smith6jt-cop Cross-Modal Normalization - Research Notes
Experiment Overview
| Item | Details |
|---|---|
| Date | 2025-01-20 |
| Goal | Fix matching quality issues caused by scale mismatch between RNA and protein |
| Environment | Python 3.10, scanpy 1.9+, sklearn |
| Status | Success |
Context
MaxFuse matching quality was poor with cells matching across clusters and "B-cell outliers" that weren't actually B cells. Investigation revealed a 16x variance mismatch between modalities:
- RNA: normalize_total → log1p → z-score → mean≈0, std≈0.8, range [-1.5, 5]
- Protein: Used as-is ("pre-scaled") → mean≈0.4, std≈0.08, range [0, 1]
The matching algorithm over-weighted RNA features because they had 16x higher variance.
The Problem: Scale Mismatch
RNA variance: ~0.64 (std² = 0.8²)
Protein variance: ~0.006 (std² = 0.08²)
Ratio: ~100x (RNA dominates matching)
When computing distances for matching, features with higher variance dominate. If RNA has 100x the variance of protein, the matching effectively ignores protein information.
Verified Workflow
Correct Normalization (BOTH z-scored)
from sklearn.preprocessing import StandardScaler
from scipy import sparse
# ============================================================
# RNA NORMALIZATION (standard pipeline)
# ============================================================
sc.pp.normalize_total(rna_shared_adata, target_sum=1e4)
sc.pp.log1p(rna_shared_adata)
sc.pp.scale(rna_shared_adata, zero_center=True, max_value=5)
rna_shared_normalized = rna_shared_adata.X.copy()
if sparse.issparse(rna_shared_normalized):
rna_shared_normalized = rna_shared_normalized.toarray()
# ============================================================
# PROTEIN NORMALIZATION - MUST Z-SCORE TO MATCH RNA
# ============================================================
protein_shared_raw = protein_shared_adata.X.copy()
if sparse.issparse(protein_shared_raw):
protein_shared_raw = protein_shared_raw.toarray()
# Z-score protein to match RNA scale
scaler = StandardScaler()
protein_shared_normalized = scaler.fit_transform(protein_shared_raw)
protein_shared_normalized = np.clip(protein_shared_normalized, -5, 5)
# ============================================================
# VERIFICATION - CRITICAL
# ============================================================
print("RNA:")
print(f" Mean: {rna_shared_normalized.mean():.4f}")
print(f" Std: {rna_shared_normalized.std():.4f}")
print(f" Range: [{rna_shared_normalized.min():.2f}, {rna_shared_normalized.max():.2f}]")
print("Protein:")
print(f" Mean: {protein_shared_normalized.mean():.4f}")
print(f" Std: {protein_shared_normalized.std():.4f}")
print(f" Range: [{protein_shared_normalized.min():.2f}, {protein_shared_normalized.max():.2f}]")
# EXPECTED OUTPUT:
# RNA: Mean: ~0, Std: ~0.8-1.0, Range: [-1.5, 5.0]
# Protein: Mean: ~0, Std: ~0.8-1.0, Range: [-5.0, 5.0]
Failed Attempts (Critical)
| Attempt | Why it Failed | Lesson Learned |
|---|---|---|
| Protein "pre-scaled" used as-is | 16x variance mismatch → RNA dominated matching | ALWAYS z-score both modalities |
| Assuming gated data is normalized | Gating ≠ normalization, still needs z-score | Check actual statistics, don't assume |
| Only checking mean (not std) | Mean≈0 but std was 0.08 vs 0.8 | Always check BOTH mean AND std |
Key Insights
Both modalities MUST have similar scales: mean≈0, std≈1, range ~[-5, 5]
"Pre-scaled" or "normalized" doesn't mean z-scored: Always verify with actual statistics
Check the VERIFICATION output: The normalization cell should print statistics for both modalities. If they don't match, the integration will fail.
Symptoms of scale mismatch:
- Cells matching across different UMAP clusters
- "Outliers" that don't match expected cell types
- Very high canonical correlations (overfitting to dominant modality)
- Poor downstream validation metrics
Clip both to same range: Using
np.clip(..., -5, 5)ensures neither modality has extreme outliers that dominate matching.
Diagnostic Checks
Before Integration
# Both should be similar
for name, data in [("RNA", rna_shared), ("Protein", protein_shared)]:
print(f"{name}: mean={data.mean():.3f}, std={data.std():.3f}")
# WARNING signs:
# - std differs by >2x between modalities
# - mean is far from 0 for either modality
# - range is very different (e.g., [0,1] vs [-5,5])
After Integration
# Check if matched pairs make biological sense
# - Same cell type markers should be correlated
# - Cells should match within clusters, not across
# - Permutation test should show significance (p < 0.01)
References
- Integration notebook:
notebooks/2_integration.ipynb, cell starting with "# Normalize shared features" - MaxFuse matching uses correlation distance, which is scale-sensitive
- sklearn StandardScaler: per-feature z-scoring