData\n\nAfter running the workflow, the following fields are added:\n\n| Location | Key | Description |\n|----------|-----|-------------|\n| `adata.layers` | `velocity` | RNA velocity per gene per cell |\n| `adata.layers` | `fit_t` | Fitted latent time per gene per cell |\n| `adata.obsm` | `velocity_umap` | 2D velocity vectors on UMAP |\n| `adata.obs` | `velocity_pseudotime` | Pseudotime from velocity |\n| `adata.obs` | `latent_time` | Latent time from dynamical model |\n| `adata.obs` | `velocity_length` | Speed of each cell |\n| `adata.obs` | `velocity_confidence` | Confidence score per cell |\n| `adata.var` | `fit_likelihood` | Gene-level model fit quality |\n| `adata.var` | `fit_alpha` | Transcription rate |\n| `adata.var` | `fit_beta` | Splicing rate |\n| `adata.var` | `fit_gamma` | Degradation rate |\n| `adata.uns` | `velocity_graph` | Cell-cell transition probability matrix |\n\n## Velocity Models Comparison\n\n| Model | Speed | Accuracy | When to Use |\n|-------|-------|----------|-------------|\n| `stochastic` | Fast | Moderate | Exploratory; large datasets |\n| `deterministic` | Medium | Moderate | Simple linear kinetics |\n| `dynamical` | Slow | High | Publication-quality; identifies driver genes |\n\n## Best Practices\n\n- **Start with stochastic mode** for exploration; switch to dynamical for final analysis\n- **Need good coverage of unspliced reads**: Short reads (\u003C 100 bp) may miss intron coverage\n- **Minimum 2,000 cells**: RNA velocity is noisy with fewer cells\n- **Velocity should be coherent**: Arrows should follow known biology; randomness indicates issues\n- **k-NN bandwidth matters**: Too few neighbors → noisy velocity; too many → oversmoothed\n- **Sanity check**: Root cells (progenitors) should have high unspliced\u002Fspliced ratios for marker genes\n- **Dynamical model requires distinct kinetic states**: Works best for clear differentiation processes\n\n## Troubleshooting\n\n| Problem | Solution |\n|---------|---------|\n| Missing unspliced layer | Re-run velocyto or use STARsolo with `--soloFeatures Gene Velocyto` |\n| Very few velocity genes | Lower `min_shared_counts`; check sequencing depth |\n| Random-looking arrows | Try different `n_neighbors` or velocity model |\n| Memory error with dynamical | Set `n_jobs=1`; reduce `n_top_genes` |\n| Negative velocity everywhere | Check that spliced\u002Funspliced layers are not swapped |\n\n## Additional Resources\n\n- **scVelo documentation**: https:\u002F\u002Fscvelo.readthedocs.io\u002F\n- **Tutorial notebooks**: https:\u002F\u002Fscvelo.readthedocs.io\u002Ftutorials\u002F\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Ftheislab\u002Fscvelo\n- **Paper**: Bergen V et al. (2020) Nature Biotechnology. PMID: 32747759\n- **velocyto** (preprocessing): http:\u002F\u002Fvelocyto.org\u002F\n- **CellRank** (fate prediction, extends scVelo): https:\u002F\u002Fcellrank.readthedocs.io\u002F\n- **dynamo** (metabolic labeling alternative): https:\u002F\u002Fdynamo-release.readthedocs.io\u002F\n\n## Related skills\n\n- `scanpy`: Standard single-cell RNA-seq analysis pipeline.\n- `openpiv`: Particle Image Velocimetry (PIV) analysis with OpenPIV.\n","# scVelo — RNA Velocity Analysis\n\n## Overview\n\nscVelo is the leading Python package for RNA velocity analysis in single-cell RNA-seq data. It infers cell state transitions by modeling the kinetics of mRNA splicing — using the ratio of unspliced (pre-mRNA) to spliced (mature mRNA) abundances to determine whether a gene is being upregulated or downregulated in each cell. This allows reconstruction of developmental trajectories and identification of cell fate decisions without requiring time-course data.\n\n**Installation:** `uv pip install scvelo`\n\n**Key resources:**\n- Documentation: https:\u002F\u002Fscvelo.readthedocs.io\u002F\n- GitHub: https:\u002F\u002Fgithub.com\u002Ftheislab\u002Fscvelo\n- Paper: Bergen et al. (2020) Nature Biotechnology. PMID: 32747759\n\n## When to Use This Skill\n\nUse scVelo when:\n\n- **Trajectory inference from snapshot data**: Determine which direction cells are differentiating\n- **Cell fate prediction**: Identify progenitor cells and their downstream fates\n- **Driver gene identification**: Find genes whose dynamics best explain observed trajectories\n- **Developmental biology**: Model hematopoiesis, neurogenesis, epithelial-to-mesenchymal transitions\n- **Latent time estimation**: Order cells along a pseudotime derived from splicing dynamics\n- **Complement to Scanpy**: Add directional information to UMAP embeddings\n\n## Prerequisites\n\nscVelo requires count matrices for both **unspliced** and **spliced** RNA. These are generated by:\n1. **STARsolo** or **kallisto|bustools** with `lamanno` mode\n2. **velocyto** CLI: `velocyto run10x` \u002F `velocyto run`\n3. **alevin-fry** \u002F **simpleaf** with spliced\u002Funspliced output\n\nData is stored in an `AnnData` object with `layers[\"spliced\"]` and `layers[\"unspliced\"]`.\n\n## Standard RNA Velocity Workflow\n\n### 1. Setup and Data Loading\n\n```python\nimport scvelo as scv\nimport scanpy as sc\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Configure settings\nscv.settings.verbosity = 3       # Show computation steps\nscv.settings.presenter_view = True\nscv.settings.set_figure_params('scvelo')\n\n# Load data (AnnData with spliced\u002Funspliced layers)\n# Option A: Load from loom (velocyto output)\nadata = scv.read(\"cellranger_output.loom\", cache=True)\n\n# Option B: Merge velocyto loom with Scanpy-processed AnnData\nadata_processed = sc.read_h5ad(\"processed.h5ad\")  # Has UMAP, clusters\nadata_velocity = scv.read(\"velocyto.loom\")\nadata = scv.utils.merge(adata_processed, adata_velocity)\n\n# Verify layers\nprint(adata)\n# obs × var: N × G\n# layers: 'spliced', 'unspliced' (required)\n# obsm['X_umap'] (required for visualization)\n```\n\n### 2. Preprocessing\n\n```python\n# Filter and normalize. As of scVelo 0.3, filter_and_normalize() only filters\n# genes and normalizes per cell -- it no longer takes n_top_genes and no longer\n# log-transforms, so the log step and HVG selection come from Scanpy.\nscv.pp.filter_and_normalize(\n    adata,\n    min_shared_counts=20    # Minimum counts in spliced+unspliced\n)\nsc.pp.log1p(adata)\nsc.pp.highly_variable_genes(adata, n_top_genes=2000, subset=True)\n\n# Compute first and second order moments (means and variances)\n# knn_connectivities must be computed first\nsc.pp.neighbors(adata, n_neighbors=30, n_pcs=30)\nscv.pp.moments(\n    adata,\n    n_pcs=30,\n    n_neighbors=30\n)\n```\n\n### 3. Velocity Estimation — Stochastic Model\n\nThe stochastic model is fast and suitable for exploratory analysis:\n\n```python\n# Stochastic velocity (faster, less accurate)\nscv.tl.velocity(adata, mode='stochastic')\nscv.tl.velocity_graph(adata)\n\n# Visualize\nscv.pl.velocity_embedding_stream(\n    adata,\n    basis='umap',\n    color='leiden',\n    title=\"RNA Velocity (Stochastic)\"\n)\n```\n\n### 4. Velocity Estimation — Dynamical Model (Recommended)\n\nThe dynamical model fits the full splicing kinetics and is more accurate:\n\n```python\n# Recover dynamics (computationally intensive; ~10-30 min for 10K cells)\nscv.tl.recover_dynamics(adata, n_jobs=4)\n\n# Compute velocity from dynamical model\nscv.tl.velocity(adata, mode='dynamical')\nscv.tl.velocity_graph(adata)\n```\n\n### 5. Latent Time\n\nThe dynamical model enables computation of a shared latent time (pseudotime):\n\n```python\n# Compute latent time\nscv.tl.latent_time(adata)\n\n# Visualize latent time on UMAP\nscv.pl.scatter(\n    adata,\n    color='latent_time',\n    color_map='gnuplot',\n    size=80,\n    title='Latent time'\n)\n\n# Identify top genes ordered by latent time\ntop_genes = adata.var['fit_likelihood'].sort_values(ascending=False).index[:300]\nscv.pl.heatmap(\n    adata,\n    var_names=top_genes,\n    sortby='latent_time',\n    col_color='leiden',\n    n_convolve=100\n)\n```\n\n### 6. Driver Gene Analysis\n\n```python\n# Identify genes with highest velocity fit\nscv.tl.rank_velocity_genes(adata, groupby='leiden', min_corr=0.3)\ndf = scv.DataFrame(adata.uns['rank_velocity_genes']['names'])\nprint(df.head(10))\n\n# Speed and coherence\nscv.tl.velocity_confidence(adata)\nscv.pl.scatter(\n    adata,\n    c=['velocity_length', 'velocity_confidence'],\n    cmap='coolwarm',\n    perc=[5, 95]\n)\n\n# Phase portraits for specific genes\nscv.pl.velocity(adata, ['Cpe', 'Gnao1', 'Ins2'],\n               ncols=3, figsize=(16, 4))\n```\n\n### 7. Velocity Arrows and Pseudotime\n\n```python\n# Arrow plot on UMAP\nscv.pl.velocity_embedding(\n    adata,\n    arrow_length=3,\n    arrow_size=2,\n    color='leiden',\n    basis='umap'\n)\n\n# Stream plot (cleaner visualization)\nscv.pl.velocity_embedding_stream(\n    adata,\n    basis='umap',\n    color='leiden',\n    smooth=0.8,\n    min_mass=4\n)\n\n# Velocity pseudotime (alternative to latent time)\nscv.tl.velocity_pseudotime(adata)\nscv.pl.scatter(adata, color='velocity_pseudotime', cmap='gnuplot')\n```\n\n### 8. PAGA Trajectory Graph\n\n```python\n# PAGA graph with velocity-informed transitions\nscv.tl.paga(adata, groups='leiden')\ndf = scv.get_df(adata, 'paga\u002Ftransitions_confidence', precision=2).T\ndf.style.background_gradient(cmap='Blues').format('{:.2g}')\n\n# Plot PAGA with velocity\nscv.pl.paga(\n    adata,\n    basis='umap',\n    size=50,\n    alpha=0.1,\n    min_edge_width=2,\n    node_size_scale=1.5\n)\n```\n\n## Complete Workflow Script\n\n```python\nimport scvelo as scv\nimport scanpy as sc\n\ndef run_rna_velocity(adata, n_top_genes=2000, mode='dynamical', n_jobs=4):\n    \"\"\"\n    Complete RNA velocity workflow.\n\n    Args:\n        adata: AnnData with 'spliced' and 'unspliced' layers, UMAP in obsm\n        n_top_genes: Number of top HVGs for velocity\n        mode: 'stochastic' (fast) or 'dynamical' (accurate)\n        n_jobs: Parallel jobs for dynamical model\n\n    Returns:\n        Processed AnnData with velocity information\n    \"\"\"\n    scv.settings.verbosity = 2\n\n    # 1. Preprocessing (scVelo 0.3 dropped log\u002FHVG from filter_and_normalize)\n    scv.pp.filter_and_normalize(adata, min_shared_counts=20)\n    sc.pp.log1p(adata)\n    sc.pp.highly_variable_genes(adata, n_top_genes=n_top_genes, subset=True)\n\n    if 'neighbors' not in adata.uns:\n        sc.pp.neighbors(adata, n_neighbors=30)\n\n    scv.pp.moments(adata, n_pcs=30, n_neighbors=30)\n\n    # 2. Velocity estimation\n    if mode == 'dynamical':\n        scv.tl.recover_dynamics(adata, n_jobs=n_jobs)\n\n    scv.tl.velocity(adata, mode=mode)\n    scv.tl.velocity_graph(adata)\n\n    # 3. Downstream analyses\n    if mode == 'dynamical':\n        scv.tl.latent_time(adata)\n        scv.tl.rank_velocity_genes(adata, groupby='leiden', min_corr=0.3)\n\n    scv.tl.velocity_confidence(adata)\n    scv.tl.velocity_pseudotime(adata)\n\n    return adata\n```\n\n## Key Output Fields in AnnData\n\nAfter running the workflow, the following fields are added:\n\n| Location | Key | Description |\n|----------|-----|-------------|\n| `adata.layers` | `velocity` | RNA velocity per gene per cell |\n| `adata.layers` | `fit_t` | Fitted latent time per gene per cell |\n| `adata.obsm` | `velocity_umap` | 2D velocity vectors on UMAP |\n| `adata.obs` | `velocity_pseudotime` | Pseudotime from velocity |\n| `adata.obs` | `latent_time` | Latent time from dynamical model |\n| `adata.obs` | `velocity_length` | Speed of each cell |\n| `adata.obs` | `velocity_confidence` | Confidence score per cell |\n| `adata.var` | `fit_likelihood` | Gene-level model fit quality |\n| `adata.var` | `fit_alpha` | Transcription rate |\n| `adata.var` | `fit_beta` | Splicing rate |\n| `adata.var` | `fit_gamma` | Degradation rate |\n| `adata.uns` | `velocity_graph` | Cell-cell transition probability matrix |\n\n## Velocity Models Comparison\n\n| Model | Speed | Accuracy | When to Use |\n|-------|-------|----------|-------------|\n| `stochastic` | Fast | Moderate | Exploratory; large datasets |\n| `deterministic` | Medium | Moderate | Simple linear kinetics |\n| `dynamical` | Slow | High | Publication-quality; identifies driver genes |\n\n## Best Practices\n\n- **Start with stochastic mode** for exploration; switch to dynamical for final analysis\n- **Need good coverage of unspliced reads**: Short reads (\u003C 100 bp) may miss intron coverage\n- **Minimum 2,000 cells**: RNA velocity is noisy with fewer cells\n- **Velocity should be coherent**: Arrows should follow known biology; randomness indicates issues\n- **k-NN bandwidth matters**: Too few neighbors → noisy velocity; too many → oversmoothed\n- **Sanity check**: Root cells (progenitors) should have high unspliced\u002Fspliced ratios for marker genes\n- **Dynamical model requires distinct kinetic states**: Works best for clear differentiation processes\n\n## Troubleshooting\n\n| Problem | Solution |\n|---------|---------|\n| Missing unspliced layer | Re-run velocyto or use STARsolo with `--soloFeatures Gene Velocyto` |\n| Very few velocity genes | Lower `min_shared_counts`; check sequencing depth |\n| Random-looking arrows | Try different `n_neighbors` or velocity model |\n| Memory error with dynamical | Set `n_jobs=1`; reduce `n_top_genes` |\n| Negative velocity everywhere | Check that spliced\u002Funspliced layers are not swapped |\n\n## Additional Resources\n\n- **scVelo documentation**: https:\u002F\u002Fscvelo.readthedocs.io\u002F\n- **Tutorial notebooks**: https:\u002F\u002Fscvelo.readthedocs.io\u002Ftutorials\u002F\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Ftheislab\u002Fscvelo\n- **Paper**: Bergen V et al. (2020) Nature Biotechnology. PMID: 32747759\n- **velocyto** (preprocessing): http:\u002F\u002Fvelocyto.org\u002F\n- **CellRank** (fate prediction, extends scVelo): https:\u002F\u002Fcellrank.readthedocs.io\u002F\n- **dynamo** (metabolic labeling alternative): https:\u002F\u002Fdynamo-release.readthedocs.io\u002F\n\n## Related skills\n\n- `scanpy`: Standard single-cell RNA-seq analysis pipeline.\n- `openpiv`: Particle Image Velocimetry (PIV) analysis with OpenPIV.\n",{"name":5,"license":15,"metadata":44,"description":7,"compatibility":50},{"tags":45,"version":46,"category":47,"maintainer":48,"contributor":49},"RNA velocity, single-cell, cell trajectories, transcriptomics","1.2","life-sciences","Kalaris Labs","Kuan-lin Huang","Requires Python 3.10+ with scvelo, scanpy, and anndata. Verified against scvelo 0.3.4, whose dynamical model and pl.scatter need pandas\u003C3 and whose stochastic estimator needs numpy\u003C2; the deterministic estimator works on current releases.","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fscvelo",483,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-13 02:16:32",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill scvelo",{"tool":67,"command":68},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill scvelo",{"tool":70,"command":71},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill scvelo","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fscvelo\u002FSKILL.md",true,[75,81],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fvelocity_models.md","REFERENCE","参考文档",5010,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fscvelo\u002Freferences\u002Fvelocity_models.md",{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"scripts\u002Frna_velocity_workflow.py","SCRIPT","脚本",8676,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fscvelo\u002Fscripts\u002Frna_velocity_workflow.py",[88,97,104,111,137,162],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":96},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":96},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":110,"publishedAt":96},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":39},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[118,119,120,121,122,123,124,125,126,127,128,20,129,130,131,132,133,134,135,136],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","you","done","reviewer","io","change","cli","merge","past",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":143,"publishedAt":39},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[144,145,146,132,147,148,149,150,151,152,153,154,155,156,123,157,158,159,160,161],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":179,"publishedAt":39},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[168,173,176],{"categoryNo":169,"categoryName":170,"taxonomyType":171,"taxonomyTypeDesc":172},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":174,"categoryName":175,"taxonomyType":171,"taxonomyTypeDesc":172},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":177,"categoryName":178,"taxonomyType":171,"taxonomyTypeDesc":172},"01JBSEEDCAT000000000000010","后端与接口",[164,180,181,182,183,184,185,158,186,124,187,188,189,190,20,191,192,193,194,195],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]