Omicverse Single Cell Monocle2 Trajectory

OmicVerse Single-Cell — Monocle2 Trajectory

Goal

Take a preprocessed annotated single-cell AnnData (typically myeloid / hematopoietic lineage) and fit a Monocle2-style trajectory: DDRTree-based ordering, pseudotime computation, branch-point detection, per-gene differential testing along pseudotime, and BEAM (Branch Expression Analysis Modeling) for branch-dependent genes. Visualise the result with the OmicVerse plotting stack (branch_streamplot, dynamic_heatmap, dynamic_trends) on top of the unified dynamic_features GAM-fitting backend.

Monocle here is a wrapper around the original Monocle2 logic re-implemented in Python; it operates on AnnData rather than cell_data_set and integrates with the rest of OmicVerse plotting / trajectory tooling. dynamic_features is the shared GAM backend used by every trajectory skill (Palantir, Slingshot, Monocle, etc.) so genes-along-pseudotime visualisations are interoperable.

Quick Workflow

1. Load a preprocessed AnnData with cell-type labels (obs[<celltype>]) and counts in .X. The Olsson 2016 hematopoietic dataset is the canonical demo.
2. Instantiate: mono = Monocle(adata). Stores a copy internally; mono.adata is the working object.
3. Preprocess + ordering genes: mono.preprocess() (variance-stabilises + filters) → mono.select_ordering_genes(max_genes=1000) (high-variance + DEG-driven gene selection); inspect with mono.plot_ordering_genes(figsize=(5, 4)). Result: mono.adata.var['use_for_ordering'] boolean mask.
4. Reduce dimension to DDRTree: mono.reduce_dimension(max_components=4, verbose=False) builds the DDRTree embedding into adata.obsm['X_DDRTree'].
5. Order cells / pick root: mono.order_cells(root_by_column='subtype', root_by_value='Lsk') picks the trajectory root and writes obs['Pseudotime'] + obs['State']. The combined reduce_dimension + order_cells pair replaces the older one-shot fit_trajectory / auto-fit-on-first-plot flow.
6. Plot: use the unified trajectory plotters with method='monocle':
   • ov.pl.trajectory(mono.adata, method='monocle', basis='X_DDRTree', color='subtype') — DDRTree backbone scatter (replaces mono.plot_trajectory(...)).
   • ov.pl.trajectory_overlay(mono.adata, ax=ax, method='monocle') — overlay the backbone on an existing ov.pl.embedding(..., basis='X_DDRTree') axis.
   • ov.pl.trajectory_tree(mono.adata, method='monocle', color='subtype') — abstract tree layout (clean topology view, useful when the DDRTree-2D scatter is too dense).
7. Branch streamplot (alternative pseudotime view): ov.pl.branch_streamplot(mono.adata, group_key=<celltype>, pseudotime_key='Pseudotime') — colour-coded streamlines along pseudotime.
8. Differential gene test along pseudotime: re-instantiate on the ordering-gene subset for speed: mono_ord = Monocle(mono.adata[:, ordering_genes].copy()) then de = mono_ord.differential_gene_test(cores=-1). Filter de[(de['qval']<0.01) & (de['status']=='OK')] for significant trends.
9. Heatmap of top trending genes: ov.pl.dynamic_heatmap(mono.adata, pseudotime='Pseudotime', var_names=top40, cell_annotation='State', use_fitted=True, cell_bins=200, ...).
10. Per-gene trends: res = ov.single.dynamic_features(mono.adata, genes=marker_genes, pseudotime='Pseudotime', store_raw=True, raw_obs_keys=['State']) → ov.pl.dynamic_trends(res, genes=marker_genes, add_point=True, point_color_by='State', ...).
11. Branch-aware trends (compare two trajectories that share an early trunk): pass groupby=<celltype>, groups=['Gmp', 'LK'] to dynamic_features, then plot with compare_groups=True, split_time=<trunk-end-time>, shared_trunk=True.
12. BEAM branch-dependent genes: mono_ord.BEAM(branch_point=1, cores=-1) → DataFrame with pval, qval per gene; combine with dynamic_features(groupby=..., groups=branch_subtypes) + dynamic_trends(compare_groups=True, ...) to visualise the branching pattern.

Interface Summary

ov.single.Monocle(adata: AnnData)
# Stores adata in `self.adata` (a copy). Subsequent methods mutate self.adata.

Class methods (chained or independent):

• mono.preprocess() — variance-stabilising normalisation; canonical Monocle2 prep.
• mono.select_ordering_genes(max_genes=1000) — populates var['use_for_ordering']. The DEG-driven side combines with high-variance genes.
• mono.plot_ordering_genes(figsize=...) — diagnostic plot of the ordering-gene selection.
• mono.reduce_dimension(max_components=4, verbose=False) — builds DDRTree embedding into adata.obsm['X_DDRTree'] and stores the principal-graph state used by the unified trajectory plotters.
• mono.order_cells(root_by_column='subtype', root_by_value='Lsk') — picks the trajectory root and writes obs['Pseudotime'] + obs['State']. Together with reduce_dimension(...) this replaces the older fit_trajectory(...) flow; fit_trajectory may still be available as a backwards-compatible alias.
• mono.differential_gene_test(cores=-1, ...) → pd.DataFrame — per-gene VGAM-style tests for variation along pseudotime; cores=-1 uses all cores. Output columns: pval, qval, status ('OK' / 'FAIL'), gene_id.
• mono.BEAM(branch_point=1, cores=-1, ...) → pd.DataFrame — Branch Expression Analysis Modeling: per-gene tests for branch-dependent expression at the named branch point. Same output schema as differential_gene_test.

Unified ov.pl trajectory plotters (replace the legacy mono.plot_trajectory(...) method; shared with the other trajectory skills):

• ov.pl.trajectory(mono.adata, method='monocle', basis='X_DDRTree', color=...) — DDRTree-2D scatter with the principal-graph backbone overlayed.
• ov.pl.trajectory_overlay(mono.adata, ax=ax, method='monocle') — overlay backbone on an existing ov.pl.embedding(..., basis='X_DDRTree') axis.
• ov.pl.trajectory_tree(mono.adata, method='monocle', color=...) — abstract tree layout of the principal graph (useful when the DDRTree-2D scatter is too dense).

Module-level (not method on Monocle):

• ov.single.dynamic_features(adata|dict[name→adata], genes, pseudotime='pseudotime', *, groupby=None, groups=None, layer=None, use_raw=False, subsets=None, weights=None, distribution='normal', link='identity', n_splines=8, spline_order=3, grid_size=200, confidence_level=0.95, min_cells=20, min_variance=1e-08, store_raw=False, raw_obs_keys=None, key_added='dynamic_features', verbose=True) → DynamicFeaturesResult. Generic GAM backend usable across any pseudotime field.
• ov.pl.branch_streamplot(adata, *, group_key, pseudotime_key, show=True, ...).
• ov.pl.dynamic_heatmap(adata, *, pseudotime, var_names, cell_annotation=None, use_cell_columns=False, use_fitted=True, cell_bins=200, smooth_window=..., fitted_window=..., figsize=..., show_row_names=True, standard_scale='var', cmap='RdBu_r', order_by='peak', show=True, ...).
• ov.pl.dynamic_trends(res, *, genes, add_point=True, point_color_by=None, line_style_by=None, compare_features=False, compare_groups=False, split_time=None, shared_trunk=True, figsize=..., legend_loc=..., ncols=..., title=..., ...).

Boundary

Inside scope:

• Full Monocle2 workflow on a preprocessed annotated AnnData.
• DEG along pseudotime (differential_gene_test).
• BEAM branch-dependent gene discovery.
• Pseudotime-along-genes visualisation (dynamic_features + dynamic_trends + dynamic_heatmap).
• Branch streamplot (ov.pl.branch_streamplot).

Outside scope — separate skill:

• Diffusion / Slingshot / Palantir trajectory inference — see omicverse-single-cell-trajectory-inference (TrajInfer wrapper).
• VIA trajectory inference (with or without RNA velocity) — see omicverse-single-cell-via-trajectory.
• scTour pseudotime — see omicverse-single-cell-sctour-trajectory (separate existing skill).
• CellRank-style fate maps from RNA velocity — see omicverse-single-cell-cellrank-fate.
• Trajectory inference on multi-modal (e.g. ATAC) data — out of scope; use dynamic_features(layer='atac') only after fitting a separate trajectory.
• Preprocessing / clustering / annotation — separate existing skills.

Branch Selection

max_genes for select_ordering_genes

• 1000 (tutorial default) — good balance of biological coverage and DDRTree compute time on cohorts of 1k–10k cells.
• 500 — for small cohorts (<2k cells); too many ordering genes cause DDRTree to over-branch.
• 2000 — for large cohorts (>20k cells) with rich biology; check that differential_gene_test runtime stays acceptable.

differential_gene_test cores

• cores=-1 uses all cores. Per-gene independent tests parallelise well; on 1000 genes × 8 cores expect ~30 s.
• Limit to physical cores (not hyperthreaded) for best throughput.

BEAM branch_point

• branch_point=1 — first branch in the DDRTree (tutorial default for the Olsson hematopoietic dataset). Inspect mono.adata.uns or the ov.pl.trajectory(..., method='monocle') / ov.pl.trajectory_tree(...) figure to identify the right branch number.
• For multi-branch trajectories, run BEAM per branch point separately and concatenate; never run on branch_point=0 (the trunk) — that's not a branch.

dynamic_features distribution / link

• 'normal' / 'identity' (default) — the right choice for log-normalised expression (post sc.pp.log1p). Tutorial default.
• 'gamma' / 'log' — for raw counts directly; rarely needed since most pipelines log-normalise first.
• 'binomial' / 'logit' — only for binary on/off detection traces, not log-CPM expression.

dynamic_features n_splines / spline_order

• 8 splines, order 3 (cubic) — tutorial default; smooth without over-fitting.
• Increase to 12–16 splines for traces with multiple inflection points (e.g. transient peaks).
• Decrease to 4–6 for cohorts with sparse pseudotime coverage.

dynamic_trends modes

• Default: one panel per gene, with cells coloured by point_color_by.
• compare_features=True — overlay multiple gene curves on the same axis; line_style_by='features' differentiates them. Useful for showing ordered marker progression.
• compare_groups=True — branch-aware: requires groupby='subtype' (or similar) at dynamic_features time and groups=[branch_a, branch_b]. Pass split_time=<trunk-to-branch transition> and shared_trunk=True to draw a single trunk that splits.

dynamic_heatmap use_fitted=True vs raw smoothing

• use_fitted=True — uses GAM-fitted curves (smoothest; cleanest for publication).
• use_fitted=False — uses raw smoothed bins; better for spotting outlier cells.
• cell_bins=200 is default; reduce to 50–100 for small cohorts (<1k cells).

Subsetting before differential_gene_test

• Re-instantiate Monocle(mono.adata[:, ordering_genes].copy()) before the test — the test runs over all genes by default, and on a 20k-gene matrix with 8 cores it's ~minutes. Subsetting to ~1000 ordering genes drops it to <1 minute.

Input Contract

• AnnData with raw counts (or normalised log-counts) in .X. obs should carry the cell-type column you'll pass to branch_streamplot (group_key) and trend plots (point_color_by).
• For Olsson-style data: obs includes a subtype column ('Gmp', 'LK', 'Cmp', etc.); the WT subset is the canonical demo (adata_wt = adata[adata.obs['genotype'] == 'WT']).
• var has gene symbols (or IDs); differential_gene_test indexes by var_names.
• obs['Pseudotime'] and obs['State'] are populated by order_cells(...) (or the legacy fit_trajectory(...) alias). Don't pass these in; they'll be overwritten.

Minimal Execution Patterns

import anndata as ad
import omicverse as ov
from omicverse.single import Monocle
import matplotlib.pyplot as plt

ov.plot_set(font_path='Arial')

# 1) Load preprocessed annotated AnnData (Olsson WT subset)
adata_wt = ad.read_h5ad('olsson_wt.h5ad')

# 2) Fit Monocle2
mono = Monocle(adata_wt)
mono.preprocess()
mono.select_ordering_genes(max_genes=1000)
mono.plot_ordering_genes(figsize=(5, 4)); plt.show()
mono.reduce_dimension(max_components=4, verbose=False)
mono.order_cells(root_by_column='subtype', root_by_value='Lsk')

# 3) Plot trajectory — unified ov.pl interface (method='monocle')
ov.pl.trajectory(mono.adata, method='monocle', basis='X_DDRTree', color='subtype')
ov.pl.trajectory(mono.adata, method='monocle', basis='X_DDRTree', color='State')

fig, ax = plt.subplots(figsize=(4, 4))
ov.pl.embedding(mono.adata, basis='X_DDRTree', color='subtype',
                ax=ax, show=False, size=50)
ov.pl.trajectory_overlay(mono.adata, ax=ax, method='monocle')

ov.pl.trajectory_tree(mono.adata, method='monocle', color='subtype')

# 4) Branch streamplot view
fig, ax = ov.pl.branch_streamplot(
    mono.adata, group_key='subtype', pseudotime_key='Pseudotime', show=False,
); plt.show()

# 5) DEG along pseudotime — re-instantiate on ordering subset for speed
ordering = mono.adata.var_names[mono.adata.var['use_for_ordering']].tolist()
mono_ord = Monocle(mono.adata[:, ordering].copy())
de = mono_ord.differential_gene_test(cores=-1)
sig = de[(de['qval'] < 0.01) & (de['status'] == 'OK')]
top40 = sig.sort_values('pval').head(40).index.tolist()

# 6) Heatmap of top trending genes (GAM-fitted)
g = ov.pl.dynamic_heatmap(
    mono.adata,
    pseudotime='Pseudotime',
    var_names=top40,
    cell_annotation='State',
    use_fitted=True,
    cell_bins=200,
    figsize=(7, 7),
    show_row_names=True,
    standard_scale='var', cmap='RdBu_r', order_by='peak',
    show=False,
); plt.show()

# 7) Per-gene trends with cell points
markers = [g for g in ['Gfi1', 'Irf8', 'Elane', 'Prtn3', 'Mpo', 'Car2']
           if g in mono.adata.var_names]
res = ov.single.dynamic_features(
    mono.adata, genes=markers, pseudotime='Pseudotime',
    store_raw=True, raw_obs_keys=['State'],
)
ov.pl.dynamic_trends(
    res, genes=markers,
    add_point=True, point_color_by='State',
    figsize=(4, 3.5), legend_loc='right margin', legend_fontsize=8,
); plt.show()

# Branch-aware trends — compare two branches that share an early trunk
import numpy as np

branch_subtypes = ['Gmp', 'LK']
res_branch = ov.single.dynamic_features(
    mono.adata,
    genes=['Gfi1', 'Irf8', 'Elane', 'Car2'],
    pseudotime='Pseudotime',
    groupby='subtype',
    groups=branch_subtypes,
    store_raw=True,
)
trunk_mask = mono.adata.obs['subtype'].astype(str).isin(['Cmp'])
split_time = float(np.nanmedian(mono.adata.obs.loc[trunk_mask, 'Pseudotime']))
ov.pl.dynamic_trends(
    res_branch, genes=['Gfi1', 'Irf8', 'Elane', 'Car2'],
    compare_groups=True,
    split_time=split_time, shared_trunk=True,
    add_point=True, point_color_by='group',
    figsize=(4.6, 3), ncols=2,
)

# BEAM at branch point 1
beam = mono_ord.BEAM(branch_point=1, cores=-1)
sig_beam = beam[beam['qval'] < 0.01].sort_values('qval')
print(f'BEAM hits: {len(sig_beam)} / {len(beam)}')
top_branch = sig_beam.head(4).index.tolist()

Validation

• After select_ordering_genes: mono.adata.var['use_for_ordering'].sum() should be close to max_genes (slight under is OK if some genes failed variance filter; large under means filter was too aggressive).
• After reduce_dimension(...): mono.adata.obsm['X_DDRTree'] is populated.
• After order_cells(...) (or the legacy fit_trajectory(...)): mono.adata.obs['Pseudotime'] and mono.adata.obs['State'] are populated, no NaNs.
• differential_gene_test: (de['status'] == 'OK').sum() / len(de) should be > 0.9. Many 'FAIL' rows indicate genes with insufficient signal after filtering — usually fine, just lowers the universe of testable genes.
• (de['qval'] < 0.01).sum() typically yields 50–500 hits on Olsson-scale cohorts; far fewer indicates the trajectory is poorly defined.
• BEAM hits should be a subset of differential_gene_test hits (with the additional branch-dependence constraint). If BEAM returns more hits than DEG, something is mis-specified (likely the wrong branch_point).
• dynamic_features always returns a DynamicFeaturesResult even when some genes failed; the .failed attribute lists failures with reasons (insufficient cells, low variance, GAM fit issues).
• dynamic_heatmap over top40 genes: rows should show clear pseudotime gradients (the whole point of selecting on q<0.01); if the heatmap looks random, the trajectory or pseudotime is degenerate.

Resource Map

• See reference.md for compact copy-paste snippets.
• See references/source-grounding.md for verified Monocle / dynamic_features / branch_streamplot / dynamic_heatmap / dynamic_trends signatures.
• For other trajectory backends (diffusion / slingshot / palantir), see omicverse-single-cell-trajectory-inference; for VIA, see omicverse-single-cell-via-trajectory; for scTour, see omicverse-single-cell-sctour-trajectory.
• For RNA velocity-based fate maps, see omicverse-single-cell-cellrank-fate.

Examples

• "Fit Monocle2 on the Olsson WT subset, plot the DDRTree trajectory by State, and run differential_gene_test on the ordering-gene subset."
• "Run BEAM at branch point 1 and visualise the top-4 branch-dependent genes with compare_groups=True, shared_trunk=True."
• "Build a 40-gene dynamic heatmap of top pseudotime-significant genes ordered by peak, GAM-fitted."
• "Show per-gene trends for Gfi1 / Irf8 / Elane along Pseudotime with cell points coloured by State."

References

• Tutorial notebook: t_traj_monocle2.ipynb — Olsson 2016 hematopoietic walkthrough (renamed from t_traj_monocle2_olsson in commit 12ce9f4).
• Live API verified — see references/source-grounding.md.