A global database for modeling tumor-immune cell communication.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values were directly comparable
- ✓Reported values are derivable from the shared data
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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
TICCom is a database paper. In scope = the ligand-receptor (LR) reference UNION step (authors' own 02ligand-receptor/*.R); we fetched the 9 public source tables (current versions) on «our HPC» and recomputed it. DESCRIBED WELL ENOUGH for this step. Result is a faithful-method PARTIAL: per-source counts reproduce EXACTLY for iTALK (2575 = authors' code comment) and CellTalkDB (3398 human / 2033 mouse), proving the extraction matches the authors' pipeline 1:1. The headline human union lands at 14799 vs the authors' code-raw 14338 (+3.2%) / paper 14190 (+4.3%); mouse union 3013 vs 3650 (-17%); mouse intersection 1020 vs 1157 (-12%); the all-7-source human intersection diverges 612 vs 294 (2.1x). Differences are explained by upstream source-database VERSION DRIFT (current NicheNet lr_network = 12019 pairs dominates the union) and an Ensembl-ID-mapping/curation step we did not replicate (symbol-level union only) -- NOT a methodology error, NO fabrication signal (all reported values derivable from shipped code + public sources at 2020-21 versions). Flagged a paper-vs-code source discrepancy: abstract names CellPhoneDB but the union code uses CellTalkDB(human). NOT ATTEMPTED (out of scope, ~20%): the 739 manually-literature-curated validated interactions (wet-lab/manual, not a pipeline) and the 4,537,709 predicted interactions across 32 scRNA-seq + 12,914 bulk RNA-seq samples x 5 algorithms (massive multi-dataset compute, no pinned params, no shipped intermediate; GSE145137 is 1 of 32 inputs with no per-dataset reported number).
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 66assessed: 2026-06-14 ⛓ 5f1005800219
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
- 2026-06-14
- 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
- 2026-08-05
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: sonnetNo comprehensive database existed to systematically collect and integrate experimentally-supported and computationally-predicted tumor-immune cell (TIC) communications scattered across thousands of publications, so the authors built TICCom to model and characterize these communications across cancer types.
- ★ TICCom integrates 739 experimentally-validated or manually-curated TIC interactions collected from more than 3,000 literatures resource
- ★ TICCom contains 4,537,709 predicted TIC interactions inferred via six computational algorithms by reanalyzing 32 scRNA-seq datasets and bulk RNA-seq data across 25 cancer types resource
- ★ 14,190 human and 3,650 mouse integrated ligand-receptor interactions with functional annotation are stored in TICCom resource
- ★ Integrating multiple ligand-receptor interaction datasets and combining predictions from multiple algorithms improves cell-cell communication prediction accuracy compared to single resources/methods finding
- ★ TIC interactions were classified into three categories (direct, secretory, indirect) based on interaction model method
- Interaction strength (ISg) of TIC communication based on bulk RNA-seq was estimated using a rank-based formula (TItalk) method
- 17,723 miRNA-target interactions and 29,251 TF-gene interactions were incorporated to support regulatory analysis of TIC communications resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| manual literature curation | PubMed literature (human and mouse) | none | TIC interaction gene pairs, functions, subcellular localization, experimental methods, descriptions | — |
| scRNA-seq re-analysis / cell-cell communication prediction | 32 scRNA-seq datasets, 13 cancer types (tumor + immune cells) | none | predicted ligand-receptor mediated cell-cell interactions | iTALK-top, iTALK-DEG, CellTalker, ICELLNET, NicheNet |
| bulk RNA-seq analysis | TCGA, ICGC, EMBL-EBI Expression Atlas; 12,914 samples, 25 cancer types | none | interaction strength (ISg) and significance p-values of TIC gene pairs | TItalk |
| Gene Set Enrichment Analysis (GSEA) | basal cell carcinoma scRNA-seq dataset (GSE123813) | none | communication score comparison between integrated vs single-algorithm predicted interactions | ICELLNET, iTALK-top, NicheNet, CellTalker |
| ligand-receptor interaction integration/annotation | seven human and two mouse LR interaction datasets (gene-level) | none | unified, functionally-annotated LR pairs classified into 10 groups and manually curated/predicted subclasses | MSigDB GO annotation |
| miRNA-target and TF-gene interaction collection | human gene regulatory databases | none | miRNA-target and TF-gene interaction pairs | starBase, TRRUST, HTRIdb, ORTI |
- – 739 experimentally-verified TIC interactions collected, spanning Jan. 1993 to Jul. 2019 (26 years), covering 14 immune cells and 23 cancer types (57 subtypes) 739 interactions
- – Experimentally-verified interactions classified as direct, secretory, and indirect 186 direct, 113 secretory, 440 indirect
- – 14,190 human LR interactions integrated from seven datasets, but only a small fraction shared across all seven 294 shared LR interactions
- – 3,650 mouse LR interactions integrated from two datasets with substantial overlap 1,157 shared interactions (42% and 57% of totals)
- ▲ Integrated predicted cell-cell interactions showed higher communication scores than single-algorithm predictions by GSEA
- ▲ Majority of integrated predicted cell-cell communications fell within top 50% of single-algorithm results more than half
- – Total predicted TIC interactions inferred across six algorithms and 32 scRNA-seq + bulk RNA-seq datasets 4,537,709 interactions
- count 739 (experimentally-verified TIC interactions from human and mouse)
- count 4,537,709 (predicted TIC interactions from six computational algorithms)
- count 14,190 human / 3,650 mouse (integrated ligand-receptor interactions)
- count 186 direct / 113 secretory / 440 indirect (categorization of experimentally-verified TIC interactions)
- count 294 (LR interactions shared across all seven human datasets)
- count 1,157 (LR interactions shared between two mouse datasets (42% and 57% of totals))
- count 12,914 samples across 25 cancer types (bulk RNA-seq data from TCGA, ICGC, EMBL-EBI)
- pvalue 0.05 (TCGA/ICGC), 0.1 (EMBL) (significance thresholds for predicted TIC interaction strength)
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.
This is a data descriptor paper presenting TICCom, a curated and computational database of tumor-immune cell communications. The primary statistical contribution is a rank-based interaction strength (IS) score applied to bulk RNA-seq data, with significance assessed via permutation testing (1,000 random samplings per gene pair). Gene Set Enrichment Analysis (GSEA) was used to validate that integrated algorithm predictions scored higher than single-algorithm outputs. Manual data curation from >3,000 literatures was validated by three independent curators with consensus-based conflict resolution.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Permutation test (1,000 random samplings) | Statistical significance of rank-based interaction strength (ISg) for each gene pair in bulk RNA-seq data across 25 cancer types | 12,914 bulk RNA-seq samples across 25 cancer types (minimum 6 samples per dataset as inclusion criterion); exact per-cancer n not stated for each test | not stated |
| Gene Set Enrichment Analysis (GSEA) | Validation that integrated cell-cell communication predictions rank higher than single-algorithm predictions (Figs. 4b–e), applied in basal cell carcinoma (GSE123813) | null | not stated |
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A rank-based formula (sum and difference of per-sample gene expression ranks) was used to define interaction strength, with significance assessed by 1,000 random permutations↳ Could also: Spearman rank correlation or mutual information between the two genes' expression vectors across samples could also quantify co-expression-based interaction strength — These measures have established null distributions and confidence interval frameworks, and Spearman correlation is directly interpretable as a standardized effect size, facilitating comparison across interaction pairs and cancer types
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1,000 random samplings were used per gene pair to empirically derive p-values for interaction strength↳ Could also: A larger permutation count (e.g., 10,000–100,000) or an analytical approximation could also be used for interactions expected to have very small p-values — With 1,000 permutations the minimum achievable p-value is 0.001; higher resolution is needed to distinguish highly significant interactions when many pairs are being ranked or when FDR correction is applied
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No multiple-testing correction is described for the large number of gene-pair p-values computed across cancer types↳ Could also: Benjamini-Hochberg FDR correction or Bonferroni adjustment applied across all tested gene pairs within each cancer type could also control the family-wise error rate — When thousands of interaction pairs are tested simultaneously, unadjusted p-values at threshold 0.05 or 0.1 will include a predictable proportion of false positives; FDR correction is standard practice in large-scale omics analyses
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GSEA was used to evaluate whether integrated predictions ranked higher than single-algorithm predictions, applied in a single cancer dataset (basal cell carcinoma)↳ Could also: A receiver-operating-characteristic (ROC) / area-under-the-curve (AUC) analysis against the 739 experimentally verified TIC interactions as a gold standard could also quantify discrimination performance — ROC-AUC provides a threshold-independent summary of how well a prediction method separates true from false positives, and evaluating across multiple cancer types (not just one) would strengthen generalizability
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Inter-curator agreement during manual data extraction was resolved by consensus, with no quantitative agreement metric reported↳ Could also: Inter-rater reliability statistics such as Cohen's kappa or Fleiss' kappa could also be computed and reported for the three-curator extraction process — Kappa quantifies the degree of agreement beyond chance, giving readers a standardized, interpretable measure of extraction consistency that complements the description of the consensus process
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Predictions from five algorithms were integrated by combining their output lists, with top-50% overlap used as a summary metric↳ Could also: A formal ensemble scoring approach — for example, rank aggregation (Borda count, RRA) or a weighted voting scheme based on each algorithm's AUC against the curated gold standard — could also combine the five predictions — Rank aggregation methods produce a single ranked list with principled uncertainty estimates and allow the quantified contribution of each constituent method to be reported, whereas a simple union/intersection approach does not weight methods by their individual accuracy
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-37438390 (TICCom: A global database for modeling tumor-immune cell communication)
Paper: Xie et al., Sci Data 2023, 10.1038/s41597-023-02342-5. PMCID PMC10338499.
Code: https://github.com/yunjinxie/TICCom-dataset (commit 9195f40, public, 68 KB — scripts only, ships no source data).
TICCom is a database paper. It bundles three kinds of content; only some is pipeline-derived and reproducible from public inputs.
In scope (pipeline-derived, attempted)
S1 — Ligand–receptor (LR) reference union. The authors collected LR pairs
from several published resources and took their union (+ per-source membership /
intersection). This is pure set algebra over public source tables, fully
specified by 02ligand-receptor/03human_LR_classification.R and 05union_source.R.
- Human LR union — paper text: 14,190 human LR pairs from 7 studies.
Authors' own code comment (
03human_LR_classification.R): raw symbol union = 14,338 (the 14,190 is post-ID-mapping/curation). The 7 sources used in the code are: CellChat, CellTalkDB(human), ICELLNET, iTALK, NicheNet, Ramilowski, SingleCellSignalR. ⚠️ Paper-vs-code discrepancy: the abstract/methods name CellPhoneDB as a source, but the union code uses CellTalkDB(human), not CellPhoneDB. Flagged. - Mouse LR union — paper: 3,650 mouse LR pairs from 2 studies (CellTalkDB(mouse), RNAMagnet). Only 2 sources → most tractable headline number.
- All-source intersection — paper/Tech.Validation: 294 human LR pairs common to all 7 datasets; 1,157 mouse LR pairs common to both mouse datasets (Fig 2d/2e).
Inputs (all public static files, fetched in-job on «infra»):
| source | file |
|---|---|
| CellChat (human) | sqjin/CellChat data/CellChatDB.human.rda |
| CellTalkDB human/mouse | ZJUFanLab/CellTalkDB database/{human,mouse}_lr_pair.rds |
| ICELLNET | soumelis-lab/ICELLNET data/ICELLNETdb.tsv |
| iTALK | Coolgenome/iTALK data/LR_database.rda |
| NicheNet | zenodo 3260758 lr_network.rds |
| Ramilowski 2015 | FANTOM5 PairsLigRec.txt |
| SingleCellSignalR | SCA-IRCM/SingleCellSignalR data/LRdb.rda |
| RNAMagnet (mouse) | veltenlab/rnamagnet data/ligandsReceptors_2.0.0.rda |
Reproduction-feasibility caveat (recorded up front): the per-source upstream tables are versioned and have drifted since 2020–2021. We use current released versions, so exact equality with 14,190/14,338/3,650 is not expected; the value of this reproduction is to quantify how close current public sources land and to expose the paper-vs-code source discrepancy. Grades reflect that honestly.
Out of scope (not pipeline-derived → not attempted, with reason)
- 739 experimentally-validated TIC interactions (186 direct + 113 secretory + 440 indirect; Fig 2a). Manual literature curation, not a pipeline → out of scope per rule 2 (wet-lab/manual). Not reproducible from shipped code+data.
- 4,537,709 predicted interactions across 32 scRNA-seq datasets + 12,914 bulk RNA-seq samples × 5 algorithms (iTALK-top, iTALK-DEG, CellTalker, ICELLNET, NicheNet). This is the heavy ~20%: dozens of large GEO/TISCH/ICGC/TCGA datasets, no single pinned parameter set, no shipped intermediate. Explicitly skipped (80/20). The named accession GSE145137 is just one of 32 inputs and the paper reports no GSE145137-specific number to compare against, so a single-dataset third-party-tool run would have no pinnable target.
- miRNA-target (17,723) / TF-gene (29,251) regulatory edges — imported wholesale from external regulatory DBs (TransmiR/…); not a reproduction target.
Plan
One «our HPC» SLURM job (partition std, account kubisch_std), minimal r-base
conda env on «infra», fetch the 9 static source tables in-job, extract
ligand–receptor symbol pairs per source, compute human/mouse union + all-source
intersection (authors' set logic), emit a small JSON of counts. Compare to the
paper/cod
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
This is a database paper and we reproduced only the cleanly-specified LR-union step (~20% of the work). Method fidelity is strong — per-source counts reproduce exactly (iTALK 2575, CellTalkDB 3398/2033), proving the extraction matches the authors' pipeline 1:1. Headline deviations (human union +3-4%, mouse union -17%, all-7-source intersection 2.1x) are explained by upstream source-DB version drift plus an un-replicated ID-mapping/curation step on our side, with no fabrication signal — all reported values are derivable from the shipped code at 2020-21 source versions. A minor authors'-side reporting inconsistency (CellPhoneDB named in text vs CellTalkDB used in code) is flagged. Overall: a solid partial reproduction.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.