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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v1 current initial assessment Score 94assessed: 2026-06-14 ⛓ e2605a8ef506
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- Reproduced
- 2026-06-14
- Rubric version
- v1.0
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no 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: opusThe malignant cell population in scRNA-seq data—because it clusters by patient due to patient-specific somatic alterations—can be integrated with bulk TCGA RNA-seq markers of inflamed/cold tumors to identify patients with a T-cell inflamed tumor immune microenvironment from single-cell data.
- ★ iBRIDGE integrates the malignant subset of scRNA-seq data with reference bulk TCGA RNA-seq data to identify patients with a T-cell inflamed vs cold TIME. method
- ★ iBRIDGE single-cell classifications correlate highly with matched bulk assessments (0.85 and 0.9 correlation coefficients). finding
- ★ Malignant cells cluster by patient whereas immune and stromal cells cluster by cell type, motivating use of malignant cells as the bridge to bulk data. mechanism
- ★ Type I and type II interferon pathways are dominant signals of the inflamed phenotype, especially in malignant and myeloid cells. finding
- ★ A TGFβ-driven mesenchymal phenotype marks the inflamed state not only in fibroblasts but also in malignant cells. finding
- ★ Per-patient average iBRIDGE scores combined with bulk ICR or CD3E RNAScope ground-truth enable threshold-based absolute classification via ROC/Youden index. method
- ★ iBRIDGE can be applied to in vitro cancer cell lines and identify lines adapted from inflamed/cold patient tumors. finding
- iBRIDGE is released as an open-source R package with a workflow vignette. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| scRNA-seq (10X Genomics) | NSCLC/LUAD patient tumors (GSE131907, Kim et al.) | none | raw UMI counts; malignant cell inflamed/cold classification | 10X Genomics |
| scRNA-seq (10X Genomics) | Colon adenocarcinoma patient tumors (GSE132465) | none | raw UMI counts; cell classification | 10X Genomics |
| scRNA-seq (10X Genomics) | Breast cancer patient tumors (GSE176078) | none | raw UMI counts; cell classification | 10X Genomics |
| Bulk RNA-seq | Breast cancer patients (GSE176078, matched to scRNA-seq) | none | raw counts; ICR GSE scores as ground-truth | — |
| scRNA-seq (10X Genomics) + CD3E RNAScope | Colon adenocarcinoma patient tumors (GSE178341; 9-patient RNAScope subset) | none | raw UMI counts; CD3E percent positive cells as ground-truth | 10X Genomics / RNAScope |
| Bulk RNA-seq | TCGA multi-cohort tumors (10079 patients) | none | raw counts; ICR signature GSE, DE between inflamed/cold tertiles | — |
| Bulk RNA-seq (in vitro) | Cancer cell lines, CCLE/DepMap (838 lines) | none (cell line) | raw counts; iBRIDGE inflamed/cold classification | DepMap portal |
| Microarray (in vivo and in vitro) | Multi-tumor patient and cell line samples (GSE85507) | none | RMA-normalized expression; in vivo correlates of ICR | microarray |
- ▲ iBRIDGE single-cell inflamed/cold scores correlated highly with matched bulk assessments. r=0.85 and r=0.90
- ▲ Malignant cells show the highest pair-wise inter-patient distances (Euclidean or 1-Pearson) versus immune/stromal cells, which cluster by type.
- ▲ GO analysis of inflamed patients shows interferon pathways enriched, dominant in malignant and myeloid cells.
- ▲ TGFβ-driven mesenchymal phenotype enriched in inflamed fibroblasts and malignant cells.
- – Within a patient cluster, malignant cells show minimal assignment of opposite (inflamed vs cold) classes, enabling unambiguous patient classification.
- – iBRIDGE applied to CCLE cell lines distinguishes lines derived from inflamed vs cold tumors.
- correlation 0.85 (iBRIDGE vs matched bulk assessment correlation coefficient)
- correlation 0.9 (iBRIDGE vs matched bulk assessment correlation coefficient)
- count 20-gene ICR signature (ICR signature used to mark Th1 cytotoxic infiltration in bulk)
- count top 3,000 highly variable features (variable genes extracted from malignant scRNA-seq cells)
- count ~210K cells (NSCLC GSE131907 dataset cell count (58 patients))
- other Padj < 0.05 and log2(FC) > 0.3 (scRNA-seq DE significance threshold (Seurat FindMarkers))
- other Padj < 0.01 and log2(FC) > 1 (bulk RNA-seq/microarray DE significance threshold (limma))
- other 5% CD3E positive cells; twofold and 20% difference cutoffs (thresholds for absolute (RNAScope) and relative patient classification)
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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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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