omicverse-single-cell-trajectory-inference

OmicVerse Single-Cell Trajectory Inference

Goal

Run the reusable trajectory-analysis part of the OmicVerse notebook on a cluster-ready AnnData: choose a TrajInfer method branch, set origin and optional terminal states, compute pseudotime or fate outputs, and optionally summarize the trajectory with PAGA. Keep raw QC and preprocessing outside this skill; hand off to the preprocessing skill first when the object is not already graph-ready.

Quick Workflow

Inspect the input AnnData and confirm it already has a usable representation, plotting basis, and cluster labels.
Choose the trajectory branch up front: method='diffusion_map', method='slingshot', or method='palantir'.
Instantiate ov.single.TrajInfer(...) with explicit basis, use_rep, n_comps, n_neighbors, and groupby.
Call set_origin_cells(...) before every trajectory run; call set_terminal_cells(...) when the branch supports or benefits from constrained terminal states.
Run TrajInfer.inference(...).
For lineage topology, use the unified ov.pl.trajectory(...) / ov.pl.trajectory_overlay(...) plotters — they consume the trajectory state written into adata and share the same visual grammar across diffusion_map / slingshot / palantir / monocle / sctour / CellRank. ov.utils.cal_paga(...) + ov.utils.plot_paga(...) remain valid for explicit PAGA computation.
Summarize the branch structure with ov.pl.branch_streamplot(adata, group_key=..., pseudotime_key=...) — river-style plot driven only by a pseudotime vector and cluster labels, interoperable across methods.
Fit marker trends with the shared GAM stack: ov.single.dynamic_features(adata, genes=..., pseudotime=...) → ov.pl.dynamic_trends(res, ...) for per-gene curves and ov.pl.dynamic_heatmap(adata, var_names=..., pseudotime=...) for many-gene panels. The same backend is used by every trajectory skill, so output is interoperable.
If you chose palantir, optionally run palantir_cal_branch(...) and then palantir_cal_gene_trends(...) only when the extra dependencies and expression layer are available.
Validate the expected obs, obsm, varm, and uns keys before treating the result as finished.

Interface Summary

Trajectory fitting (TrajInfer)

ov.single.TrajInfer(adata, basis='X_umap', use_rep='X_pca', n_comps=50, n_neighbors=15, groupby='clusters') creates the shared trajectory wrapper.
TrajInfer.set_origin_cells(origin) stores the root label used by diffusion_map, slingshot, and palantir.
TrajInfer.set_terminal_cells(terminal) stores optional terminal labels for slingshot and palantir.
TrajInfer.inference(method='palantir'|'diffusion_map'|'slingshot', **kwargs) dispatches the main branch.
TrajInfer.palantir_cal_branch(q=..., eps=..., masks_key=...) stores branch masks after a Palantir run.
TrajInfer.palantir_cal_gene_trends(layers='MAGIC_imputed_data') computes lineage-wise trends after Palantir branch masks exist.
TrajInfer.palantir_plot_gene_trends(genes) and TrajInfer.palantir_plot_pseudotime(...) are optional visualization helpers after the corresponding Palantir outputs exist.

Unified ov.pl trajectory plotting (shared across methods — added by commit 4f28ab6)

ov.pl.trajectory(adata, *, method='diffusion'|'slingshot'|'palantir'|'monocle'|'sctour'|'cellrank', basis='X_umap', color=...) draws the trajectory backbone on the chosen embedding. Replaces method-specific plot calls.
ov.pl.trajectory_overlay(adata, *, ax, method=..., ...) overlays the trajectory backbone on an existing axis already populated by ov.pl.embedding(...). Use when you want a custom-styled UMAP with the PAGA / curve overlay on top.
ov.pl.branch_streamplot(adata, *, group_key, pseudotime_key, trunk_groups=None, branch_center=0.5, figsize=..., show=False) — river-style per-cluster pseudotime view. Works with any pseudotime key (dpt_pseudotime, palantir_pseudotime, slingshot_pseudotime, sctour_pseudotime, velocity_pseudotime, etc.).
ov.pl.dynamic_heatmap(adata, *, pseudotime, var_names, cell_annotation=None, use_fitted=True, cell_bins=200, ...) — GAM-fitted heatmap of many genes along pseudotime; var_names accepts a list or {program_name: [genes]} dict for multi-module panels.
ov.utils.cal_paga(adata, groups=..., vkey='paga', use_time_prior=..., minimum_spanning_tree=True) still computes an explicit PAGA topology summary; ov.utils.plot_paga(...) is still valid but the ov.pl.trajectory* family is the canonical entrypoint in the current tutorials.

Marker-dynamics GAM stack (shared backend across all trajectory skills)

ov.single.dynamic_features(adata, genes, pseudotime, *, layer=None, groupby=None, groups=None, n_splines=8, store_raw=True, raw_obs_keys=..., key_added='dynamic_features') fits per-gene GAM curves. Pass groupby + groups for branch-aware fits.
ov.pl.dynamic_trends(res, *, genes, compare_features=False, compare_groups=False, split_time=None, shared_trunk=True, add_point=True, point_color_by=..., line_style_by=..., ...) plots single-line global trends, multi-marker overlays, or branch-aware comparisons.

Read references/source-grounding.md before adding more interface-specific details.

Boundary

Keep this skill on the trajectory-analysis side of the notebook.
Do not absorb QC, HVG selection, PCA building, clustering, or general embedding generation here; those are already covered by the preprocessing skill.
Keep diffusion_map, slingshot, palantir, PAGA, and Palantir follow-ups in one skill because they share the same cluster-ready AnnData contract and the same TrajInfer entrypoint.
Split sctour into a separate skill because it changes the dependency and compute profile and is independently triggerable.

Branch Selection

Use method='diffusion_map' when you want DPT-style pseudotime from the same representation that drives the neighborhood graph.
Use method='slingshot' when you need lineage curves on a low-dimensional embedding and can satisfy its extra dependency.
Use method='palantir' when you need pseudotime plus entropy and fate probabilities, or when you plan to compute branch masks.
Use set_terminal_cells(...) for Palantir when you know the terminal states and want to constrain the fate labels; leave them unset only when you intentionally want automatic terminal-state discovery.
Run ov.utils.cal_paga(...) after you already have a pseudotime key such as dpt_pseudotime or palantir_pseudotime.
Run palantir_cal_branch(...) only after palantir_fate_probabilities exists.
Run palantir_cal_gene_trends(...) only after branch masks exist and you have a suitable expression layer.

Input Contract

Start from a cluster-ready AnnData.
Ensure groupby points to a valid categorical key in adata.obs.
Ensure use_rep points to a representation already stored in adata.obsm; the common portable path is X_pca.
Ensure basis points to a plotting embedding already stored in adata.obsm; the notebook path uses X_umap.
Ensure the origin label exists in adata.obs[groupby].
Ensure terminal labels exist in adata.obs[groupby] before calling set_terminal_cells(...).
Ensure a neighbor graph exists before ov.utils.cal_paga(...).
Ensure the expression layer used by palantir_cal_gene_trends(...) exists before calling it.

Minimal Execution Patterns

import omicverse as ov

traj = ov.single.TrajInfer(
    adata,
    basis="X_umap",
    use_rep="X_pca",
    n_comps=50,
    n_neighbors=15,
    groupby="clusters",
)
traj.set_origin_cells("Ductal")
traj.inference(method="diffusion_map")

# Unified trajectory plotting — canonical in the current tutorials
ov.pl.trajectory(adata, method="diffusion", basis="X_umap", color="clusters")

# Or overlay on an existing custom embedding
fig, ax = ov.plt.subplots(figsize=(4, 4))
ov.pl.embedding(adata, basis="X_umap", color="clusters", ax=ax, show=False)
ov.pl.trajectory_overlay(adata, ax=ax, method="diffusion")

# River-style branch view
ov.pl.branch_streamplot(
    adata, group_key="clusters", pseudotime_key="dpt_pseudotime", show=False,
)

# Marker trends along DPT pseudotime
res = ov.single.dynamic_features(
    adata, genes=["Pdx1", "Ins1", "Gcg"],
    pseudotime="dpt_pseudotime", store_raw=True, raw_obs_keys=["clusters"],
)
ov.pl.dynamic_trends(
    res, genes=["Pdx1", "Ins1", "Gcg"],
    add_point=True, point_color_by="clusters",
)

# Optional: explicit PAGA still supported
ov.utils.cal_paga(adata, groups="clusters", vkey="paga", use_time_prior="dpt_pseudotime")
ov.utils.plot_paga(adata, basis="umap", color="clusters", show=False)

import omicverse as ov

traj = ov.single.TrajInfer(
    adata,
    basis="X_umap",
    use_rep="X_pca",
    n_comps=50,
    n_neighbors=15,
    groupby="clusters",
)
traj.set_origin_cells("Ductal")
traj.set_terminal_cells(["Alpha", "Beta", "Delta", "Epsilon"])
traj.inference(method="palantir", num_waypoints=500)
traj.palantir_cal_branch(eps=0)

gene_trends = traj.palantir_cal_gene_trends(layers="MAGIC_imputed_data")
traj.palantir_plot_gene_trends(["Pax4", "Ins2"])

import matplotlib.pyplot as plt
import omicverse as ov

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))
traj = ov.single.TrajInfer(
    adata,
    basis="X_umap",
    use_rep="X_pca",
    groupby="clusters",
)
traj.set_origin_cells("Ductal")
traj.inference(method="slingshot", num_epochs=1, debug_axes=axes)

Constraints

Do not hardcode notebook-specific cell types, terminal labels, or marker genes as if they were universal defaults.
Do not rebuild preprocessing here when the real problem is that the input is not trajectory-ready; hand off to the preprocessing skill instead.
Set basis, use_rep, groupby, and method explicitly; do not rely on notebook-local state.
slingshot needs the extra pcurvepy2 dependency in the current OmicVerse implementation.
Palantir trend computation needs the extra mellon dependency.
The current Palantir wrapper does not expose a direct run_magic_imputation(..., n_jobs=...) override; keep local smoke-specific workarounds in the validation harness, not in the reusable workflow.
Keep notebook display-polish cells optional unless the user explicitly wants the same figure layout.

Validation

For diffusion_map, confirm adata.obs['dpt_pseudotime'] exists after inference.
For slingshot, confirm adata.obs['slingshot_pseudotime'] exists after inference.
For palantir, confirm adata.obs['palantir_pseudotime'], adata.obs['palantir_entropy'], and adata.obsm['palantir_fate_probabilities'] exist after inference.
After palantir_cal_branch(...), confirm adata.obsm['branch_masks'] exists.
After palantir_cal_gene_trends(...), confirm one or more palantir_gene_trends_* or gene_trends_* keys exist in adata.varm.
After ov.utils.cal_paga(...), confirm adata.uns['paga'] exists with connectivity and transition-confidence entries.
After ov.pl.trajectory(...) / ov.pl.trajectory_overlay(...), the figure should show the backbone connecting clusters in a topology consistent with the chosen method; an empty overlay means the trajectory state was not written into adata (re-run TrajInfer.inference(...) or ov.utils.cal_paga(...)).
After ov.single.dynamic_features(...), the returned result should have .failed (list of skipped genes with reasons) and a populated per-gene fit table; treat .failed non-empty as expected for low-variance / sparse genes.
If you only validated a bounded smoke path, say so; do not claim full notebook reproduction.

Resource Map

Read references/branch-selection.md when deciding whether the notebook change belongs in this skill or the separate sctour skill, and when choosing a TrajInfer method branch.
Read references/source-notebook-map.md to see which notebook cells were reused, delegated to the preprocessing skill, or split into the separate sctour skill.
Read references/source-grounding.md for checked signatures, source-derived branch behavior, and dependency-sensitive follow-up steps.
Read references/compatibility.md for dependency caveats and current runtime limitations.
Run scripts/trajectory_smoke.py only for bounded local validation of representative branches; it is a smoke utility, not the reusable workflow contract.