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Add scVelo RNA velocity analysis workflow and IQ-TREE reference documentation
- Introduced a comprehensive RNA velocity analysis pipeline using scVelo, including data loading, preprocessing, velocity estimation, and visualization. - Added a script for running RNA velocity analysis with customizable parameters and output options. - Created detailed documentation for IQ-TREE 2 phylogenetic inference, covering command syntax, model selection, bootstrapping methods, and output interpretation. - Included references for velocity models and their mathematical framework, along with a comparison of different models. - Enhanced the scVelo skill documentation with installation instructions, use cases, and best practices for RNA velocity analysis.
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huangkuanlin
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scientific-skills/scvelo/scripts/rna_velocity_workflow.py
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scientific-skills/scvelo/scripts/rna_velocity_workflow.py
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"""
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RNA Velocity Analysis Workflow using scVelo
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===========================================
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Complete pipeline from raw data to velocity visualization.
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Usage:
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python rna_velocity_workflow.py
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Or import and use run_velocity_analysis() with your AnnData object.
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"""
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import scvelo as scv
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import scanpy as sc
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import numpy as np
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import matplotlib
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matplotlib.use('Agg') # Non-interactive backend
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import matplotlib.pyplot as plt
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import os
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def run_velocity_analysis(
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adata,
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groupby="leiden",
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n_top_genes=2000,
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n_neighbors=30,
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mode="dynamical",
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n_jobs=4,
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output_dir="velocity_results",
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):
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"""
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Complete RNA velocity analysis workflow.
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Parameters
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----------
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adata : AnnData
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AnnData object with 'spliced' and 'unspliced' layers.
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Should already have UMAP and cluster annotations.
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groupby : str
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Column in adata.obs for cell type labels.
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n_top_genes : int
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Number of top highly variable genes.
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n_neighbors : int
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Number of neighbors for moment computation.
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mode : str
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Velocity model: 'stochastic' (fast) or 'dynamical' (accurate).
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n_jobs : int
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Parallel jobs for dynamical model fitting.
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output_dir : str
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Directory for saving output figures.
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Returns
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-------
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AnnData with velocity annotations.
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"""
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os.makedirs(output_dir, exist_ok=True)
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# ── Settings ──────────────────────────────────────────────────────────────
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scv.settings.verbosity = 2
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scv.settings.figdir = output_dir
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# ── Step 1: Check layers ───────────────────────────────────────────────────
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assert "spliced" in adata.layers, "Missing 'spliced' layer. Run velocyto first."
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assert "unspliced" in adata.layers, "Missing 'unspliced' layer. Run velocyto first."
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print(f"Input: {adata.n_obs} cells × {adata.n_vars} genes")
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# ── Step 2: Preprocessing ─────────────────────────────────────────────────
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print("Step 1/5: Preprocessing...")
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scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=n_top_genes)
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if "neighbors" not in adata.uns:
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sc.pp.neighbors(adata, n_neighbors=n_neighbors, n_pcs=30)
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scv.pp.moments(adata, n_pcs=30, n_neighbors=n_neighbors)
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print(f" {adata.n_vars} velocity genes selected")
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# ── Step 3: Velocity estimation ────────────────────────────────────────────
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print(f"Step 2/5: Fitting velocity model ({mode})...")
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if mode == "dynamical":
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scv.tl.recover_dynamics(adata, n_jobs=n_jobs)
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scv.tl.velocity(adata, mode=mode)
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scv.tl.velocity_graph(adata)
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print(" Velocity graph computed")
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# ── Step 4: Downstream analyses ────────────────────────────────────────────
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print("Step 3/5: Computing latent time and confidence...")
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scv.tl.velocity_confidence(adata)
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scv.tl.velocity_pseudotime(adata)
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if mode == "dynamical":
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scv.tl.latent_time(adata)
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if groupby in adata.obs.columns:
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scv.tl.rank_velocity_genes(adata, groupby=groupby, min_corr=0.3)
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# ── Step 5: Visualization ─────────────────────────────────────────────────
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print("Step 4/5: Generating figures...")
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# Stream plot
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scv.pl.velocity_embedding_stream(
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adata,
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basis="umap",
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color=groupby,
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title="RNA Velocity",
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save=f"{output_dir}/velocity_stream.png",
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)
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# Arrow plot
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scv.pl.velocity_embedding(
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adata,
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arrow_length=3,
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arrow_size=2,
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color=groupby,
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basis="umap",
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save=f"{output_dir}/velocity_arrows.png",
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)
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# Pseudotime
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scv.pl.scatter(
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adata,
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color="velocity_pseudotime",
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cmap="gnuplot",
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title="Velocity Pseudotime",
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save=f"{output_dir}/pseudotime.png",
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)
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if mode == "dynamical" and "latent_time" in adata.obs:
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scv.pl.scatter(
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adata,
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color="latent_time",
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color_map="gnuplot",
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title="Latent Time",
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save=f"{output_dir}/latent_time.png",
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)
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# Speed and coherence
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scv.pl.scatter(
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adata,
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c=["velocity_length", "velocity_confidence"],
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cmap="coolwarm",
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perc=[5, 95],
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save=f"{output_dir}/velocity_quality.png",
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)
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# Top driver genes heatmap (dynamical only)
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if mode == "dynamical" and "fit_likelihood" in adata.var:
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top_genes = adata.var["fit_likelihood"].sort_values(ascending=False).index[:50]
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scv.pl.heatmap(
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adata,
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var_names=top_genes,
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sortby="latent_time",
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col_color=groupby,
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n_convolve=50,
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save=f"{output_dir}/driver_gene_heatmap.png",
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)
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# ── Step 6: Save results ───────────────────────────────────────────────────
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print("Step 5/5: Saving results...")
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output_h5ad = os.path.join(output_dir, "adata_velocity.h5ad")
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adata.write_h5ad(output_h5ad)
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print(f" Saved to {output_h5ad}")
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# Summary statistics
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confidence = adata.obs["velocity_confidence"].dropna()
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print("\nSummary:")
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print(f" Velocity model: {mode}")
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print(f" Cells: {adata.n_obs}")
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print(f" Velocity genes: {adata.n_vars}")
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print(f" Mean velocity confidence: {confidence.mean():.3f}")
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print(f" High-confidence cells (>0.7): {(confidence > 0.7).sum()} ({(confidence > 0.7).mean():.1%})")
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if mode == "dynamical" and "fit_likelihood" in adata.var:
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good_genes = (adata.var["fit_likelihood"] > 0.1).sum()
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print(f" Well-fit genes (likelihood>0.1): {good_genes}")
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print(f"\nOutput files saved to: {output_dir}/")
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return adata
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def load_from_loom(loom_path, processed_h5ad=None):
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"""
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Load velocity data from velocyto loom file.
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Args:
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loom_path: Path to velocyto output loom file
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processed_h5ad: Optional path to pre-processed Scanpy h5ad file
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"""
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adata_loom = scv.read(loom_path, cache=True)
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if processed_h5ad:
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adata_processed = sc.read_h5ad(processed_h5ad)
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# Merge: keep processed metadata and add velocity layers
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adata = scv.utils.merge(adata_processed, adata_loom)
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else:
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adata = adata_loom
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# Run basic Scanpy pipeline
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sc.pp.normalize_total(adata, target_sum=1e4)
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sc.pp.log1p(adata)
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sc.pp.highly_variable_genes(adata, n_top_genes=3000)
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sc.pp.pca(adata)
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sc.pp.neighbors(adata)
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sc.tl.umap(adata)
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sc.tl.leiden(adata, resolution=0.5)
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return adata
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if __name__ == "__main__":
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# Example usage with simulated data (for testing)
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print("scVelo RNA Velocity Workflow - Demo Mode")
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print("=" * 50)
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# Load example dataset
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adata = scv.datasets.pancreas()
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print(f"Loaded pancreas dataset: {adata}")
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# Run analysis
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adata = run_velocity_analysis(
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adata,
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groupby="clusters",
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n_top_genes=2000,
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mode="dynamical",
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n_jobs=2,
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output_dir="pancreas_velocity",
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)
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print("\nAnalysis complete!")
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print(f"Key results:")
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print(f" adata.layers['velocity']: velocity per gene per cell")
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print(f" adata.obs['latent_time']: pseudotime from dynamics")
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print(f" adata.obs['velocity_confidence']: per-cell confidence")
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if "rank_velocity_genes" in adata.uns:
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print(f" adata.uns['rank_velocity_genes']: driver genes per cluster")
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