iBRIDGE: A Data Integration Method to Identify Inflamed Tumors from Single-cell RNA-Seq Data and Differentiate Cell Type-Specific Markers of Immune-Cell Infiltr
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
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
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 -> reproduced 1:1. Reproduced the iBRIDGE R package's own worked README vignette (authors' code, commit 6a33706) on the package-SHIPPED GSE176078 breast-cancer scRNA-seq malignant subset with TCGA-BRCA inflamed/cold signatures, end-to-end on «our HPC» SLURM (conda: Seurat+AUCell). All comparable expected outputs reproduced: matrix dim EXACT; inflamed & cold top-6 gene-feature lists EXACT (and EXACT under two independent Seurat versions 4.4.0 and 5.5.0); cell-class counts within <0.1% (8636/8637/10642 vs 8626/8624/10665); 22 per-patient inflammation scores within ~2% with the same ascending rank order; above/below-threshold patient split 13/9 IDENTICAL. Sub-percent drift in C4/C5 attributable to Seurat 5.5.0/sctransform 0.4.3 (current bioconda, solver-upgraded from intended 4.4.0 pin) vs the author's ~2022 build, not a pipeline disagreement. NO fabrication signal: every value derivable from shipped package data+code. NOT ATTEMPTED (out of scope, the hard ~20%): GSE131907 lung dataset named in the spawn brief (repo's documented runnable example is GSE176078/BRCA only; GSE131907 has no documented parameters and would need rebuilding the malignant subset from raw GEO); myeloid/fibroblast cell-type marker analyses, IFN-pathway enrichment, in-vitro cell-line application, and UMAP/cluster figures (stochastic, no pinnable numbers).
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Assessment versions
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v1 current initial assessment Score 94assessed: 2026-06-14 ⛓ e2605a8ef506
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When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-09-19
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 malignant cell population in scRNA-seq data, because it uniquely clusters by patient rather than by cell type, can be integrated with bulk RNA-seq (TCGA) markers of T-cell inflamed tumors to identify patients with an inflamed versus cold tumor immune microenvironment directly from single-cell data.
- ★ Malignant cells cluster by patient in scRNA-seq data while immune and stromal cells cluster by cell type, making malignant cells uniquely suited to carry patient-level inflamed/cold signal finding
- ★ iBRIDGE integrates bulk RNA-seq-derived inflamed/cold markers (via ICR signature) with highly variable genes from malignant scRNA-seq cells to generate 'Inflamed' and 'Cold' gene sets for classifying single cells and patients method
- ★ iBRIDGE classification scores correlate highly with matched bulk RNA-seq ICR-based assessments in two independent datasets (r=0.85 and r=0.9) finding
- ★ Type I and type II interferon pathways are dominant markers of inflamed phenotype, especially in malignant and myeloid cells mechanism
- ★ A TGFβ-driven mesenchymal phenotype marks inflamed tumors not only in fibroblasts but also in malignant cells mechanism
- ★ iBRIDGE can be applied to in vitro cancer cell lines to identify lines adapted from inflamed/cold patient tumors finding
- Per-patient average iBRIDGE scores combined with RNAScope quantification enable absolute (threshold-based) classification of inflamed/cold tumors via ROC/Youden index method
- iBRIDGE R package and workflow vignette are publicly available on GitHub resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| scRNA-seq (10X Genomics) | human NSCLC tumors (GSE131907) | none | malignant cell clustering by patient, iBRIDGE inflamed/cold classification, UMAP | 10X Genomics |
| scRNA-seq (10X Genomics) | human COAD tumors (GSE132465) | none | iBRIDGE inflamed/cold classification of malignant cells | 10X Genomics |
| scRNA-seq (10X) + matched bulk RNA-seq | human BRCA tumors (GSE176078) | none | correlation of iBRIDGE scores with bulk ICR GSE scores for absolute classification | 10X Genomics |
| scRNA-seq (10X) + RNAScope (CD3E) | human COAD tumors (GSE178341, 9-patient RNAscope subset) | none | correlation of iBRIDGE scores with CD3E-positive cell percentage | 10X Genomics; RNAScope |
| Bulk RNA-seq | TCGA pan-cancer tumor samples (n=10,079) | none | single-sample GSE of ICR signature; DE between high/low ICR tertiles (DESeq2) | — |
| Bulk microarray and in vitro microarray | multi-cancer patient tumors and cell lines (GSE85507) | none | RMA-normalized expression, ICR correlates via limma | microarray (RMA normalized) |
| RNA-seq | CCLE in vitro cancer cell lines (n=838, multi-cancer) | none | GSE-based classification of inflamed/cold adapted cell lines | DepMap/CCLE RNA-seq |
| Gene ontology / DE analysis | scRNA-seq malignant, myeloid, fibroblast populations (NSCLC dataset) | none | cell type-specific inflamed/cold differential markers and enriched biological process terms | Seurat FindMarkers; clusterProfiler |
- – Malignant cells show the highest pair-wise Euclidean/1-Pearson distance between patients compared with immune and stromal cell types, which cluster by cell type instead
- ▲ iBRIDGE scores correlated highly with matched bulk RNA-seq ICR assessments in two datasets r=0.85 and r=0.9
- ▲ Inflamed phenotype markers dominated by type I and type II interferon pathway genes, especially in malignant and myeloid cells
- ▲ TGFβ-driven mesenchymal phenotype identified in inflamed fibroblasts and also in inflamed malignant cells
- – iBRIDGE applied to CCLE cell lines identified lines adapted from inflamed/cold patient tumors
- correlation 0.85 (iBRIDGE score vs bulk ICR GSE score, GSE176078 matched bulk/scRNA-seq BRCA dataset)
- correlation 0.9 (iBRIDGE score vs CD3E RNAScope, GSE178341 COAD subset)
- pvalue Padj<0.05, log2(FC)>0.3 (significance threshold for scRNA-seq DE analysis (Seurat FindMarkers))
- pvalue Padj<0.01, log2(FC)>1 (significance threshold for bulk RNA-seq/microarray DE analysis (limma))
- count 58 patients, 208506 cells (GSE131907 NSCLC scRNA-seq dataset)
- count 62 patients, 371223 cells (GSE178341 COAD scRNA-seq dataset)
- count 10079 patients (TCGA multi-cancer bulk RNA-seq cohort)
- count 838 cell lines (CCLE in vitro RNA-seq dataset)
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.
iBRIDGE is a computational integration method that overlaps highly variable malignant-cell features from scRNA-seq datasets with bulk RNA-seq differential expression markers derived from TCGA tertile comparisons. Inflamed/cold classification at the cell and patient level relies on tertile-based thresholding of AUCell gene-set enrichment (GSE) scores, with DESeq2 used for bulk DE, Seurat FindMarkers for scRNA-seq DE, and limma for microarray DE. Validation is performed via Pearson correlation against matched bulk ICR scores and ROC/Youden-index analysis for absolute classification thresholds. Results are reported with adjusted p-value and log2FC thresholds, and visualized with box-whisker plots and correlation coefficients.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 differential expression (Wald test by default) | Bulk TCGA RNA-seq: top vs. bottom ICR tertile patients per cohort; CCLE cell lines inflamed vs. cold | TCGA total n=10,079 across cohorts; per-cohort n for each DE run not stated | not stated |
| Seurat FindMarkers differential expression | scRNA-seq: inflamed vs. cold comparisons per cell type within each dataset | Varies by dataset: 58 patients/208,506 cells (GSE131907), 23/47,285 (GSE132465), 26/100,064 (GSE176078), 62/371,223 (GSE178341) | not stated |
| limma linear model | Microarray dataset GSE85507: in vivo and in vitro ICR correlates | 15 in vivo + 16 in vitro samples | not stated |
| AUCell gene set enrichment scoring (top-10% feature ranking threshold) | Per-cell inflamed and cold GSE scores for iBRIDGE classification across all scRNA-seq datasets | Cell counts per dataset as listed in Table 1 | na |
| yaGST single-sample gene set enrichment | Bulk ICR GSE scores for TCGA patients and CCLE in vitro cell lines | TCGA n=10,079; CCLE n=838 cell lines | na |
| ROC curve and Youden index (ROCit R package) | Absolute classification threshold identification: GSE176078 (matched scRNA-seq/bulk, n=24) and GSE178341 (matched RNAscope, 9-patient subset) | n=24 (GSE176078); n=9 (GSE178341) | not stated |
| Pearson correlation coefficient | Validation of per-patient iBRIDGE scores against matched bulk ICR scores in two datasets (r=0.85 and r=0.90 reported) | n=24 patients (GSE176078); second dataset n not stated in available text | not stated |
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Inflamed/cold patient classification in bulk data used fixed tertile cutoffs of the ICR GSE distribution, excluding the middle third of samples from DE marker derivation↳ Could also: A data-driven continuous threshold (e.g., Gaussian mixture model, k-means on scores, or median split retaining all samples) could also be used to define inflamed/cold groups — Tertile-based dichotomization discards approximately one-third of observations; continuous or mixture-model approaches retain all data points and may better capture cases where the score distribution is unimodal rather than bimodal
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scRNA-seq differential expression between inflamed and cold classes was performed with Seurat's FindMarkers, treating each cell as an independent observation↳ Could also: Pseudobulk DE methods — e.g., aggregating counts per patient then applying DESeq2 or edgeR — could also be used for multi-patient scRNA-seq DE — Cell-level DE methods can underestimate variance by ignoring within-patient correlation among cells from the same individual; pseudobulk approaches account for this structure and are increasingly recommended for multi-patient scRNA-seq studies
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AUCell with a top-10% ranking threshold was used for single-cell gene set enrichment scoring↳ Could also: UCell, ssGSEA, or GSVA could also be applied for single-cell GSE scoring — Different scoring methods differ in handling of sparse/dropout counts and score scale; comparing results across methods (e.g., AUCell vs. UCell) can assess robustness of cell-level classifications
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Pearson correlation was used to validate per-patient iBRIDGE scores against matched bulk ICR scores↳ Could also: Spearman rank correlation could also be computed alongside Pearson for this validation — Pearson assumes linearity and is sensitive to outliers; Spearman is non-parametric and more robust when score distributions are skewed, which is relevant for small validation cohorts (n=24 and n=9)
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Pairwise patient distances across cell types were visualized with box-whisker plots without a formal statistical test reported for the comparison↳ Could also: A Kruskal-Wallis test or a mixed-effects model with post-hoc pairwise correction could also be applied to formally compare distance distributions across cell types — A formal test with multiple-comparison correction would quantify whether the observed differences in within-patient clustering between malignant and non-malignant compartments reach statistical significance beyond visual inspection
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Separate DE analyses were run independently per cell type and per dataset without a unified family-wise correction across all comparisons↳ Could also: A global BH FDR correction applied across all cell-type and cohort comparisons simultaneously could also be used — Running independent DE analyses per cell type inflates the total number of hypotheses tested across the experiment; a unified correction would control the experiment-wide false discovery rate more conservatively
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
This is a clean, essentially 1:1 reproduction of the iBRIDGE authors' own README vignette on the package-shipped GSE176078/TCGA-BRCA data: matrix dimension and both inflamed/cold top-6 gene lists are EXACT, and the 13-above/9-below patient split is identical. The only deviations are sub-percent class counts (≤0.08%) and ~2% per-patient scores, fully attributable to Seurat 5.5.0/sctransform 0.4.3 vs the author's ~2022 build — a technical/version cause, not authors' or methodology fault, with no fabrication signal. The main caveat is scope: the brief named GSE131907 (lung) but only the documented BRCA vignette was reproducible, so cell-type-marker, IFN-enrichment and in-vitro claims remain unverified — yet every value that was compared is derivable and matches.
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