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REVIEW 5 major objections 5 minor 89 references

HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity

T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper introduces HR-VILAGE-3K3M, a publicly available, AI-ready dataset that unifies 14,136 longitudinal RNA-seq profiles from 3,178 subjects across 66 respiratory-viral vaccination and inoculation studies.

desk verdict A substantial curated resource that is probably useful, but the paper's central promise depends on subject-level labels and ID links that aren't audited; worth serious review. read the letter →

arxiv 2505.14725 v2 pith:QDC5PVWH submitted 2025-05-19 q-bio.GN cs.LGstat.AP

Xuejun Sun , Yiran Song , Xiaochen Zhou , Ruilie Cai , Yu Zhang , Xinyi Li , Rui Peng , Jialiu Xie
show 9 more authors
Yuanyuan Yan Muyao Tang Prem Lakshmanane Baiming Zou James S. Hagood Raymond J. Pickles Didong Li Fei Zou Xiaojing Zheng
This is my paper · ORCID
classification q-bio.GNcs.LGstat.AP
keywords HR-VILAGE-3K3Msystemsimmunologylongitudinaltranscriptomicsvaccineresponsepredictiondataharmonizationbatcheffectcorrectionsingle-cellRNA-seqbenchmarkdataset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces HR-VILAGE-3K3M, a public, AI-ready dataset built by merging 14,136 longitudinal RNA-seq profiles from 3,178 subjects in 66 studies of respiratory viral vaccination and controlled infection. The authors' central claim is that after harmonizing gene symbols, sample identifiers, subject metadata, and outcome definitions, and after removing duplicate and technically corrupted samples, these scattered public datasets form one dependable resource for systems-immunology research. The value of the claim, if correct, is that researchers can skip the time-consuming step of data cleaning and ID matching and go directly to training and benchmarking predictive models, testing batch-effect corrections, and studying immune trajectories over time. The paper also demonstrates the resource with two analyses: predicting antibody responder status after influenza vaccination with machine-learning models, and comparing bulk and single-cell estimates of immune cell-type changes after COVID-19 vaccination.

What carries the argument

The load-bearing mechanism is the curation and harmonization pipeline rather than any single statistical model. Its parts are: subject-level metadata reconstruction, including resolving sample-ID mismatches and emailing study authors for missing antibody titers; standardized preprocessing, with RMA and quantile normalization for microarray data, read alignment and count generation for RNA-seq, and total-count-plus-log1p normalization for single-cell data; gene-symbol correction to official HGNC names, using a tool that repairs invalid or outdated symbols; duplicate-sample detection through protocol review and Spearman correlation of expression profiles; and standardized outcome definitions, notably the HAI-titer maximum-fold-change thresholds for influenza vaccine response. These steps convert raw, scattered depositions into expression matrices whose row names align with metadata and whose outcome labels are comparable across studies; every downstream example analysis depends on this alignment.

What would settle it

Take a random sample of studies in HR-VILAGE-3K3M, retrieve the original supplementary clinical tables and any author-provided antibody files, and independently reconstruct subject-to-sample links and responder labels; if mismatches are found at a non-negligible rate, the outcome labels used in Section 3 are unreliable. A quick quantitative check is to rerun the responder-prediction benchmark excluding all studies whose antibody data had to be obtained by email and see whether the reported F1 and AUC hold.

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Extended reading notes

Core claim

The core claim is infrastructural: that a carefully curated integration of existing public transcriptomic studies can serve as a standardized benchmark for the human immune response to respiratory viral immunization. Specifically, the authors report assembling 3,178 participants—462 from 15 inoculation studies covering H3N2, H1N1, HRV, RSV, and SARS-CoV-2, 2,412 from 47 influenza and COVID-19 vaccination studies, and 304 from four mixed-exposure studies—profiled at up to 22 time points across whole blood, PBMCs, and nasal swabs, using microarray, bulk RNA-seq, and 10x single-cell platforms. They standardize gene symbols to official HGNC names, assign unique GEO accession identifiers to samples, define a consistent subject identifier, and set common outcome criteria: for influenza vaccines, high responders are those with maximum HAI fold change at least 4 and a Day-28 titer at least 40; non-responders have fold change at most 1; COVID-19 vaccine studies carry raw antibody data so users can choose their own thresholds. In their benchmark on 20 influenza vaccine studies with 1,268 participants, PCA-based logistic regression after quantile normalization achieved F1 0.822, AUC 0.748, and accuracy 0.726, surpassing RNN, LSTM, GRU, and Transformer models, with the QN-corrected Transformer the best deep model (F1 0.804). The paired bulk and single-cell demonstration for two SARS-CoV-2 vaccine studies shows both modalities detect a rise in B cells from Day 1 to Day 7 and a fall in monocytes and NK cells.

Load-bearing premise

The entire resource depends on the correctness of the manually resolved subject-level metadata and outcome labels, especially antibody titers obtained by emailing study authors and sample IDs matched by hand, since an ID mapping error or a wrong responder label would silently corrupt every expression-to-outcome link and every benchmark built on it.

Editorial extensions

If this is right

  • Researchers can train and compare models for early prediction of vaccine response on a common set of 20 influenza studies with harmonized responder labels; the paper's own benchmark shows quantile normalization giving better results than ComBat or regression batch correction.
  • The paired bulk and single-cell datasets from the same subjects give deconvolution methods a check: cell proportions from scRNA-seq can be compared with enrichment scores from bulk RNA-seq at matching time points.
  • The platform-structured missingness of genes, where availability is determined by study and platform, provides a benchmark for imputation methods that do not assume missing-at-random.
  • Irregular sampling times and multiple time points per subject make the dataset suitable for change-point detection and causal inference over immune trajectories.
  • At its stated scale, the dataset is positioned as pretraining material for foundation models of immune response, with downstream fine-tuning for outcome prediction or transfer to smaller studies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if the dataset becomes a standard benchmark, the paper's binary responder definition (MFC at least 4 and titer at least 40) may harden into a default even though antibody responses are continuous; using a threshold discards information about moderate responders.
  • Editorial inference: the strongest test of the curation is reproducibility of outcome labels; an independent re-derivation of antibody titers from original study supplements and author correspondence would either confirm or undermine every supervised benchmark in Section 3.
  • Editorial inference: because the benchmark found a simple PCA-logistic model beating all deep sequence models, a likely downstream effect is that researchers will treat quantile normalization plus logistic regression as a strong baseline that any longitudinal deep-learning claim must surpass on this resource.
  • Editorial inference: the paired bulk and single-cell datasets cover PBMCs from COVID-19 vaccine studies; extending the same paired design to nasal swabs would test whether local mucosal immune dynamics track systemic blood dynamics, a question the current resource cannot answer.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper introduces HR-VILAGE-3K3M, a curated longitudinal transcriptomic repository for human respiratory viral immunization studies, integrating 14,136 RNA-seq profiles from 3,178 subjects across 66 studies, with additional single-cell data reported as over 2.56 million cells. The authors describe a detailed curation workflow: collection from GEO, ImmPort, and ArrayExpress; harmonization of gene symbols; resolution of duplicate samples; standardized preprocessing for microarray, bulk RNA-seq, and scRNA-seq; and definition of responder status for influenza vaccine studies based on HAI titers. To demonstrate utility, they report two analyses: a benchmark of batch-effect correction methods and deep-learning sequence models for antibody responder prediction on 20 influenza vaccine studies, and a paired bulk/scRNA-seq cell-type annotation analysis of SARS-CoV-2 vaccine samples. The paper's central claim is that this resource is an AI-ready, rigorously curated benchmark for systems immunology, with data and code publicly hosted.

Significance. If the hosted dataset is complete and correctly curated, HR-VILAGE-3K3M would be a substantial and useful integrative resource for systems immunology, filling a gap in harmonized longitudinal transcriptomic data for respiratory viral immunization. The paper's strengths include the open availability of data and code, the explicit attempt to standardize outcome definitions across studies, the reporting of preprocessing details, and the inclusion of paired bulk and single-cell analyses. The benchmark analyses, while internal, provide a reasonable illustration of possible downstream uses. However, the central claims cannot be fully assessed from the manuscript alone because the hosted artifact is not independently auditable from the text: the paper lacks a manifest or per-study count table, and the subject-level outcome labels that underpin all benchmarks rest on ID-mapping and antibody-curation steps whose error rates are not reported. These are fixable with additional documentation and validation, but they are load-bearing for the paper's main contribution.

major comments (5)
  1. [Section 2, Summary (and Appendix A)] The aggregate counts (3,178 subjects, 14,136 profiles, 66 studies) and the abstract's 'over 2.56 million cells' are the paper's central quantitative claims, but the manuscript provides no per-study count table, no manifest of the hosted files, and no checksums or scripts that regenerate these totals from the Hugging Face release. Because the dataset itself is the contribution, please add a machine-readable manifest with per-study accessions, platform, subject/sample/cell counts, and a reproducibility script that recomputes the aggregate numbers from the released files.
  2. [Section 2, Outcome definition] The responder labels used in all Section 3 benchmarks are defined as MFC≥4 with Day-28 HAI≥40 (high responders) and MFC≤1 (non-responders), but the paper does not document how each of the 20 benchmark studies satisfies the Day-28 measurement requirement or how assay differences (e.g., hemagglutination inhibition assay variants, egg- versus cell-based antigens, different laboratories) were harmonized. Without a per-study endpoint table specifying the exact assay, the measurement day, and the numbers reclassified from original study definitions, the cross-study responder labels and the reported AUC/F1 differences are not auditable.
  3. [Section 1 and Section 2, Data quality control] The paper states that metadata sample IDs differ from expression-data IDs in several instances and that merging required significant human effort, including contacting study authors, yet no reconciliation log, error count, or independent validation of subject-to-sample and subject-to-antibody links is reported. Since every benchmark in Section 3 depends on these links, an undetected ID mapping error would propagate directly into the responder prediction results. Please release the mapping tables (or appropriately de-identified versions) and describe validation checks, such as sex/age consistency, duplicate-sample Spearman verification, and any resulting exclusion rates.
  4. [Section 3, Outcome Prediction Using Longitudinal Bulk Data] The benchmark cohort of 20 influenza vaccine studies and 1,268 participants is selected to be 'balanced' and to exclude studies with 'extremely unbalanced' responder distributions, but the exact selection criteria and per-study responder counts are not reported. Moreover, the modeling results are evaluated internally on the same resource and do not constitute external validation. Please provide the full inclusion/exclusion audit trail, per-study responder counts, and explicit statements framing these analyses as internal utility demonstrations rather than validated predictive performance.
  5. [Section 3, Figure 2b] The conclusion that QN 'consistently outperformed' ComBat and Regression is based on mean metrics averaged over six random seeds, but Figure 2b does not show error bars, standard deviations, or statistical tests. Because the reported differences (e.g., Transformer AUC 0.727 vs. 0.669) may be within seed-level variability, please provide per-seed results, confidence intervals, or paired significance tests before asserting consistent superiority of one normalization method.
minor comments (5)
  1. [Figure 2 caption and Section 4 title] The name 'HV-RIGEL-3K3M' appears in the Figure 2 caption and the Section 4 heading, whereas the dataset is consistently called 'HR-VILAGE-3K3M' elsewhere; please unify the spelling.
  2. [Appendix D, E, F] The main text refers to 'section D', 'section E', and 'section F', but the appendix sections are not explicitly numbered; please number them or use descriptive cross-references.
  3. [Section 2, Summary and abstract] The abstract reports 'over 2.56 million cells', but Section 2 does not provide per-study cell counts or define which seven single-cell studies contribute to this total; please add a per-study cell count summary.
  4. [Appendix B] Appendix B lists reference numbers without a mapping between GEO/ImmPort accessions and the corresponding dataset references; a table linking accession, reference, platform, and inclusion status would greatly improve traceability.
  5. [Appendix F] The single-cell annotation details mention GSE201534 and GSE246937, but GSE246937 does not appear elsewhere in the main text; please ensure the accessions are consistent and cross-referenced.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the repository's construction and illustrative benchmarks do not reduce to their own inputs.

full rationale

The paper's central contribution is a curated resource assembled from external public repositories (GEO, ImmPort, ArrayExpress) and antibody data obtained from study authors; no claim is defined in terms of the benchmark outputs. The Section 3 responder labels come from an independent external antibody threshold (MFC>=4 with Day-28 HAI>=40 for high responders; MFC<=1 for non-responders), not from gene expression, so outcome prediction is a genuine supervised task rather than a fitted-input prediction. Preprocessing (RMA, QN, ComBat, regression, log transforms, HGNC symbol harmonization, duplicate removal) is standard and applied uniformly; no fitted parameter is renamed as a prediction. The example analyses are explicitly framed as illustrations of utility, not as external validation of the resource, so using the same dataset for demonstration is not circularity. Reference [24] is an overlapping-author prior collection that the current work extends; this self-citation documents provenance and is not load-bearing for any result. Remaining concerns, such as the absence of an audited sample-ID reconciliation log or checksum manifest and possible leakage from pre-CV PCA or gene filtering, are data-reliability and methodological risks, not circular derivations. No equation in the paper is equivalent to its input by construction.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central resource claim depends mainly on assumptions about metadata correctness and redistribution rights. The benchmark analyses add hand-set thresholds and model hyperparameters, but these do not create the data itself.

free parameters (4)
  • Influenza high-responder definition thresholds = MFC >= 4 and Day 28 HAI titer >= 40
    Hand-chosen outcome criteria applied to all influenza vaccine studies; the benchmark labels in Section 3 depend on these thresholds.
  • Early immune response window cutoff = day 28
    Samples after day 28 are excluded from the prediction benchmark, so all reported performance is conditional on this cutoff.
  • Low-expression gene filter = bottom 25% of mean and variance
    Applied in benchmark preprocessing to reduce noise, which changes the feature set used for prediction.
  • Number of principal components = 10
    Used in the PCA-logistic baseline and described as explaining over 80% of variance; a modeling choice.
assumptions (3)
  • domain assumption Reconciled sample IDs and outcome labels obtained from original study authors are correct.
    Section 2 describes manual reconciliation of sample identifiers and email requests for antibody data; any error here would propagate through all downstream analyses.
  • domain assumption Public data from GEO, ImmPort, and ArrayExpress can be redistributed under the repository's terms.
    The paper states the data come from public repositories with appropriate usage permissions, but it does not list specific licenses for each source study.
  • domain assumption Standard normalization methods (RMA, quantile normalization, log2 transformation) preserve comparable biological signal across platforms.
    The harmonization pipeline relies on these methods to make microarray, bulk RNA-seq, and single-cell data comparable, which is a reasonable but unproven assumption.

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Cite this review

Pith. "Pith review of HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity." pith.science (2026). https://pith.science/paper/QDC5PVWH

@misc{pith2026250514725,
  author       = {Pith},
  title        = {Pith review of: HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QDC5PVWH}},
  note         = {Machine review of arXiv:2505.14725}
}
read the original abstract

Respiratory viral infections pose a global health burden, yet the cellular immune mechanisms underlying protection and pathology remain unclear. Natural infection cohorts often lack pre-exposure baselines and time-controlled sampling, whereas inoculation and vaccination trials generate well-structured longitudinal transcriptomic data. However, these datasets are scattered across repositories and processed inconsistently, hindering integrative and AI-driven analyses. To address these challenges, we developed the Human Respiratory Viral Immunization LongitudinAl Gene Expression (HR-VILAGE-3K3M) repository: an AI-ready resource integrating bulk and single-cell transcriptomic profiles from 3,178 subjects across 66 studies. The dataset spans vaccination, inoculation, and mixed exposures, with samples from blood and nasal swabs collected from public repositories including GEO, ImmPort, and ArrayExpress. We curated and harmonized subject-level metadata, standardized outcome measures, and applied unified preprocessing with rigorous quality control. We further provide benchmark analyses illustrating its utility. This resource supports discovery of biomarkers, immune mechanisms, and methodological development. As one of the largest longitudinal transcriptomic resources for human respiratory viral immunization, HR-VILAGE-3K3M enables reproducible and scalable analyses to accelerate vaccine and antiviral research.

Figures

Figures reproduced from arXiv: 2505.14725 by the authors.

Figure 1
Figure 1. Overview of HR-VILAGE-3K3M. (a) HR-VILAGE-3K3M construction workflow. (b) [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Evaluation of Batch Effect Correction and Predictive Modeling on the HV-RIGEL-3K [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Comparison of immune cell-type composition and transcriptional activity using paired [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Potential tasks using HR-VILAGE-3K3M. (a) Change point detection. (b) Gene expression [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.