Bimodal Score Diagnosis

Bimodal Matching Score Diagnosis

Experiment Overview

Symptom

After CCA refinement, matching scores show two distinct modes:

• Good mode: mean ≈ 0.2-0.3 (45% of matches)
• Bad mode: mean ≈ 0.8 (55% of matches)

GMM analysis confirms bimodality with clear separation at threshold ≈ 0.5.

Diagnostic Process

Step 1: Check Tissue Cross-Tabulation

# Get tissue labels for matched cells
rna_tissues = rna_adata.obs['Tissue'].values[rna_indices[ref_rows]]
prot_tissues = protein_adata.obs['Tissue'].values[prot_indices[ref_cols]]

print(pd.crosstab(rna_tissues, prot_tissues, margins=True))

Finding: pLN RNA cells (10k) forced to match Pancreas protein (476k) due to imbalance.

Step 2: Check Expression Levels by Mode

low_mode = ref_scores < 0.5
high_mode = ref_scores >= 0.5

print(f"Low mode: mean protein expr = {prot_total_expr[low_mode].mean():.2f}")
print(f"High mode: mean protein expr = {prot_total_expr[high_mode].mean():.2f}")

Finding: Bad mode has near-zero or negative mean expression → non-immune cells.

Step 3: Check if CCA Creates Bimodality

from scipy.stats import kurtosis

init_kurtosis = kurtosis(init_scores)   # Before CCA
ref_kurtosis = kurtosis(ref_scores)     # After CCA

# Negative kurtosis = bimodal
# If ref_kurtosis << init_kurtosis, CCA increased bimodality

Finding: CCA refinement INCREASED bimodality (kurtosis went from 0.87 to -1.78).

Root Causes Identified

Solutions Implemented

1. Increase Region Prior Weight

fusor.set_region_priors(
    rna_labels=rna_tissue,
    spatial_regions=protein_tissue,
    celltype_to_region_weights=tissue_weights,
    wt_on_prior=0.7,  # Increased from 0.3
    verbose=True
)

2. Pre-filter Non-Immune Cells

# Calculate immune score
immune_markers = ['CD3E', 'MS4A1', 'CD68', 'PTPRC']
marker_idx = [marker_names.index(m) for m in immune_markers]
immune_score = protein_shared[:, marker_idx].sum(axis=1)

# Use pLN 25th percentile as threshold
threshold = np.percentile(immune_score[is_pln], 25)
keep_mask = (is_pln) | (immune_score > threshold)

protein_shared = protein_shared[keep_mask]
protein_active = protein_active[keep_mask]
protein_adata = protein_adata[keep_mask].copy()

3. Marker-Specific Z-Score Filtering

# Filter cells where specific markers have extreme values
MARKER_ZSCORE_THRESHOLDS = {
    'MPO': -2.5,  # Remove cells where MPO z-score < -2.5
}

for marker, threshold in MARKER_ZSCORE_THRESHOLDS.items():
    marker_idx = marker_names.index(marker)
    keep_mask &= protein_shared[:, marker_idx] >= threshold

Critical: Cell Execution Order

After pre-filtering, ALL subsequent cells must be re-run:

1. Fusor creation
2. Batching parameters
3. Tissue priors
4. Graph construction
5. Initial pivots, refinement, etc.

Add verification to Fusor creation:

if protein_shared.shape[0] > 400000:
    print("WARNING: Filter may not have run!")

Failed Attempts

Key Insights

1. Bimodality is diagnostic: Two score modes usually indicate systematic data issues, not random noise.

2. CCA amplifies, doesn't create: If initial scores are unimodal but refined are bimodal, CCA is correctly separating matchable from unmatchable pairs.

3. Filter BEFORE Fusor: Pre-filtering must happen before Fusor creation. Arrays must be reduced in size.

4. GMM threshold is useful: The threshold between GMM modes (≈0.5) is a good cutoff for filtering bad matches if pre-filtering isn't sufficient.

5. Check tissue cross-tabulation first: This is the fastest diagnostic - often reveals the root cause immediately.

References

• Integration notebook: notebooks/2_integration.ipynb
• Pre-filter cell: # PRE-FILTER: Remove non-immune Pancreas protein cells
• Diagnostic cells: After GMM analysis cell