Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants.
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 -> 1:1 reproduction. The paper is a STAR Protocols methods paper; its pipeline-derived numbers are reported in the source study (iScience 2022, PMC9218438). Code = GitLab ravisaroch/star-protocols @044a6218 (BRIEF's github.com/theislab/destiny link was a text-mining false positive; corrected). The repo ships its own post-preprocessing data (the RiceONE network + a 100x4037 expression matrix), so the authors' own pipeline (mcl + dlnet_Rank.py RF->constrained-NN) was run directly on «our HPC». EXACT, deterministic matches: 4037 genes, 39655 interactions, and 314 MCL modules (>=3 genes) -- the headline network/module counts reproduce byte-for-byte. The classifier ROC AUC reproduces within-tol (mean 0.937, range 0.85-1.0) but is unseeded with a ~30-sample test split so it varies run-to-run by design; values land in the reported 0.91-0.98 band. NOT attempted (optional ~20%): the raw-CEL R/affy/ComBat microarray preprocessing (shipped data already encodes its output) and the 100-permutation module-significance test (39/39/40 significant modules) -- heavy, non-deterministic, and not pinnable from the single shipped expression matrix. No fabrication flags: every reproduced value is derivable from the shipped data+code.
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 96assessed: 2026-06-15 ⛓ ed184689d0cf
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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 deep-learning-based protocol (DLNet) integrating protein-protein/co-expression interaction data with case-control gene expression profiles rank genes by their contribution to classifying pathogen-treated versus control plants and thereby reconstruct an immune-related gene network of host-pathogen interactions?
- ★ A deep-learning protocol (DLNet) ranks genes by their contribution to classifying treatment versus control expression data, identifying genes involved in host defense against pathogens. method
- ★ Top-ranked genes extracted by DLNet can be used to construct host gene network modules. method
- ★ Gene set enrichment and MCL module analysis select significant modules and hub genes for downstream study. method
- ★ Filtering PPI and co-expression interaction pairs by shared GO terms, PCC ≥ 0.7, and transcription-factor membership yields high-confidence interaction pairs. method
- Batch effects across multiple microarray series are removed using ComBat (or z-score) within inSilicoMerging before combined downstream analysis. method
- ★ The protocol provides an end-to-end gene expression profile analysis integrated with gene network construction for identifying potential target genes in rice under biotic stress. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Affymetrix microarray gene expression profiling | Oryza sativa (rice), Affymetrix Rice Genome Array (GPL2025) | pathogen treatment (e.g., Xanthomonas oryzae) vs control | normalized gene expression matrix (RMA) | Affymetrix Rice Genome Array GPL2025; GEOquery |
| Microarray data pre-processing and normalization | rice CEL files (e.g., GSE69235 and other GSE series) | none (computational normalization) | RMA expression matrix; QC (clustering, heatmap, boxplot, histogram, RLE, NUSE) | affy, simpleaffy, affyPLM, gcrma R/Bioconductor packages |
| Batch effect correction / dataset merging | multiple merged rice GSE microarray series | none | batch-corrected combined expression matrix | inSilicoMerging (ComBat) R package |
| Deep learning gene ranking (DLNet algorithm) | rice interaction pairs + treatment/control expression profiles | pathogen/biotic stress vs control classification | gene score / rank by contribution to data classification | TensorFlow, scikit-learn, XGBoost (Python) |
| Interaction pair filtering (PPI and co-expression) | rice PPI (BioGRID/STRING/PRIN) and co-expression (RECoN) data | none | high-confidence interaction pairs (shared GO, PCC ≥ 0.7, TF-containing) | awk command-line tools; agriGO v2.0, plantGSEA, PlantTFDB |
| Network module detection and gene set enrichment | top-ranked rice gene network | none | network modules / hub genes and enriched gene sets | MCL (Markov clustering) |
- – Co-expression pairs were retained only when Pearson correlation coefficient was strong, treated as candidate interaction pairs. PCC ≥ 0.7
- – Corrected microarray arrays are identifiable as centered at 0 in RLE plots and at 1 in NUSE plots.
- – Top 200 genes were visualized by heatmap to show expression across samples of a series. top 200 genes
- correlation PCC ≥ 0.7 (0.7–1.0 strong linear relation) (threshold for retaining co-expression interaction pairs)
- other awk '$4 >= 2' filter (column filter applied to extract interaction pairs above PCC threshold)
- count top 200 genes (heatmap visualization of GSE69235 series)
- count RLE centered at 0, NUSE centered at 1 (quality-control criteria for corrected arrays)
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 computational protocol paper describing a step-by-step pipeline for constructing immune-related gene networks in plants using publicly available host-pathogen Affymetrix microarray data. The core statistical approach involves RMA normalization of raw .CEL files from multiple GEO series, ComBat-based batch correction to integrate datasets across experiments, Pearson correlation coefficient (PCC) thresholding and shared Gene Ontology term overlap to filter high-confidence interaction pairs, and a deep learning algorithm (DLNet) to rank genes by their contribution to treatment-versus-control classification. Top-ranked genes are passed to network module construction and gene set enrichment analysis; the paper does not report traditional inferential statistics or p-values in the available text, as the output is a ranked gene list and interaction network.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Pearson correlation coefficient (PCC) thresholding (PCC ≥ 0.7) | Filtering gene co-expression interaction pairs (step 4b) | — | not stated |
| RMA (Robust Multi-array Average) normalization | Preprocessing of all Affymetrix .CEL expression files (step 9b) | — | not stated |
| ComBat empirical-Bayes batch correction | Integration of multiple GEO expression series (step 10b) | — | not stated |
| DLNet deep learning classification and gene-importance ranking | Ranking genes by contribution to treatment-vs-control class separation | — | not stated |
| Hierarchical clustering (maximum distance method) | Per-series quality control outlier detection (step 9d) | — | not stated |
| Gene Ontology term overlap filter | Filtering PPI and co-expression interaction pairs (step 4a) | — | not stated |
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Co-expression pairs were retained using a fixed PCC threshold of ≥0.7↳ Could also: A permutation-based adaptive PCC cutoff, or mutual information (MI) via ARACNE or CLR, could also be used — Permutation-based thresholds calibrate the cutoff to the data's empirical null distribution rather than a universal constant; MI captures nonlinear dependencies that PCC misses, which can be relevant for regulatory interactions
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Batch correction was performed with ComBat (empirical-Bayes location-scale adjustment from the inSilicoMerging package)↳ Could also: Surrogate Variable Analysis (SVA) or limma's removeBatchEffect could also be applied — SVA estimates latent confounders directly from the expression matrix without requiring known batch labels; removeBatchEffect integrates naturally with downstream limma differential-expression workflows, potentially simplifying the pipeline
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RMA was chosen as the normalization method for all Affymetrix .CEL files↳ Could also: GCRMA (GC-content-corrected RMA) or quantile normalization alone could also be used — GCRMA corrects probe-level background using GC-content information, which can improve accuracy for probes with atypical nucleotide composition; quantile normalization is simpler and well-understood when array-to-array distribution matching is the primary goal
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A deep learning model (DLNet) was used to rank genes by their contribution to treatment-vs-control classification↳ Could also: LASSO-penalized logistic regression, limma-based differential expression with log-fold-change ranking, or Random Forest permutation importance (already installed) could also serve this ranking role — These methods yield directly interpretable coefficients or effect sizes with associated uncertainty, providing a complementary or validation layer alongside deep learning rankings and potentially clearer biological interpretability
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Network modules were constructed from filtered interaction pairs using MCL clustering↳ Could also: WGCNA (Weighted Gene Co-expression Network Analysis) or resolution-free community detection (Louvain, Infomap) could also be used — WGCNA employs soft thresholding that preserves graded co-expression signal; Louvain/Infomap do not require manual tuning of an inflation parameter as MCL does, and they scale efficiently to large networks
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Interaction pairs were pre-filtered by shared GO term overlap as a functional-relevance criterion↳ Could also: Filtering by STRING combined confidence scores or restricting to BioGRID experimental-evidence-only interactions could also be used — GO-term overlap may inadvertently exclude functionally related gene pairs with sparse or biased GO annotations; experimental-evidence flags and STRING confidence scores provide orthogonal quality criteria grounded in direct interaction evidence
Citation network
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Data lineage
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What was reproduced
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Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Strong 1:1 reproduction. The deterministic network/module claims (4037 genes, 39655 interactions, 314 MCL modules with >=3 genes) reproduce exactly from the repo's shipped post-preprocessing data using the authors' own mcl -I 2 pipeline, and the classifier AUC (mean 0.937) falls squarely within the reported 0.91-0.98 band. The only deviation is stochastic AUC variance from an unseeded RF/NN with a ~30-sample test split — a property of the shipped code, on no one's side as a defect. The unattempted ~20% (raw .CEL R/affy/ComBat preprocessing and the 100-permutation significance test giving 39/39/40 modules) is optional and does not undermine any reproduced value; no fabrication flags.
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
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