Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
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
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡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
Described well enough to reproduce: the paper combines 3 public liver scRNA-seq deposits (GSE115469/124395/125188) into a CD45+ immune meta-atlas (Seurat v3 + Harmony validation). Code link is the third-party Harmony tool (no authors' analysis repo); per P16 we reproduced by running an independent scanpy 1.10.4 + harmonypy-equivalent pipeline on the same data. STRONG 1:1 on structure and concordance: all 4 immune lineages recovered; GSE115469 and GSE124395 lineage proportions fall in the reported ranges and match the original authors' own labels; all 3 inter-dataset pseudobulk Pearson correlations reproduce within 0.02 (0.944/0.82/0.807 vs 0.95/0.81/0.79). TWO reported figures are contradicted by the deposited data and flagged possible-discrepancy: (C4) the paper's B-cell range of 2-7% is incompatible with GSE125188, which is B-rich (~19% B per its own deposited author labels, ~22% in our re-run); (C5) the ~32,000-cell atlas is smaller than the GSE125188 CD45+ matrix alone (~60,672 cells), implying undocumented subsampling. NOT attempted (hard last-20% / out of scope): combined Harmony merge, Seurat-vs-Harmony 96.5-99.9% agreement meta-claim, between-dataset DE-gene counts, IPA pathway analysis (commercial).
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 63assessed: 2026-06-15 ⛓ 3a5aa75e25a3
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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
- 2026-06-15
- 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: opusCan a meta-analysis of existing normal human liver scRNA-seq datasets be performed—accounting for inter-study heterogeneity—to generate a comprehensive human liver immune meta-atlas that defines the dominant phenotypes of hepatic immune cell subpopulations and serves as a reference for healthy liver immune homeostasis?
- ★ Independent human liver immune scRNA-seq datasets can be combined into an integrated meta-atlas in which all datasets co-cluster, despite differing cell-type proportions between studies. finding
- ★ A reproducible method (Seurat v3 integration with anchor-based batch correction) for combining heterogeneous human liver scRNA-seq datasets into a re-usable immune meta-atlas. method
- ★ Canonical pathways differing between datasets relate to cell stress and oxidative phosphorylation rather than immune-related function, supporting that integration is biologically meaningful. finding
- ★ Hepatic immune homeostasis is characterized by decreased expression across immunologic pathways and enhancement of pathways involved with cell death, relative to PBMC. mechanism
- ★ An online interactive human liver immune meta-atlas resource defining gene signatures for myeloid, NK/T, B, and plasma cell subpopulations. resource
- Clustering results are robust to algorithm choice, with 96.5–99.9% agreement between Seurat and Harmony clusters. finding
- NK and T cells are the most abundant hepatic immune subpopulation, while B cells and plasma cells are the rarest, consistent across datasets. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| scRNA-seq (mCEL-Seq2) | Human liver, 9 liver resection patients (mCRC/ICC), CD45+ leukocytes (LACEe / Aizarani et al.) | none | single-cell UMI gene expression counts | mCEL-Seq2; GSE124395 |
| scRNA-seq (10x Genomics) | Human liver, 5 caudate lobes of DBD transplant donors, CD45+ leukocytes (Lnb / MacParland et al.) | none | single-cell UMI gene expression counts | 10x Genomics; GSE115469 |
| scRNA-seq (10x Genomics) | Human liver (CD45-enriched), 3 adult transplant donors (blood, spleen, liver barcoded; only liver used) (LCD45e / Zhao et al.) | none | single-cell UMI gene expression counts | 10x Genomics; GSE125188 |
| Integrated scRNA-seq meta-analysis (clustering/UMAP, differential expression) | Combined human liver immune meta-atlas, 17 patients / ~32,000 CD45+ cells | none | integrated clustering, immune subpopulation proportions, differential gene expression | Seurat v3.0 in R (RRID:SCR_007322); Harmony R package |
| Reference scRNA-seq comparison (differential expression) | Normal human PBMC, 67,221 cells | none | differential gene expression vs liver immune subpopulations | GSE171555 |
| Ingenuity Pathway Analysis | Differential expression gene lists from liver immune subpopulations | none | up/downregulated canonical pathways, gene function heatmaps | IPA, Qiagen (RRID:SCR_008653) |
| Gene expression correlation analysis (linear/quadratic regression, RRHO) | Pairwise between three liver datasets | none | correlation coefficient R / rank-rank hypergeometric overlap | RRHO package in R |
- – All three datasets co-clustered on shared UMAP coordinates with homogeneous interdigitation of clusters
- – Lnb and LCD45e (both 10x platform) showed high gene expression correlation R=0.95
- – LACEe had lower concordance with LCD45e R=0.79
- – LACEe had lower concordance with Lnb R=0.81
- – Immune cell subpopulation proportions differed significantly across all three datasets for each cell type p<0.01 each
- – Seurat vs Harmony clustering agreement across cell types 96.5%-99.9%
- – Hepatic immune homeostasis shows decreased expression across immunologic pathways and enhanced cell-death pathways vs PBMC
- – LCD45e comprised the majority of cells (~24,000) vs ~4,000 in each of the other two studies ~24,000 vs ~4,000
- correlation R=0.95 (Gene expression correlation Lnb vs LCD45e (both 10x))
- correlation R=0.81 (Gene expression correlation LACEe vs Lnb)
- correlation R=0.79 (Gene expression correlation LACEe vs LCD45e)
- pvalue p<0.01 (Chi-squared test, differences in each immune cell subpopulation proportion across datasets)
- pvalue p=0.16 (Chi-squared, plasma cells LCD45e vs Lnb (not significant))
- pvalue p=0.08 (Chi-squared, B cells Lnb vs LACEe (not significant))
- count ~32,000 hepatic CD45+ cells (Combined meta-atlas across 17 normal human liver samples)
- count 67,221 cells (Normal human PBMC reference dataset (GSE171555))
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 study performed a meta-analysis of three publicly available scRNA-seq datasets of normal human liver immune cells (~32,000 CD45+ cells from 17 donors), integrating them using Seurat v3 anchor-based batch correction. Dataset comparability was assessed via Chi-squared tests on cell-type proportions and Pearson/Spearman gene expression correlation. Differential gene expression between immune subpopulations and between the liver meta-atlas and a PBMC reference was conducted using the Wilcoxon rank-sum test, with Bonferroni-adjusted p-values and log fold-change thresholds used to define significant genes for downstream pathway analysis in IPA.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Chi-squared test | Comparison of immune cell subpopulation proportions between the three datasets (Figure 2I) and pairwise between datasets | ~32,000 cells across 17 donors (cell counts per subpopulation per dataset; exact per-group counts not stated) | not stated |
| Wilcoxon rank-sum test (via Seurat FindMarkers) | Differential gene expression between each immune cell subpopulation vs. all other subpopulations within and across datasets; also liver meta-atlas vs. PBMC reference | Varies by subpopulation and dataset; exact per-group cell counts not stated | not stated |
| Two-sample t-test | Difference-of-differences analysis comparing differential expression between datasets and between cell subpopulations (volcano plots) | Not stated | not stated |
| Linear and quadratic regression (Pearson R) | Pairwise gene expression correlation between datasets (Figures 2E–G) | Averaged expression per gene across cells; number of genes not stated | not stated |
| Spearman rank correlation | Correlation of differential-expression rankings between datasets, stratified by cell type | Not stated | not stated |
| Rank-Rank Hypergeometric Overlap (RRHO) | Assessment of overlap in ranked DE gene lists between datasets by cell type | Not stated | not stated |
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Cell-type proportions were compared between datasets using Chi-squared tests, treating cell counts as independent observations↳ Could also: Compositional data analysis methods such as Dirichlet regression or scCODA (a Bayesian compositional model for single-cell data) could also be applied — Cell-type proportions from scRNA-seq sum to one and are compositional by nature; methods designed for compositional data explicitly model this constraint and the correlation structure among cell types, and scCODA additionally accounts for donor-level variability
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Differential gene expression was performed at the single-cell level using the Wilcoxon rank-sum test via Seurat FindMarkers, treating each cell as an independent observation↳ Could also: Pseudobulk approaches — aggregating counts to the donor level and then applying DESeq2 or edgeR — could also be used — Cells from the same donor are not statistically independent; pseudobulk methods propagate donor-level variability into the test, reducing inflated type I error rates that can arise when many cells per donor are treated as independent replicates
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Bonferroni correction was applied to control for multiple testing across genes in the volcano plot analyses↳ Could also: Benjamini-Hochberg false discovery rate (FDR) correction could also be applied across the same family of gene-level tests — Bonferroni controls the family-wise error rate and is conservative when tests are correlated (as co-expressed genes tend to be); BH-FDR controls the expected proportion of false discoveries and is widely used in genomic contexts where thousands of genes are tested simultaneously, typically yielding more discoveries at the same nominal error threshold
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Multiple pairwise Chi-squared tests were conducted across cell types and dataset pairs without a stated correction for the family of comparisons↳ Could also: A single omnibus test (e.g., a log-linear model or permutation-based test) followed by post-hoc pairwise comparisons with a correction such as Bonferroni or Holm could also be used — Running multiple pairwise Chi-squared tests increases the probability of at least one spurious significant result; an omnibus-then-post-hoc strategy makes the error-rate control for the full family of comparisons explicit
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Seurat v3 anchor-based canonical correlation analysis (CCA) integration was used for batch correction across the three datasets↳ Could also: Alternative integration methods such as scVI (a deep generative model) or BBKNN (batch-balanced k-nearest neighbors) could also be applied to the same datasets — Different integration methods make different assumptions about batch structure; benchmarking studies show that no single method dominates across all data configurations, and trying a second method provides additional evidence that conclusions are not integration-algorithm-dependent (the authors did partially address this by comparing Seurat to Harmony)
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Gene expression similarity between datasets was summarized using Pearson correlation on per-gene average expression values↳ Could also: Mutual nearest neighbors (MNN) distance or a cosine similarity on the full single-cell embedding could also quantify dataset concordance — Pearson correlation on averaged expression collapses within-cell-type heterogeneity and can be dominated by highly expressed genes; cell-embedding-level similarity metrics preserve single-cell resolution and are less sensitive to a small number of outlier genes
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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Liver immune cell gene expression profiles from two independent 10x Genomics datasets (Lnb and LCD45e) show high cross-dataset correlation (R=0.95)scRNA-seq human liver 2021×1papers★ This paper is the founder (earliest)
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Seurat and Harmony clustering algorithms agree on immune cell type assignment in 96.5-99.9% of integrated human liver CD45+ cellsscRNA-seq human liver 2021×1papers★ This paper is the founder (earliest)
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Proportions of each immune cell subpopulation differ significantly across three independent human liver scRNA-seq datasets (p<0.01 per cell type)scRNA-seq human liver mixed 2021×1papers★ This paper is the founder (earliest)
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CD45+ immune cells from three independent human liver scRNA-seq datasets co-cluster homogeneously on a shared UMAP, confirming successful cross-dataset integrationscRNA-seq human liver 2021×1papers★ This paper is the founder (earliest)
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Hepatic CD45+ immune cells show downregulated immunologic pathways and upregulated cell-death pathways relative to peripheral blood mononuclear cellsscRNA-seq human liver mixed 2021×1papers★ This paper is the founder (earliest)
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.
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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.
Downstream reach in the literature
94 downstream papers · 4 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34335581
Paper: Rocque B, Barbetta A, Singh P, et al. "Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis." Front Immunol 2021;12:679521. PMCID PMC8322955. DOI 10.3389/fimmu.2021.679521.
What the paper does (pipeline)
Integrates THREE published human-liver scRNA-seq datasets into one CD45+ immune "meta-atlas", clusters, annotates immune lineages, compares composition.
| paper alias | dataset | GEO | platform | cells sequenced |
|---|---|---|---|---|
| LACEe | Aizarani et al. liver atlas | GSE124395 | mCEL-Seq2 | 10,372 |
| Lnb | MacParland et al. | GSE115469 | 10x | 8,444 |
| LCD45e | Zhao et al. CD45+ | GSE125188 | 10x | 70,706 |
Tools: Seurat v3.0 (integration FindIntegrationAnchors + clustering
FindClusters, UMAP), Harmony R package as clustering validation, IPA (pathway,
commercial). Normalization = library-size scaling, factor 1e4, log1p. 30 PCs.
In scope (pipeline-derived → attempted)
- S1 Recover the 4 major immune lineages: NK&T (CD3D/KLRF1/FCGR3A), Myeloid (CD14/FCGR3A), B (CD19), Plasma (SDC1/CD138). [Results, Fig 1-2]
- S2 Per-dataset immune-lineage proportions vs reported ranges: NK&T 51-69%, Myeloid 18-32%, Plasma 4-14%, B 2-7%.
- S3 Total hepatic CD45+ cells ≈ 32,000 across the 3 datasets.
- S4 (stretch) Pseudobulk Pearson R between datasets: Lnb
LCD45e 0.95, LACEeLnb 0.81, LACEe~LCD45e 0.79.
Out of scope (not pipeline / not attempted)
- IPA pathway analysis (commercial Qiagen software, no key) — env_unresolvable.
- Seurat-vs-Harmony 96.5-99.9% cluster-agreement: a meta-claim about the authors' own two clusterings on their own integrated object; not independently re-derivable without their exact Seurat object. Noted, not graded 1:1.
- DE-gene counts between datasets (3,526 / 767 / 924 …) and signature sizes (16/22/45/54): highly sensitive to exact integration + DE test params; skipped as the hard last-20%.
- Shiny web resource (interactive, not a numeric pipeline output).
Approach
Code link is immunogenomics/harmony (third-party tool) → per brief P16 this is a
valid reproduction by applying the documented pipeline (Seurat-equivalent scanpy +
Harmony) to the paper's own data. We run an INDEPENDENT scanpy 1.10 + harmonypy
pipeline (standard equivalent of Seurat v3 + Harmony) on the three GEO matrices.
Author-provided cluster labels (shipped by GSE125188 / GSE115469) are used only as
an orthogonal cross-check, not as the reproduced value.
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.
Structure and the central quantitative claim reproduce strongly: all 4 immune lineages recover, 2/3 dataset compositions fall in range, and all three pseudobulk Pearson R reproduce within 0.02 (0.944/0.82/0.807 vs 0.95/0.81/0.79). However, two reported figures — the 2-7% B-cell range (C4) and the ~32,000-cell atlas (C5) — are contradicted by GSE125188's own deposited data (~19% B, ~60,672 CD45+ cells), and our independent re-run matches the original deposit authors rather than this paper. The discrepancy sits on the authors' side (undocumented subsampling/denominator and an overgeneralized composition range), not our methodology. Net: solid reproduction with two unsupported descriptive figures flagged for admin review — not core-conclusion fabrication.
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.