Trajectory Analysis

Trajectory Analysis

Overview

Reconstructs continuous cell-state transitions from single-cell RNA-seq. Four layers:

1. Diffusion pseudotime (scanpy.tl.dpt) — orders cells along a diffusion manifold from a user-specified root.
2. PAGA — coarse-grained cluster connectivity; initializes force-directed layouts.
3. RNA velocity (scVelo) — directional flow from unspliced/spliced mRNA ratios. Note: scVelo is in maintenance mode (no new features); APIs remain stable.
4. CellRank v2 — unified kernel API combining velocity, pseudotime, real-time, or CytoTRACE signals into a Markov chain for macrostates, terminal states, and fate probabilities. CytoTRACEKernel enables velocity-free trajectory inference.

Inputs assume a preprocessed AnnData (normalized, log-transformed, HVGs, neighbors computed). Velocity additionally requires spliced/unspliced layers from velocyto or STARsolo.

Usage

Describe the analysis in natural language:

• "Compute diffusion pseudotime rooted in the HSC cluster."
• "Run PAGA on leiden clusters and re-embed with force-directed layout."
• "Run dynamical scVelo and plot the velocity stream."
• "Use CellRank with CytoTRACEKernel to find terminal states without velocity data."
• "Combine VelocityKernel and ConnectivityKernel to compute fate probabilities."

The skill produces a runnable Python script or notebook cell.

Response Format

1. Method selection — state which approach fits the user's data (use decision tree below)
2. Prerequisites check — confirm required layers/annotations exist
3. Working code — complete, copy-paste-ready script
4. Key parameters — explain non-obvious choices
5. Gotchas — warn about common failure modes specific to the chosen method

Decision Tree — Choosing a Trajectory Method

Have spliced/unspliced layers?
├─ YES → RNA velocity (scVelo dynamical) → CellRank VelocityKernel
│         Best for: steady-state or dynamical kinetics inference
│
└─ NO
    ├─ Have experimental time points?
    │   └─ YES → CellRank RealTimeKernel
    │             Best for: time-course experiments (reprogramming, perturbation)
    │
    └─ NO time points
        ├─ Want directionality (root → terminal)?
        │   ├─ CytoTRACEKernel (uses gene-count gradient as differentiation proxy)
        │   │   Best for: differentiation without velocity; no extra input needed
        │   └─ PseudotimeKernel (uses precomputed DPT as direction)
        │       Best for: when you trust your DPT root choice
        │
        └─ Just want cluster connectivity?
            └─ PAGA (no directionality, shows which clusters connect)

Core Concepts

1. Prerequisites — spliced/unspliced counts

Velocity needs spliced and unspliced layers. Generate with velocyto or STARsolo:

# Option A: velocyto run10x
velocyto run10x \
    --samtools-threads 8 \
    /path/to/cellranger_outs/sample_id \
    /path/to/genes.gtf

# Option B: STARsolo (Velocyto-compatible matrices)
STAR --runThreadN 16 \
     --genomeDir /path/to/star_index \
     --readFilesIn R2.fq.gz R1.fq.gz \
     --readFilesCommand zcat \
     --soloType CB_UMI_Simple \
     --soloCBallowlist 3M-february-2018.txt \
     --soloFeatures Gene Velocyto \
     --soloCBstart 1 --soloCBlen 16 --soloUMIstart 17 --soloUMIlen 12 \
     --outSAMtype BAM SortedByCoordinate

Merge the loom with an existing AnnData:

import scvelo as scv

adata = sc.read_h5ad("processed.h5ad")
ldata = scv.read("sample_id.loom", cache=True)
# scv.utils.merge inner-joins on barcodes — ensure obs_names match between objects
adata = scv.utils.merge(adata, ldata)
assert "spliced" in adata.layers and "unspliced" in adata.layers

2. Diffusion pseudotime (DPT)

import numpy as np
import scanpy as sc

sc.tl.diffmap(adata)

# Root cell: pick one cell in the known starting population
root_cluster = "HSC"
adata.uns["iroot"] = np.flatnonzero(adata.obs["celltype"] == root_cluster)[0]

sc.tl.dpt(adata)  # writes adata.obs['dpt_pseudotime']
sc.pl.umap(adata, color=["celltype", "dpt_pseudotime"])

3. PAGA

sc.tl.paga(adata, groups="leiden")

# threshold controls edge pruning: higher = fewer edges shown
# Default 0.01 shows almost everything; 0.03-0.1 is typical for clean plots
sc.pl.paga(adata, threshold=0.03, show=False)

# PAGA-initialized layout for cleaner trajectory visualization
sc.tl.draw_graph(adata, init_pos="paga")
sc.pl.draw_graph(adata, color=["leiden", "dpt_pseudotime"], legend_loc="on data")

# Gene trends along a chosen path
sc.pl.paga_path(
    adata,
    nodes=["HSC", "MPP", "GMP", "Mono"],
    keys=["Elane", "Mpo", "Gata1"],
)

PAGA threshold note: sc.pl.paga(threshold=...) only affects visualization (which edges to draw), not the underlying graph. The connectivity weights live in adata.uns['paga']['connectivities']. Set threshold=0 to see all edges, increase to declutter.

4. RNA velocity (scVelo)

Status: scVelo is in maintenance mode. APIs are stable but no new features are planned. For new projects without velocity data, consider CellRank's CytoTRACEKernel as a velocity-free alternative.

import scvelo as scv

scv.settings.n_jobs = 8

# Preprocessing — operates on spliced/unspliced layers
scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)

# Dynamical mode (recommended): fits full kinetic model per gene
scv.tl.recover_dynamics(adata, n_jobs=8)
scv.tl.velocity(adata, mode="dynamical")
scv.tl.velocity_graph(adata)

scv.pl.velocity_embedding_stream(adata, basis="umap", color="celltype")

# Latent time (dynamical mode only)
scv.tl.latent_time(adata)
scv.pl.scatter(adata, color="latent_time", color_map="gnuplot")

5. CellRank v2 — unified kernel API

CellRank v2 provides a unified kernel interface. All kernels:

• Accept an AnnData and call .compute_transition_matrix()
• Can be combined with + and * (weighted sum)
• Feed into cr.estimators.GPCCA for macrostate decomposition

5a. VelocityKernel (requires scVelo velocity)

import cellrank as cr
from cellrank.kernels import VelocityKernel, ConnectivityKernel

vk = VelocityKernel(adata).compute_transition_matrix()
ck = ConnectivityKernel(adata).compute_transition_matrix()
kernel = 0.8 * vk + 0.2 * ck  # weighted combination

g = cr.estimators.GPCCA(kernel)
g.compute_schur(n_components=20)
g.compute_macrostates(n_states=5, cluster_key="celltype")
g.predict_terminal_states()
g.compute_fate_probabilities()
g.plot_fate_probabilities(same_plot=False)

# Lineage driver genes
drivers = g.compute_lineage_drivers(lineages=["Mono"], use_raw=False)

5b. CytoTRACEKernel (velocity-free, new in v2)

Uses gene-count gradient as a proxy for differentiation potential. No velocity data needed.

from cellrank.kernels import CytoTRACEKernel, ConnectivityKernel

ctk = CytoTRACEKernel(adata).compute_transition_matrix()
ck = ConnectivityKernel(adata).compute_transition_matrix()
kernel = 0.8 * ctk + 0.2 * ck

g = cr.estimators.GPCCA(kernel)
g.compute_schur(n_components=15)
g.compute_macrostates(n_states=4, cluster_key="celltype")
g.predict_terminal_states()
g.compute_fate_probabilities()

5c. RealTimeKernel (experimental time points)

For time-course experiments where cells are collected at discrete time points.

from cellrank.kernels import RealTimeKernel

# adata.obs["day"] must contain numeric time labels (e.g., 0, 2, 4, 7)
rtk = RealTimeKernel(adata, time_key="day").compute_transition_matrix()

g = cr.estimators.GPCCA(rtk)
g.compute_macrostates(n_states=6, cluster_key="celltype")
g.predict_terminal_states()
g.compute_fate_probabilities()

5d. PseudotimeKernel (DPT-directed)

Uses precomputed pseudotime to define directionality.

from cellrank.kernels import PseudotimeKernel, ConnectivityKernel

# Requires adata.obs["dpt_pseudotime"] from sc.tl.dpt
pk = PseudotimeKernel(adata, time_key="dpt_pseudotime").compute_transition_matrix()
ck = ConnectivityKernel(adata).compute_transition_matrix()
kernel = 0.8 * pk + 0.2 * ck

g = cr.estimators.GPCCA(kernel)
g.compute_macrostates(n_states=4, cluster_key="celltype")
g.predict_terminal_states()
g.compute_fate_probabilities()

5e. Kernel combination rules

# All kernels support arithmetic combination after .compute_transition_matrix()
combined = 0.6 * vk + 0.2 * ck + 0.2 * pk  # weights must sum to 1

# Inspect kernel quality
combined.plot_projection(basis="umap")  # visualize transition arrows

6. Spatial neighbors (squidpy)

For spatial transcriptomics, replace kNN with a spatial graph:

import squidpy as sq

sq.gr.spatial_neighbors(adata, coord_type="generic", n_neighs=6)
# Feed spatial connectivities into CellRank ConnectivityKernel

Quick Reference

Common Mistakes

• Wrong: Calling sc.tl.dpt without setting adata.uns['iroot'] Right: Set the root cell index before computing DPT: adata.uns['iroot'] = np.flatnonzero(adata.obs['celltype'] == root_cluster)[0] Why: Without a root, pseudotime has no direction and results are meaningless

• Wrong: Running scVelo velocity without spliced/unspliced layers in the AnnData Right: Check adata.layers.keys() after scv.utils.merge; ensure both layers exist with matching barcodes Why: Velocity computation requires both layers; missing layers cause errors or silent failures

• Wrong: Using mode='dynamical' in scv.tl.velocity without first running scv.tl.recover_dynamics Right: Always run scv.tl.recover_dynamics(adata) before scv.tl.velocity(adata, mode='dynamical') Why: The dynamical model needs per-gene rate parameters that are only computed by recover_dynamics

• Wrong: Computing scv.tl.latent_time after running velocity in stochastic mode Right: Use mode='dynamical' for velocity if you need latent time Why: Latent time is only defined for the dynamical model; stochastic mode does not produce the required parameters

• Wrong: Calling scv.tl.velocity without running scv.pp.moments first Right: Run scv.pp.moments(adata, n_pcs=30, n_neighbors=30) before velocity computation Why: Moments (Ms, Mu layers) are required inputs for velocity estimation

• Wrong: Running sc.tl.paga before computing cluster labels Right: Run sc.tl.leiden (or equivalent clustering) first; pass the cluster key via groups= Why: PAGA computes connectivity between existing groups; without clusters it has nothing to connect

• Wrong: Thinking the threshold parameter in sc.pl.paga() affects the underlying graph Right: Understand that threshold is purely visual — it controls which edges are drawn, not computed Why: Misinterpreting this leads to incorrect claims about cluster connectivity

• Wrong: Re-running sc.pp.neighbors after computing velocity graph or CellRank transition matrices Right: If neighbors must change, recompute velocity_graph and CellRank kernels afterward Why: Velocity graph and transition matrices depend on the neighbor graph; changing it silently invalidates them

• Wrong: Using n_cells parameter in CellRank v2's compute_macrostates() Right: Use n_states — the v2 API renamed this parameter Why: n_cells was v1 syntax and will raise an error or be silently ignored in v2

• Wrong: Calling predict_terminal_states() without first inspecting macrostates Right: Always run g.plot_macrostates() and review coarse-grained transitions before predicting terminals Why: Terminal state prediction depends on macrostate quality; garbage macrostates produce garbage fate maps

• Wrong: Forcing velocity-based analysis when velocity arrows look random or incoherent Right: Switch to CytoTRACEKernel or PseudotimeKernel when velocity signal is absent Why: Random velocity arrows indicate the data lacks kinetic signal; forcing velocity produces meaningless trajectories

References

• CellRank 2: Weiler et al. Nat Methods 2024, https://doi.org/10.1038/s41592-023-02088-9
• scVelo: Bergen et al. Nat Biotechnol 2020, https://doi.org/10.1038/s41587-020-0591-3
• CellRank: Lange et al. Nat Methods 2022, https://doi.org/10.1038/s41592-021-01346-6
• PAGA: Wolf et al. Genome Biol 2019, https://doi.org/10.1186/s13059-019-1663-x
• velocyto: La Manno et al. Nature 2018, https://doi.org/10.1038/s41586-018-0414-6