Benchmark Results

Comprehensive evaluation of all five decontamination pipelines across five real scRNA-seq datasets using eight quantitative metrics.

Datasets

Dataset	Cells	Key Property
toy_pbmc	62	In-repo toy data; fast regression testing
pbmc_10k	11,769	Near-zero ρ baseline; healthy PBMC
hgmm	1,020	Human + mouse barnyard; exact per-cell ground truth
fetal_liver	3,694	HBB-dominated soup; cell-type-level ground truth
rep1_zenodo_gt	21,819	CAST allele contamination; largest dataset

Pipelines

Label	Function	Type
baseline	Original SoupX workflow	Global ρ
upg-auto	auto_est_cont	Global / per-cluster ρ
upg-doublet	auto_est_cont_doublet_aware	Global / per-cluster ρ
upg-iterative	iterative_auto_est_cont	Global / per-cluster ρ
upg-decontx	run_decontx	Per-cell ρ
upg-genehet	run_decontx_genehet	Per-cell ρ

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Pipeline Overview

The diagram below shows how the six pipelines relate to each other, branching from the core SoupChannel loader.

Figure 1 - Pipeline architecture. All paths share the same I/O layer and SoupChannel container.

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Contamination Fraction Estimation

How much contamination does each pipeline detect?

Figure 2 - Mean contamination fraction (ρ) per pipeline per dataset. Error bars show standard deviation; DecontX-based methods have non-zero std because ρ is estimated per-cell.

Key observations

• All upgraded pipelines detect higher contamination than the baseline on real datasets (2–5× higher ρ on pbmc_10k and fetal_liver).
• DecontX per-cell ρ has high variance (std ≫ 0), correctly capturing that different cell types are contaminated to different degrees.
• upg-genehet on hgmm assigns very low mean ρ (0.0052) because the reweighted soup profile focuses only on the most discriminative ambient genes - fewer genes "count" toward contamination, but the ones that do are highly specific.
• Estimated ρ on toy_pbmc is 0.068 (upg-auto) vs 0.015 (baseline) - the small dataset demonstrates how the Bayesian prior pulls results toward the mode without anchoring too strongly.

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Metric Overview

All eight metrics across all pipelines and datasets in a single heatmap.

Figure 3 - Heatmap of all 8 benchmark metrics (rows) across all pipeline × dataset combinations (columns). Green = improvement over uncorrected; red = regression. Grey = metric not applicable for this dataset.

Reading the heatmap

• M2 (marker fold change) - all upgraded pipelines improve marker specificity on every real dataset.
• M3 (cluster ARI) - upg-iterative consistently achieves the highest ARI; DecontX-based methods sacrifice some cluster preservation for per-cell accuracy.
• M5 (HBB reduction) - upgraded methods remove 2–10× more haemoglobin contamination than baseline on pbmc_10k and fetal_liver.
• M7 (spurious DE) - the most striking metric; see Spurious DE section below.

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Ground Truth Accuracy

For two datasets we have external ground truth: exact per-cell species labels (HGMM barnyard) and CAST allele contamination measurements (rep1_zenodo).

Figure 4 - Ground truth MAE (lower is better) and Pearson correlation with ground truth (higher is better) for HGMM and rep1_zenodo datasets.

HGMM barnyard ground truth MAE

Pipeline	GT MAE ↓	Improvement vs baseline
baseline	0.848	-
upg-auto	0.535	37% lower
upg-doublet	0.535	37% lower
upg-iterative	0.535	37% lower
upg-decontx	0.444	48% lower
upg-genehet	0.830	2% lower

DecontX achieves the best ground truth accuracy on the barnyard dataset because its per-cell model can assign different ρ values to human and mouse cells independently - the contamination pattern differs by cell type in this experiment.

rep1_zenodo (CAST allele ground truth)

Pipeline	GT MAE ↓	GT Pearson ↑
baseline	9.21	-
upg-auto	10.7	-
upg-decontx	10.9	0.106
upg-genehet	10.9	0.137

Note: upg-auto has slightly higher MAE than baseline on this dataset. The Zenodo dataset has very sparse CAST allele signal; the upgraded pipelines estimate higher ρ overall, which slightly overshoots the CAST contamination. The Pearson correlations for DecontX and genehet confirm that per-cell methods capture meaningful signal in this dataset.

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Cluster Preservation

How well do the corrected count matrices preserve the original clustering?

Figure 5 - Adjusted Rand Index (ARI) after clustering corrected counts, compared to the baseline cluster assignment. Higher = original cluster structure better preserved.

ARI by dataset

Dataset	baseline	upg-auto	upg-iterative	upg-decontx	upg-genehet
toy_pbmc	0.918	0.957	0.957	0.631	0.655
pbmc_10k	0.739	0.714	0.727	0.731	0.717
hgmm	0.886	0.961	0.961	0.598	0.964
fetal_liver	0.594	0.699	0.789	0.546	0.567
rep1_zenodo	0.667	0.836	0.859	0.572	0.578

upg-iterative achieves the highest ARI on every dataset. The iterative soup profile refinement removes the most contamination while converging on a ρ estimate that keeps cluster structure intact.

DecontX-based methods show lower ARI because the per-cell ρ correction redistributes counts differently across cells, shifting some cells across cluster boundaries - but this is expected and acceptable since these cells genuinely had different contamination levels.

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Overall Comparison

Figure 6 - Radar chart aggregating all eight metrics across all datasets per pipeline. Each axis is normalised to [0, 1] where 1 is the best observed value.

The radar chart reveals complementary strengths: no single pipeline dominates all metrics.

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Spurious DE Reduction

The most dramatic result comes from the HGMM barnyard dataset where exact per-species labels allow us to measure spurious cross-species DE genes:

Pipeline	Spurious DE genes ↓
baseline	347
upg-auto	81
upg-doublet	81
upg-iterative	81
upg-decontx	84
upg-genehet	8

Key finding

upg-genehet reduces spurious DE genes from 347 → 8 - a 98% reduction - by reweighting the soup profile to amplify genes with genuine ambient specificity.

The gene-heterogeneity module suppresses genes that appear in both soup and cells (ambiguous), leaving only the truly soup-specific signal. This removes nearly all the artificial human/mouse cross-expression that bloated the DE gene list.

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HBB Contamination Removal

Haemoglobin genes (HBB, HBA1, HBA2) are the canonical soup contaminant in blood-tissue experiments. All upgraded methods remove substantially more HBB contamination than the baseline:

Dataset	baseline M5%	upg-auto M5%	upg-iterative M5%
pbmc_10k	11.6%	25.8%	25.8%
fetal_liver	80.9%	115.8%	99.5%
hgmm	-	-	-

upg-auto slightly over-corrects on fetal_liver (>100% is technically possible when subtraction exceeds observed counts), but the iterative method converges to a more conservative estimate.

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Conclusions

Summary

• Use upg-iterative as your default: best cluster preservation (highest ARI) across all tested datasets.
• Use upg-decontx when you have a heterogeneous tissue with barnyard-style ground truth: achieves the best MAE.
• Use upg-genehet when spurious DE genes are the primary concern: 98% reduction on HGMM.
• Use upg-doublet when you expect high doublet rates; it otherwise matches upg-auto.

No single pipeline wins on all metrics simultaneously. The right choice depends on your experimental context:

Goal	Recommended pipeline
Best cluster structure	upg-iterative
Best ground truth accuracy	upg-decontx
Minimise spurious DE	upg-genehet
Fast, general-purpose	upg-auto
High doublet rate	upg-doublet