Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡The deviation was non-trivial in magnitude
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough to reproduce, and reproduced largely 1:1 using the authors' OWN R library + bundled BioGRID 3.5.166 graphml + bundled dapcstp solver (solved to proven optimality) + the shipped per-cluster all-cell FindAllMarkers DE p-values. This re-run («job») installed r-mass and used the authors' UNMODIFIED fitBUM (MASS::fitdistr) — no substitution. EXACT (10/15 pinned claims): PPIN size 17309 nodes / 296637 edges; both repo-documented tutorials (3-node {APP,SCD,ALDOB}; 16-node); the two low-DEG hepatocyte modules H4=3 and H6=1 (node identity PPP1R3C confirmed); the entire H6 FDR-sensitivity sweep (M=1 for FDR<=1e-26, M=9 at 1e-25, with EGFR/PPP1R3C); and dataset profiling (2597 cells, 2123 hepatocytes). PARTIAL (5/15): the Table-1 module sizes for the high-DEG hepatocyte clusters reproduce in correct rank order but are inflated ~12-85% (27->33, 30->35, 51->74, 52->96, 253->294). This gap is now well-characterized and three hypotheses tested: (1) wrong DE input REJECTED (the shipped 20190423 RDS is the exact, only input, confirmed from the liver Rmd: all-cell FindAllMarkers, hep clusters 0,1,2,3,5,6,7); (2) BUM-fit method REJECTED (authors' MASS::fitdistr gives IDENTICAL sizes to v1's optim() MLE, so v1 was faithful); (3) significant-only node counting REJECTED (sig counts 272/88/59/32 still exceed reported 253/52/51/30). Most likely residual cause, not resolvable from shipped artifacts: the published Table 1 stems from a slightly different Seurat clustering/p-value set than the deposited RDS (deposited ~1.5yr pre-publication) and/or a marginally different effective FDR; at the extreme FDR=1e-27 the score threshold tau is hypersensitive to small alpha (strong-signal clusters), exactly where the gap appears while low-signal clusters match. NO fabrication indicated: every reported value is derivable from shipped data/code; the high-DEG integers are simply not uniquely pinned by the deposited artifacts. NOT attempted: GO enrichment (Table 1, depends on module membership), the adipose secondary dataset (Suppl. 6), and a from-raw Seurat v2.3.4 re-clustering (stochastic; Seurat v2 uninstallable). Grades are provisional pending human audit.
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Assessment versions
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v1 current initial assessment Score 83assessed: 2026-06-18 ⛓ 76b9c17e675a
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-24
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-18no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetThe paper tests whether integrating single-cell RNA-seq differential expression data with protein-protein interaction networks (via the scPPIN method) can reveal active functional modules and biological pathways in different cell transcriptional states that would not be detectable from differential gene expression analysis alone.
- ★ scPPIN integrates scRNA-seq-derived p-values with PPINs to detect maximum-weight connected subgraphs (active/functional modules) via an exact Steiner-tree approach method
- ★ Different transcriptional cell states have different subnetworks of the PPIN significantly enriched, representing distinct biological pathways finding
- ★ scPPIN identifies proteins that are not differentially expressed but have crucial biological function (e.g., receptors like EGFR) as connectors within modules finding
- ★ Module size is non-decreasing with the false discovery rate (FDR) parameter, but detection is non-monotonous (proteins found at one FDR are not guaranteed at larger FDRs) finding
- ★ GO-term enrichment analysis shows detected modules correspond to distinct biological functions across hepatocyte clusters (e.g., translational initiation, stress response, extracellular structure organisation) finding
- APP (amyloid precursor protein) recurs as a connector protein across three different hepatocyte clusters, suggesting a role in multiple biological contexts finding
- The method is applicable to other organisms and tissues, demonstrated with a secondary case study on human adipose tissue resource
- The PPIN was constructed from publicly available BIOGRID data and clustering was performed using SEURAT with Wilcoxon rank sum test for DEGs method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA sequencing | human liver spheroids (hepatocytes) | none | clustering into transcriptional states, differentially expressed genes (p-values) | SEURAT (clustering) |
| differential gene expression analysis (Wilcoxon rank sum test) | human hepatocyte clusters (H1-H7) | none | p-values per gene per cluster used to compute node scores | — |
| protein-protein interaction network construction / functional module detection (scPPIN, Steiner-tree) | human hepatocyte clusters mapped onto PPIN | varying FDR parameter | maximum-weight connected subgraph size and composition | BIOGRID (PPIN source) |
| GO-term enrichment (Fisher's exact test) | detected functional modules per hepatocyte cluster | none | enriched GO terms, multiple-testing-corrected p-values, fold-enrichment | — |
| single-cell RNA sequencing | human adipose tissue | none | functional modules and enriched biological functions (Supplementary Results) | — |
| t-distributed stochastic neighbour embedding (t-SNE) | human liver scRNA-seq data (d=6983 genes) | none | two-dimensional visualisation of cell clusters | — |
- ▼ PPP1R3C has the smallest p-value (~10^-63) in hepatocyte cluster H6, serving as a potential biomarker p~10^-63
- – In H6, a 9-protein module is detected at FDR=10^-25, with APP (p~10^-7) and EGFR (p~10^-2) as non-DE connector proteins ranking 373 and 1800 among DEGs M=9
- ▲ Module size is M=1 (PPP1R3C only) for FDR<10^-26 and grows to M=42 nodes (9 not identifiable from expression data alone) at larger FDRs, including connectors ELAVL1, ECI2, S100A4 M=1 to M=42
- – At FDR=10^-27, detected module sizes vary across the seven hepatocyte clusters: H1=51, H2=52, H3=27, H4=3, H5=30, H6=1, H7=253 1-253 nodes
- – Five of seven modules (all but the two smallest) show significant GO term enrichment; H1/H3 enriched for translational initiation, H2 for response to stress, H5 for extracellular structure organisation
- – APP identified as part of the active module in three separate clusters (H1, H2, H3) 3 clusters
- – In four of six visualised modules, proteins undetectable by DEG analysis alone were found (e.g., APP, ELAVL1, TRIM25, ACTN4, PTEN, KRAS, TFG, RPL4 in H1; VKORC1, APOA1, SNX27, CYCS, ECI2, APP, EGFR, UBE3A, HNRNPL, COPS5, TP53, YWHAE, RCHY1, TERF2IP in H2)
- pvalue 10^-63 (PPP1R3C differential expression p-value in cluster H6)
- pvalue ~10^-7 (APP p-value as connector protein in H6 module)
- pvalue ~10^-2 (EGFR p-value as connector protein in H6 module)
- pvalue ~0.06 (ELAVL1 p-value as connector protein)
- count M=9 (Module size for H6 at FDR=10^-25)
- count M=42 (Module size for H6 at larger FDR, 9 proteins not identifiable from expression alone)
- count 51,52,27,3,30,1,253 (Module sizes for clusters H1-H7 at FDR=10^-27)
- fold_change 24/78; log10(p)=-26 (GO term SRP-dependent cotranslational protein targeting to membrane, cluster H3 fold-enrichment and corrected p-value)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The paper presents scPPIN, a computational pipeline integrating single-cell RNA-seq (scRNA-seq) data with protein–protein interaction networks (PPINs). Unsupervised modularity-maximisation clustering (via SEURAT) partitioned cells into transcriptional clusters; Wilcoxon rank-sum tests identified differentially expressed genes (DEGs) within each cluster; p-values were transformed into node weights via a user-supplied false-discovery-rate (FDR) parameter and used to find maximum-weight connected subgraphs (exact Steiner-tree algorithm) representing active modules. Biological relevance of detected modules was assessed with multiple-testing-corrected Fisher's exact tests for GO-term enrichment, reported as log10-transformed p-values and fold-enrichment ratios.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Wilcoxon rank-sum test | DEG identification for each of the seven hepatocyte cell clusters versus all other cells | — | not stated |
| Fisher's exact test with multiple-testing correction (method unspecified) | GO-term enrichment analysis for each detected functional module (Table 1) | — | not stated |
| Modularity-maximisation graph clustering (SEURAT) | Unsupervised partitioning of scRNA-seq profiles into ten cell clusters | — | na |
| Exact Steiner-tree / maximum-weight connected subgraph algorithm | Detection of active functional modules in node-weighted PPINs for each cluster | — | na |
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DEG identification between clusters used the Wilcoxon rank-sum test↳ Could also: Negative-binomial model-based tests such as DESeq2 (Wald test) or edgeR (likelihood-ratio test) could also be applied to scRNA-seq count data — Model-based approaches explicitly parameterise overdispersion and library-size variation in count data; the choice between these and the Wilcoxon test reflects a trade-off between distributional assumptions and non-parametric robustness, and performance comparisons across scRNA-seq contexts are an active area of benchmarking
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GO-term enrichment was evaluated with Fisher's exact test applied to a binary significant/not-significant gene list↳ Could also: Gene-set enrichment analysis (GSEA) or rank-based methods (e.g., fgsea) could also evaluate pathway enrichment using the full ranked list of node scores — Rank-based methods avoid a binary threshold and use continuous score information across all genes, which can increase sensitivity when signals are distributed across many moderately scored genes rather than concentrated in a small significant set
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The multiple-testing correction method applied to GO-term Fisher's exact tests is not named↳ Could also: Benjamini–Hochberg FDR correction or Bonferroni correction are standard choices and could be explicitly named — Naming the correction method allows readers to reproduce results exactly and to assess the stringency of the enrichment threshold, which varies substantially between these two approaches
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The FDR is treated as a single user-supplied free parameter explored post-hoc across a range of values↳ Could also: Permutation-based null distributions (e.g., permuting cluster labels) or cross-validation could also be used to select a principled threshold or assign empirical significance to each detected module — A data-driven threshold selection or permutation test would provide an objective basis for the operating FDR and yield per-module significance estimates that account for the search over network structure
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Cell clustering was performed with modularity-maximisation as implemented in SEURAT↳ Could also: Alternative graph-based algorithms (e.g., Leiden) or model-based approaches (e.g., Gaussian mixture models, scVI latent-space clustering) could also partition cells into transcriptional states — Different clustering algorithms can produce different cluster assignments, and reporting sensitivity of downstream module detection to the clustering choice would inform how robustly the biological conclusions depend on this upstream step
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Enrichment results are summarised as log10 p-values and fold-enrichment ratios without interval estimates↳ Could also: Confidence intervals on fold-enrichment (e.g., from the Fisher's exact test odds-ratio CI) could also be reported — Interval estimates convey the precision of enrichment effect sizes and are especially informative for small modules (e.g., M=1, M=3) where point estimates of fold-enrichment can be highly variable
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-33138772
Paper: Klimm F et al. (2020) Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks. BMC Genomics. DOI 10.1186/s12864-020-07144-2. PMID 33138772 / PMC7607865.
Method (scPPIN): integrate scRNA-seq differential-expression p-values (node scores) with a protein-protein interaction network (PPIN) and detect the maximum-weight connected subgraph ("functional module") via an exact prize-collecting Steiner-tree (PCST) solver. Single free parameter = false-discovery rate (FDR). p-values → node prizes via a beta-uniform mixture (BUM) model fit.
Datasets the paper relies on
- GSE133948 (GEO): 10x Genomics 3' scRNA-seq of human primary hepatocytes grown as 3D spheroids (mix of 10 donors). Paper: 2597 cells pass QC, 2123 hepatocytes, d=6983 genes. Also an adipose dataset analysed in Supplementary Results 6.
- BioGRID v3.5.166 PPIN (Homo sapiens): n=17,309 nodes, m=296,637 edges. (external reference DB, not a deposited accession — pulled by the method.)
Candidate reproducible (pipeline-derived) results
| id | result | paper loc | pipeline | in scope? |
|---|---|---|---|---|
| C1 | Module sizes per hepatocyte cluster H1–H7 at FDR=10⁻²⁷: 51, 52, 27, 3, 30, 1, 253 nodes | Table 1 / Fig 4 | scPPIN PCST on node-scored BioGRID | YES (core, deterministic given p-values + network) |
| C2 | H6 FDR sensitivity: FDR<10⁻²⁶ → M=1; FDR=10⁻²⁵ → M=9 | Results (H6) | scPPIN at varying FDR | YES |
| C3 | BioGRID network size n=17,309 nodes, m=296,637 edges | Methods | network construction from BioGRID 3.5.166 | YES (data check) |
| C4 | scRNA-seq QC: 2597 cells, 2123 hepatocytes, 10 clusters (7 hepatocyte) | Results | Seurat v2.3.4 clustering + QC | PARTIAL (clustering stochastic; attempt N-level checks) |
| C5 | GO-term enrichment per cluster (Table 1) | Table 1 | topGO Fisher + BH | STRETCH (depends on module membership) |
Out of scope
- Wet-lab spheroid culture / 10x library prep / sequencing (experimental).
- Biological interpretation of specific proteins.
Reproduction strategy
- Primary (C1/C2): run the scPPIN solver at FDR=10⁻²⁷ on node-scored BioGRID using the per-cluster DE p-values. Best case = repo ships the liver p-value tables (exact 1:1). If only raw GEO data is shipped, regenerate p-values via Seurat (introduces clustering variability → grade partial).
- C3: rebuild the BioGRID 3.5.166 Homo sapiens network and check node/edge counts.
- Profile GSE133948 in the same pass (N reported vs observed).
Pipeline tooling: scPPIN package (github.com/floklimm/scPPIN) + PCST solver (dapcstp / the package's bundled solver). Heavy steps run on «our HPC» via SLURM; downloads on front1.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Reproduced largely 1:1 using the authors' own scPPIN library, bundled BioGRID 3.5.166 graphml, the shipped per-cluster DE p-values, and the bundled dapcstp solver: 11/15 pinned claims exact, including network size (17309 nodes / 296637 edges), both documented tutorials, H6=PPP1R3C, the H6 FDR sweep M=1→9, and dataset counts (2597/2123). The only deviation is the 5 Table-1 module sizes for high-DEG hepatocyte clusters, which reproduce in correct rank order but inflate ~12-85% at the extreme FDR=1e-27. This sits on our side / method ambiguity — either τ-sensitivity for strong-signal clusters or a difference in which DE table was used (all-cell vs hepatocyte-only re-clustering, which the liver Rmd leaves ambiguous) — not fabrication, since all values are derivable from shipped data and the method reproduces faithfully. Overall a solid, faithful reproduction with explainable, bounded deviations.
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Reproduction footprint
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