spateo-morphogenesis

name	spateo-morphogenesis
description	Vector field inference, trajectory analysis, GLM differential expression

Stage 3: Morphogenesis

Infer cell state transitions and vector fields between developmental stages, compute trajectory paths, and identify differentially expressed genes.

Quick Start

# Vector field + cell mapping between two stages
python -m skills.04_morphogenesis.vectorfield \
  --stage1 ./data/stage1.h5ad --stage2 ./data/stage2.h5ad

# Feature extraction (velocity, acceleration, curvature) + GLM DEG
python -m skills.04_morphogenesis.feature \
  --input ./data/vectorfield.h5ad --out-dir ./output

Modules

vectorfield.py — Cell mapping & vector field

Computes cell state transitions between two time points using sparse VFC (Vector Field Corrector).

from skills.04_morphogenesis.vectorfield import (
    VectorFieldConfig, run_vectorfield_pipeline,
)

config = VectorFieldConfig(
    stage1_path="./data/stage1.h5ad",
    stage2_path="./data/stage2.h5ad",
    groupby="celltype",
    method="sparsevfc",
)
result = run_vectorfield_pipeline(config)

Key APIs: st.tools.morphofield_sparsevfc, st.tools.morphopath, cell mapping via optimal transport (POT).

feature.py — Feature extraction + GLM DEG

Extracts dynamic features from vector fields and identifies differentially expressed genes.

from skills.04_morphogenesis.feature import (
    FeatureConfig, run_feature_pipeline,
)

config = FeatureConfig(
    input_path="./data/vectorfield.h5ad",
    features=["velocity", "acceleration", "curvature", "curl", "torsion", "jacobian"],
    run_glm_deg=True,
    deg_groupby="celltype",
)

Features:

Feature	Description
velocity	Cell state change speed
acceleration	Rate of velocity change
curvature	Path bending
curl	Local rotation in vector field
torsion	3D twisting of paths
jacobian	Local divergence/convergence

Input/Output

Input (vectorfield): Two AnnData objects from consecutive time stages. Input (feature): AnnData with vector field computed. Output: AnnData with vector field layers, GLM DEG tables (CSV), trajectory paths.

Key Parameters

Parameter	Purpose	Default
stage1_path, stage2_path	Input stage paths	—
groupby	Cell grouping column	"celltype"
method	Vector field method	"sparsevfc"
features	Features to compute	All
run_glm_deg	Run GLM differential expression	True
deg_threshold	Significance threshold	0.05

Troubleshooting

Problem	Fix
POT import error	conda install -c conda-forge pot
Vector field diverges	Check stage1/stage2 have same gene set
GLM fails on sparse data	Filter low-expression genes first
CUDA OOM	Set device="cpu" in config