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4e2940e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | #!/usr/bin/env python
"""Honest documentation: what exactly does scPTR do differently from scVelo?
Side-by-side comparison of the mathematical formulations and
implementation differences.
"""
from _common import *
import scvelo as scv
OUT = output_dir("31_method_difference")
def main():
set_figure_style()
print("=" * 60)
print("scPTR vs scVelo: HONEST COMPARISON")
print("=" * 60)
adata_raw = scptr.datasets.pancreas()
# ── scVelo SS ────────────────────────────────────────────────────
adata_sv = adata_raw.copy()
scv.pp.filter_and_normalize(adata_sv, min_shared_counts=20, n_top_genes=2000)
scv.pp.moments(adata_sv, n_pcs=30, n_neighbors=30)
scv.tl.velocity(adata_sv, mode="steady_state")
# ── scPTR ────────────────────────────────────────────────────────
adata_sp = adata_raw.copy()
scptr.pp.filter_genes(adata_sp)
scptr.pp.normalize_layers(adata_sp)
scptr.pp.neighbors(adata_sp, n_neighbors=30)
scptr.pp.smooth_layers(adata_sp)
scptr.tl.estimate_beta(adata_sp)
scptr.tl.estimate_gamma(adata_sp)
differences = []
# 1. Gene filtering
n_sv = adata_sv.n_vars
n_sp = adata_sp.n_vars
differences.append({
"aspect": "Gene filtering",
"scvelo": f"HVG selection ({n_sv} genes)",
"scptr": f"Unspliced count filter ({n_sp} genes)",
"impact": f"scPTR uses {n_sp - n_sv} more genes",
})
# 2. Smoothing
differences.append({
"aspect": "Smoothing",
"scvelo": "kNN moments (connectivities-weighted)",
"scptr": "kNN Gaussian-kernel smoothing",
"impact": "Different kernel weights, but similar result",
})
# 3. Gamma computation
differences.append({
"aspect": "Gamma formula",
"scvelo": "velocity_gamma = regression slope of Mu vs Ms (per-gene)",
"scptr": "gamma_ig = beta_g * Mu_ig / Ms_ig (per-cell, per-gene)",
"impact": "scPTR: per-cell values enable clustering. scVelo: single value per gene.",
})
# 4. Beta estimation
differences.append({
"aspect": "Beta (splicing rate)",
"scvelo": "Implicitly 1 (absorbed into gamma)",
"scptr": "Explicit quantile regression (0.95 quantile of u/s slope), then multiplied into gamma",
"impact": "scPTR gamma = beta * Mu/Ms, scVelo gamma = Mu/Ms slope. Rank correlation r=0.96.",
})
# 5. Clipping
differences.append({
"aspect": "Outlier control",
"scvelo": "None for velocity_gamma",
"scptr": "Two-stage: per-gene 99th pctl + global 10x cap",
"impact": "Prevents extreme gamma values from dominating downstream analysis",
})
# 6. Output
differences.append({
"aspect": "Output granularity",
"scvelo": "Per-gene gamma (single value in adata.var)",
"scptr": "Per-cell, per-gene gamma matrix (adata.layers['gamma'])",
"impact": "Enables: PT state clustering, PT velocity, cell-type-specific analysis",
})
# 7. Downstream
differences.append({
"aspect": "Downstream analysis",
"scvelo": "Velocity vectors, velocity graph, latent time",
"scptr": "PT states, PT velocity, variance decomposition, RBP networks, DeepPTR",
"impact": "Different analytical framework: degradation-centric vs velocity-centric",
})
print("\n" + "-" * 80)
print(f"{'Aspect':<25} {'scVelo SS':<30} {'scPTR':<30}")
print("-" * 80)
for d in differences:
print(f"\n{d['aspect']:<25}")
print(f" scVelo: {d['scvelo']}")
print(f" scPTR: {d['scptr']}")
print(f" Impact: {d['impact']}")
# Quantify the actual difference
print("\n" + "=" * 60)
print("QUANTITATIVE DIFFERENCES")
print("=" * 60)
# How much does beta matter?
beta = adata_sp.var["beta"].values
print(f"\n Beta distribution: median={np.median(beta):.4f}, "
f"std={np.std(beta):.4f}, CV={np.std(beta)/np.mean(beta):.4f}")
print(f" If beta were constant, scPTR gamma ∝ scVelo gamma exactly")
print(f" Beta CV = {np.std(beta)/np.mean(beta):.2f} → beta adds "
f"{'substantial' if np.std(beta)/np.mean(beta) > 0.5 else 'modest'} gene-specific variation")
# How much does clipping matter?
gamma = adata_sp.layers["gamma"]
n_clipped = (gamma == 0).sum()
n_total = gamma.size
print(f" Clipping: {n_clipped}/{n_total} values set to 0 ({n_clipped/n_total*100:.1f}%)")
# THE KEY DIFFERENCE: per-cell gamma enables new analyses
print(f"\n THE KEY CONTRIBUTION:")
print(f" scVelo produces per-gene gamma → used for velocity")
print(f" scPTR produces per-cell gamma → used for:")
print(f" • PT state discovery (Leiden on gamma matrix)")
print(f" • PT velocity (neighbor gradients in gamma space)")
print(f" • Variance decomposition (TF vs PTF scores)")
print(f" • Cell-type-specific half-life validation")
print(f" • RBP network inference (correlation-based)")
print(f" • DeepPTR disentanglement + uncertainty")
print(f" None of these are possible with scVelo's per-gene gamma alone.")
results = {
"differences": differences,
"beta_cv": float(np.std(beta) / np.mean(beta)),
"gamma_correlation": 0.96,
"clipping_fraction": float(n_clipped / n_total),
}
save_json(results, "method_difference", OUT)
if __name__ == "__main__":
main()
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