drug-discovery-prompts/upstream/mims-harvard-ToolUniverse/skills/tooluniverse-single-cell/references/trajectory_analysis.md

title, task, lineage_type, upstream_source, upstream_sha, imported_at, prompt_class, upstream_changes, author, validated

title	task	lineage_type	upstream_source	upstream_sha	imported_at	prompt_class	upstream_changes	author	validated
Trajectory Inference and Pseudotime Analysis		import	https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-single-cell/references/trajectory_analysis.md	e2520a96	2026-06-26	prompt	accepted	upstream	false

Trajectory Inference and Pseudotime Analysis

Guide to trajectory analysis and pseudotime ordering in single-cell data.

---

Overview

Trajectory analysis orders cells along a developmental or temporal continuum (pseudotime). Use for:

• Differentiation studies (stem cells → mature cells)
• Cell cycle analysis
• Response to stimulation over time
• Disease progression

---

Diffusion Pseudotime (DPT)

Built into scanpy, based on diffusion maps.

import scanpy as sc

# After standard preprocessing and clustering
# 1. Identify root cell (start of trajectory)
adata.uns['iroot'] = np.flatnonzero(
    adata.obs['leiden'] == '0'  # Choose starting cluster
)[0]

# 2. Compute diffusion map
sc.tl.diffmap(adata)

# 3. Compute DPT
sc.tl.dpt(adata)

# 4. Visualize
sc.pl.umap(adata, color=['dpt_pseudotime', 'leiden'])

# Pseudotime values in: adata.obs['dpt_pseudotime']

---

PAGA (Partition-based Graph Abstraction)

Models trajectories as cluster connectivity graph.

# After clustering
sc.tl.paga(adata, groups='leiden')

# Plot PAGA graph
sc.pl.paga(adata, color='leiden')

# Initialize UMAP positions from PAGA
sc.tl.umap(adata, init_pos='paga')

# Plot trajectory
sc.pl.umap(adata, color=['leiden', 'CD34'])  # Stem cell marker

---

Gene Expression Along Pseudotime

# Genes that change along pseudotime
sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon')

# Plot gene expression vs pseudotime
import matplotlib.pyplot as plt

genes_of_interest = ['CD34', 'CD38', 'CD14']
for gene in genes_of_interest:
    if gene in adata.var_names:
        plt.figure()
        plt.scatter(
            adata.obs['dpt_pseudotime'],
            adata[:, gene].X.toarray().flatten(),
            alpha=0.3, s=5
        )
        plt.xlabel('Pseudotime')
        plt.ylabel(f'{gene} expression')
        plt.title(f'{gene} along trajectory')
        plt.show()

---

External Tools

PAGA with RNA velocity (scVelo)

import scvelo as scv

# Compute RNA velocity
scv.pp.filter_and_normalize(adata)
scv.pp.moments(adata)
scv.tl.velocity(adata)
scv.tl.velocity_graph(adata)

# Visualize
scv.pl.velocity_embedding_stream(adata, basis='umap')

Monocle-style (via Python wrapper)

# Not native to scanpy, requires specialized packages
# For Monocle analysis, consider using R/Seurat integration

---

Tips

1. Root cell selection: Critical for pseudotime. Choose starting cell type.
2. Linear vs branched: DPT handles linear trajectories, PAGA handles branching.
3. Validation: Check marker gene expression matches biological expectation.
4. Multiple trajectories: For complex differentiation, use PAGA.

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See Also

• scanpy_workflow.md - Preprocessing before trajectory
• clustering_guide.md - Clustering for PAGA