Whole-dataset CCI benchmarking#

Overview#

This tutorial summarizes whole-dataset cell-cell interaction benchmarking on the full Atera Xenium WTA breast sample (170,057 cells). The clean PDC full_common runs used a shared human cell-cell interaction resource so that methods are compared by recovered biology and method-internal rank behavior, not by raw score magnitude.

The benchmark is separate from the basic cell-cell interaction tutorial, which focuses on the fixed smoke/topology panel and the pyXenium.cci workflow.

Completed full common-db methods#

Method

Full common-db rows

Highest-level signal recovered

pyXenium

1,319,600

Vascular and topology-supported stromal axes, led by VWF-SELP, VWF-LRP1, and endothelial-to-CAF/pericyte programs.

CellPhoneDB

1,183,456

Reproducible non-spatial expression baseline, led by CCN2-ITGB2, VWF-LRP1, and CAF/endothelial/immune expression programs.

LARIS

1,304,935

Diffusion-smoothed tumor-stroma signals, led by PLAT-LRP1 and GNAS-ADCY1.

LIANA+

744,209

Spatial bivariate signals including CXCL12-CD4, HMGB1-THBD, and ICOSLG-CTLA4; top hits require caution because several involve Unassigned cells.

SpatialDM

446,023

Spatial co-expression signals dominated by tumor-intrinsic epithelial interactions such as CDH1-IGF1R.

stLearn

505,281

Local neighborhood CCI signals dominated by tumor-intrinsic high-expression interactions such as ADAM17-MUC1.

Canonical Atera axis recovery#

Canonical Atera axis

Benchmark recovery

CXCL12-CXCR4 CAF/DCIS to T cells

Strongly recovered. pyXenium ranked the expected CAFs, DCIS Associated -> T Lymphocytes interaction at rank 28, with CellPhoneDB and LARIS also recovering the same sender-receiver axis near the top of their full results.

DLL4-NOTCH3 endothelial to pericytes

Strongly recovered by pyXenium at rank 24 with the expected Endothelial Cells -> Pericytes direction, and also recovered by CellPhoneDB and LARIS.

JAG1-NOTCH1 tumor/stromal Notch

Recovered by multiple methods, but with method-dependent receiver compartments. pyXenium prioritized a tumor/DCIS axis, while CellPhoneDB and LARIS favored tumor-to-endothelial interpretations.

CSF1-CSF1R stromal to macrophages

Not recovered in the clean full common-db outputs; this should be interpreted as a database/expression/filtering limitation rather than proof that the macrophage axis is absent.

TGFB1-TGFBR2 endothelial/stromal TGF-beta

Not recovered in the clean full common-db outputs, again suggesting panel detectability or common-resource limitations.

Biological interpretation#

Overall, pyXenium gave the strongest topology-supported biological discovery profile because it recovered the expected CXCL12-CXCR4 and DLL4-NOTCH3 axes with the most anatomically plausible sender-receiver assignments. CellPhoneDB is the most useful reproducible non-spatial baseline, and LARIS is a strong diffusion-aware complement.

SpatialDM and stLearn are best read as supplementary spatial co-expression methods in this dataset because their top ranks are dominated by tumor-intrinsic high-expression programs. LIANA+ produced biologically interesting spatial bivariate hits, but the strongest calls require caution because several involve the Unassigned compartment.

Caveats#

  • Scores are standardized within each method; raw scores are not directly comparable across methods.

  • This page reports clean PDC full_common outputs only, not stale A100 salvage runs or smoke-only results.

  • Non-recovery of a canonical axis in the common-db benchmark can reflect CCI resource coverage, ligand-receptor-resource filtering, or panel detectability rather than biological absence.

Next steps#

  • Use pyXenium when topology-supported biological discovery is the priority.

  • Use CellPhoneDB as the reproducible non-spatial expression baseline.

  • Use LARIS, SpatialDM, stLearn, and LIANA+ as complementary views whose discoveries should be interpreted through their method-specific assumptions.

  • Extend the clean benchmark to the registered Atera cervical Xenium WTA dataset (atera_cervical_wta) and one public non-Xenium spatial dataset before using these results as manuscript-level cross-dataset evidence.

  • For reviewer-facing TopoLink-CCI results, report CCI_score as the discovery score and use cci_pvalue, cci_fdr, spatial nulls, matched-gene controls, downstream support, and bootstrap stability as orthogonal validation evidence.