Disease-first public-data integration with local virtual knockout prioritizes shared proteins linking osteoarthritis and osteoporosis

Jiayin Pang; Yu Li; Zhichao Qi; Wenbin Yang

doi:10.1371/journal.pone.0359583

. 2026 Oct 1;21(10):e0359583. doi: 10.1371/journal.pone.0359583

Disease-first public-data integration with local virtual knockout prioritizes shared proteins linking osteoarthritis and osteoporosis

Jiayin Pang ¹, Yu Li ^2,³, Zhichao Qi ⁴, Wenbin Yang ^4,^*

Editor: Jung-Eun Kim⁵

PMCID: PMC13630263 PMID: 42821566

Abstract

Osteoarthritis and osteoporosis frequently coexist in older adults, but shared molecular programs remain unclear. We developed a disease-first multilayer public-data integration framework to identify shared protein candidates while preserving disease-specific structure before cross-disease comparison. Public bulk transcriptomic datasets from osteoarthritis synovium and cartilage and osteoporosis-related monocytes, femoral bone, and osteogenic stromal compartments were analyzed independently within each disease. Disease-level signatures were generated using differential expression analysis, robust rank aggregation, functional enrichment, and weighted gene co-expression network analysis. A shared axis was defined by concordant dysregulation, shared biological processes, and criteria-positive coexpression-module correspondences, with module matching subsequently calibrated against a size-preserving null model. Candidate genes were mapped to proteins and prioritized by integrating interaction topology, signaling priors, protein annotation, tissue support, disease-association and genetic evidence, local CARNIVAL virtual knockout, and single-cell contextual perturbation analysis. Both diseases showed reproducible signatures enriched in antigen presentation, cytokine regulation, extracellular matrix organization, osteoclast differentiation, ossification, and bone remodeling. Cross-disease comparison yielded 234 criteria-positive module pairs; 53 retained pair-specific support at empirical FDR < 0.05, whereas the total number of criteria-positive pairs did not exceed the global null expectation. Protein-level integration prioritized HLA-DRB1, HSP90AA1, CTSK, RPL7, PRG4, HLA-DRA, CLEC3B, TIMP1, SPP1, and APOE. Local virtual knockout assigned the highest model-derived CARNIVAL scores to HLA-DRB1 and HSP90AA1. Across the evaluated network configurations, HLA-DRB1 exceeded the prespecified score threshold in all four evaluable configurations, whereas HSP90AA1 exceeded it in four of six, indicating greater configuration stability for HLA-DRB1. Primary matched-null calibration of 39 evaluable candidate-compartment pairs retained nine pairs at global FDR < 0.05. External quality-control sensitivity analysis retained 125,090 of 161,470 cells and identified 10 globally FDR-supported pairs. Although only two of the nine primary pairs remained supported in the same compartment, five of the six primary supported genes retained evidence in at least one compartment. Recalculation using the quality-controlled cell-context evidence retained all primary top 10 proteins, with HLA-DRB1 and HSP90AA1 remaining the two highest-ranked candidates. Composition-aware bulk sensitivity analysis retained the direction of 36 of 40 candidate–cohort effects, while matched transcriptional-program adjustment retained 24 of 27 effects and showed greater attenuation of MHC-II candidates than of ribosomal or protein-folding candidates. These findings support a shared osteoimmune–matrix-remodeling protein axis linking osteoarthritis and osteoporosis and provide candidates for future experimental validation.

Introduction

Osteoarthritis (OA) and osteoporosis (OP) are among the most common musculoskeletal disorders in ageing societies. Clinically, OA and OP frequently coexist in the same individual. Traditionally, OA has been regarded as a disease of articular cartilage wear or degeneration, whereas OP has been viewed as a systemic skeletal metabolic disorder characterized by reduced bone mass and increased bone fragility. Earlier studies on the relationship between OA and OP proposed an inverse association, suggesting that patients with OA may have higher bone mineral density and a lower risk of OP [1]. However, recent reviews indicate that the relationship between these two conditions is not simply antagonistic, but is influenced by age, sex, body mass index (BMI), skeletal site of measurement, disease stage, bone quality, mechanical loading, and inflammatory status [2].

High-quality evidence in recent years has established that OA is not merely a disease of cartilage wear, but rather a heterogeneous “whole-joint disease” involving cartilage, synovium, subchondral bone, infrapatellar fat pad, meniscus, ligaments, and periarticular muscles. Its onset and progression are driven by multiple factors, including mechanical stress, metabolic abnormalities, low-grade inflammation, cellular senescence, oxidative stress, impaired autophagy, and pain-related neuroregulation [3–6]. The central pathological feature of OP is an imbalance in bone remodeling, in which osteoclast-mediated bone resorption exceeds osteoblast-mediated bone formation. Ageing, estrogen deficiency, chronic inflammation, oxidative stress, immune dysregulation, and altered lineage commitment of bone marrow mesenchymal stem cells all contribute to the development of OP. Particularly in older individuals, the immune system and bone remodeling system are closely interconnected through signaling pathways involving RANKL/RANK/OPG, TNF-α, IL-1β, IL-6, NF-κB, and NFATc1, collectively forming the pathological basis of osteoimmunology [7–9]. Therefore, although OA and OP differ in their tissue-level phenotypes, they may share overlapping mechanisms related to cellular stress, inflammation, senescence, autophagy, and dysregulated tissue remodeling. OA complicated by OP may represent a distinct osteoarticular comorbidity phenotype, in which the underlying mechanisms are not a simple summation of OA and OP, but rather the convergence of stress responses, inflammation, and abnormal bone–joint remodeling in the context of ageing.

Despite this emerging concept, defining the shared biology linking OA and OP remains challenging. OA studies are commonly based on synovium or cartilage, whereas OP studies often involve circulating monocytes, femoral bone, or osteogenic stromal compartments. In the present study, we sought to address this problem by developing a disease-first, multilayer public-data integration framework to investigate shared OA-OP pathology. Rather than merging heterogeneous datasets at the outset, we first characterized robust disease signatures within OA and within OP, and then used multilayer evidence to identify a shared pathological axis linking the two conditions. On this basis, we further prioritized candidate proteins with support from network context, disease relevance, and cellular localization. The aim of the study was to derive a biologically interpretable set of shared OA-OP protein candidates that may be useful for future mechanistic and translational investigation.

Results

Disease-first cohort organization preserved within-disease structure before cross-disease inference

The study design and included datasets are summarized in Fig 1A–D and Table 1. The OA bulk layer included GSE55235, GSE55457 [10], GSE82107 [11], and GSE117999 [12], spanning synovium and cartilage. The OP bulk layer included GSE56814, GSE56815 [13], GSE35958 [14], and GSE230665 [15], spanning circulating monocytes, femoral bone, and osteogenic stromal compartments. The single-cell layer included GSE216651 [16] and GSE152805 [17] for OA-related context and GSE147287 [18] for OP-related context, with CELLxGENE [19] used as an auxiliary reference for cell-type interpretation.

Table 1. Public datasets included in the study. Summary of bulk and single-cell public datasets used in the disease-first workflow, including their analytical role in the manuscript.

Dataset	Disease	Layer	Material / focus used in study	Platform	Sample summary	Use in manuscript
GSE55235	OA	bulk	synovium	GPL96	10 OA / 10 healthy	Bulk differential analysis and OA RRA
GSE55457	OA	bulk	synovium	GPL96	10 OA / 10 healthy	Bulk differential analysis and OA RRA
GSE82107	OA	bulk	synovium	GPL570	10 OA / 7 healthy	Bulk differential analysis and OA RRA
GSE117999	OA	bulk	cartilage	Illumina array	12 OA / 12 healthy	Bulk differential analysis and OA RRA
GSE56814	OP	bulk	circulating monocytes	GPL5175	31 low BMD / 42 high BMD	Bulk differential analysis and OP RRA
GSE56815	OP	bulk	circulating monocytes	GPL96	40 low BMD / 40 high BMD	Bulk differential analysis and OP RRA
GSE35958	OP	bulk	osteogenic mesenchymal stromal cells	Array	5 OP / 4 control	Bulk differential analysis and primary OP RRA; not eligible for WGCNA because n = 9.
GSE230665	OP	bulk	femoral bone	GPL10332	12 OP / 3 control	Bulk differential analysis and OP RRA
GSE216651	OA	single-cell	IPFP synovium; OA fibroblast/macrophage perturbation	GPL24676	5 OA synovium / 4 healthy synovium donors	Primary OA single-cell case-control cohort and external quality-control sensitivity analysis
GSE152805	OA	single-cell	paired OA synovium-cartilage atlas; tissue-state support only	GPL20301	paired OA synovium-cartilage atlas; no healthy/control comparator	Descriptive OA atlas support and external quality-control cell-retention assessment (not used for case-control perturbation)
GSE147287	OP	single-cell	bone marrow; BM-MSC and osteoclast-precursor perturbation	GPL24676	1 OP marrow group / 1 OA marrow comparator group	Contextual OP marrow analysis and external quality-control sensitivity analysis for BM-MSCs and osteoclast precursors
CELLXGENE CENSUS	Reference	single-cell	bone marrow/synovium reference atlas for annotation	Census	reference atlas	Reference for annotation transfer and baseline context

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All bulk cohorts contributed to cohort-level differential analysis and disease-specific RRA; WGCNA was performed only within eligible tissue- and platform-consistent blocks.

All cohorts were processed within disease before any cross-disease integration. OA and OP were modeled separately at the differential-expression and network-construction stages, and cross-disease comparison was introduced only after disease-level signatures or disease-specific network features had been derived (Fig 1B–D).

OA and OP each exhibited robust disease signatures before shared-axis construction

Independent limma-based differential analyses followed by disease-level robust rank aggregation yielded reproducible signatures for OA and OP (Fig 2A–D). In OA, top-ranked robust genes included SON, TTC3, GRB10, PRRC2C, and LRRFIP1, whereas in OP the top-ranked genes included NCOA1, DOK1, SLC43A3, TMEM14B, INTS6, and PIAS1. Representative narrative-anchor genes in OA were concentrated in antigen presentation, inflammatory signaling, wound response, and matrix- or bone-interface biology, whereas OP anchors were concentrated in immune activity, myeloid signaling, antigen presentation, and bone remodeling.

Fig 2 — (A) OA signature genes prioritized by integration across four OA bulk cohorts. The panel presents the highest-ranked genes and selected genes representing antigen presentation, inflammatory signaling, and matrix or tissue-response processes. (B) OP signature genes prioritized across four OP bulk cohorts, together with selected genes representing bone remodeling, immune and antigen-presentation activity, and bone or matrix adaptation. (C) Representative Gene Ontology biological processes enriched in the OA signature. (D) Representative Gene Ontology biological processes enriched in the OP signature. (E) Cross-cohort effects of the displayed genes. Cell color indicates the signed within-dataset effect, asterisks indicate cohort-level FDR < 0.05, and NA indicates unavailable mapping. Complete cohort-level gene and pathway results are provided in the accompanying data files. OA, osteoarthritis; OP, osteoporosis; GO, Gene Ontology; FDR, false discovery rate.

At the functional level, OA robust signatures were enriched in antigen processing and presentation, positive regulation of cytokine production, positive regulation of tumor necrosis factor production, wound healing, extracellular matrix organization, collagen fibril organization, and regulation of ossification (Fig 2C). OP robust signatures were enriched in mononuclear cell differentiation, regulation of T-cell activation, antigen processing and presentation of peptide antigen, positive regulation of cytokine production, myeloid leukocyte cytokine production, osteoclast differentiation, regulation of ossification, and ossification (Fig 2D).

Across individual bulk cohorts, representative OA and OP genes showed recurrent directional behavior in the same overall direction after within-disease integration, although effect magnitude varied by cohort and tissue context (Fig 2E). These results established reproducible disease-level transcriptional signatures before cross-disease comparison.

Exclusion of GSE35958 yielded rank correlations of 0.916 for the OP RRA and 0.895 for the shared axis relative to the primary analysis, with 5/20 and 10/20 primary top-ranked features retained, respectively (S1 Fig and S1 Table). Layer 1 + 2 and Layer 1 + 2 + 3 candidate counts changed from 5,481–5,391 and from 810 to 804, respectively. In the conditional protein-level comparison, rank correlation was 0.918 and 8 of the primary top 10 proteins were retained; HSP90AA1 and RPL7 no longer met the cross-disease direction-consistency criterion.

A three-layer framework integrated shared OA-OP evidence

Shared OA-OP pathology was defined using three ordered evidence layers (Fig 3A–B). Layer 1 retained genes with concordant direction of dysregulation between OA and OP after disease-level integration. Layer 2 further restricted candidates to genes supported by biological processes enriched in both diseases. The resulting shared pathway space was concentrated in immune and inflammatory activity, antigen presentation, extracellular-matrix organization, collagen-related structure, and bone-remodeling or ossification-related processes (Fig 3B).

Layer 3 incorporated disease-associated coexpression structure derived independently in OA and OP (Fig 3C). Across 464 tested OA-OP module combinations, 234 pairs met the prespecified deterministic matching criteria (Fig 3D). In 10,000 block-stratified, size-preserving permutations, 53 of these pairs retained pair-specific support at empirical FDR < 0.05, and an independent repeat using a second random seed recovered the same 53 pairs (Fig 3D; S2 Fig; S3 Table). The total number of criteria-positive pairs did not exceed the global null expectation (observed, 234; null mean, 254.83; 95% interval, 242–267; upper-tail empirical P = 0.9995), indicating that module-level evidence was concentrated in selected pair-specific correspondences rather than a globally enriched OA-OP module-matching architecture.

Protein-level integration prioritized proteins across complementary evidence dimensions

Shared-axis candidates were mapped to proteins and integrated with STRING interaction topology [20], OmniPath signaling priors [21], UniProt annotation [22], Human Protein Atlas tissue support [23], Open Targets evidence [24], DisGeNET evidence [25], and direct GWAS Catalog associations [26] (Fig 4, Table 2). The primary integrated ranking prioritized HLA-DRB1, HSP90AA1, CTSK, RPL7, PRG4, HLA-DRA, CLEC3B, TIMP1, SPP1, and APOE (Fig 4A, Table 2).

Fig 4 — (A) Integrated ranking of the top 15 proteins according to the composite priority score, with the three highest-ranked proteins highlighted. (B) Profiles of the six normalized evidence dimensions for the top 10 proteins. Values correspond to those reported in Table 2, and right-side labels identify the largest evidence dimension for each protein. (C) OmniPath neighborhood of the leading proteins. Colored nodes indicate prioritized proteins, gray nodes indicate shared connector genes, and directed edges indicate curated signaling relationships. (D) Genetic-evidence and protein-feasibility profiles of the leading proteins. Point size represents cell-type specificity, and asterisks identify proteins with model-derived CARNIVAL score of at least 0.1 in the selected CARNIVAL configuration. OA, osteoarthritis; OP, osteoporosis. Degree- and annotation-aware sensitivity results are shown in S6 Fig and S7 Table.

Table 2. Top 10 prioritized proteins in shared OA-OP pathology.

Rank	Protein	Total score	Primary CARNIVAL status	Shared-disease robustness	Network centrality	Normalized CARNIVAL evidence	Cell-context support	Genetic evidence	Protein feasibility	FDR-supported module pairs
1	HLA-DRB1	0.674	Above model-derived threshold	0.843	0.02	1	0.875	0.516	0.455	2
2	HSP90AA1	0.528	Above model-derived threshold	0.627	0.701	0.556	0.724	0	0.545	3
3	CTSK	0.353	Evaluated, below threshold	0.397	0.009	0	0.639	0.588	1	4
4	RPL7	0.345	Not in local network	1	0.038	0	0.806	0	0.091	2
5	PRG4	0.335	Not in local network	0.606	0	0	1	0.008	0.818	0
6	HLA-DRA	0.327	Evaluated, below threshold	0.598	0.022	0	0.897	0.261	0.455	0
7	CLEC3B	0.322	Not in local network	0.607	0	0	0.543	0.227	0.818	2
8	TIMP1	0.312	Evaluated, below threshold	0.762	0.014	0	0.721	0.014	0.455	0
9	SPP1	0.285	Evaluated, below threshold	0.329	0.05	0	0	0.996	0.455	1
10	APOE	0.262	Evaluated, below threshold	0.264	0.154	0	0	0.85	0.455	0

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Primary CARNIVAL status refers to the first evaluable baseline–knockout pair in the prespecified configuration sequence. Normalized CARNIVAL evidence is the 0–1 transformed evidence dimension used in the composite protein score; model-derived CARNIVAL scores are shown in Fig 5. Configuration-level evaluability, model-derived CARNIVAL scores, and stability summaries are provided in S4 Table. Degree-corrected and annotation-removal sensitivity rankings are provided in S7 Table.

The evidence composition of the leading proteins differed across the six scoring dimensions (Fig 4B). HLA-DRB1 ranked first overall (0.674), with the model-derived CARNIVAL component contributing its largest normalized evidence value [27]. HSP90AA1 ranked second (0.528) and was dominated by cell-context support. CTSK ranked third (0.353) and was dominated by protein feasibility, reaching the maximum feasibility score in the summary table. RPL7 and TIMP1 were dominated by shared-disease robustness, PRG4 and HLA-DRA by cell-context support, CLEC3B by protein feasibility, and SPP1 and APOE by genetic evidence (Table 2). Fig 4C places the leading proteins within their OmniPath neighborhood, whereas Fig 4D compares their genetic-evidence and protein-feasibility profiles.

Among the top 10 proteins, HLA-DRB1, HSP90AA1, CTSK, RPL7, CLEC3B, and SPP1 occurred in at least one FDR-supported module pair, with supported-pair counts of 2, 3, 4, 2, 2, and 1, respectively. No FDR-supported module-pair correspondence was identified for PRG4, HLA-DRA, TIMP1, or APOE.

Direct external evidence further supported the ranking. Among the top 200 proteins evaluated, 49 showed nonzero OA- or OP-relevant evidence in GWAS Catalog and 8 retained nonzero disease-association support in DisGeNET. Within the leading proteins, SPP1 and APOE showed the strongest genetic-evidence scores, whereas CTSK, PRG4, and CLEC3B remained prominent because protein-feasibility and context-related evidence remained high after integration.

The primary integrated score correlated with STRING degree (Spearman ρ = 0.476, P < 2.2 × 10⁻¹⁶). Degree correction retained 9 of the primary top 10 and 165 of the top 200 proteins, with a rank correlation of ρ = 0.882 among the primary top 200. Omission of network centrality retained 9 of the top 10 and 190 of the top 200 proteins. HLA-DRB1 and HSP90AA1 remained ranked first and second in both analyses. Among the externally evaluated top 200 proteins, the primary score correlated with generic UniProt/HPA annotation count (ρ = 0.388, P = 1.36 × 10⁻⁸); after omission of protein feasibility, this correlation decreased to ρ = 0.106 (P = 0.133. Omission of both genetic evidence and protein feasibility retained 7 of the top 10 and 185 of the top 200 proteins, with SPP1 and APOE moving to ranks 34 and 38, respectively (S6 Fig and S7 Table).

Composition-aware analyses qualified recurrent MHC-II and ribosomal signals

Cell-composition sensitivity analysis was performed in four eligible mixed-tissue cohorts: GSE55457, GSE82107, GSE117999, and GSE230665. None of the 40 population-by-cohort comparisons remained significant after global false-discovery-rate correction (S5A Fig; S6 Table). After adjustment for the first two principal components of the inferred abundance scores, 36 of 40 candidate–cohort effects had the same sign before and after adjustment (S5B Fig). Direction was retained in all four cohorts for APOE, HLA-DRA, HSP90AA1, PRG4, RPL7, SPP1, and TIMP1; retention was observed in three of four cohorts for HLA-DRB1 and CLEC3B and in two of four cohorts for CTSK. Fifty of 400 residual candidate–population associations remained significant at global FDR < 0.05 (S5C Fig), indicating that some candidate signals continued to track inferred cellular abundance within disease groups.

Adjustment for matched transcriptional programs retained the direction of 24 of 27 evaluable candidate–cohort effects (S5D Fig; S6 Table). Direction was retained for HLA-DRA in seven of seven cohorts, HLA-DRB1 in five of seven, HSP90AA1 in six of seven, and RPL7 in six of six. The median absolute adjusted-to-unadjusted effect ratios were 0.483 for HLA-DRA, 0.584 for HLA-DRB1, 1.117 for HSP90AA1, and 0.850 for RPL7. Thus, the MHC-II candidates showed greater attenuation after adjustment for the corresponding transcriptional program, whereas the HSP90AA1 and RPL7 effects were generally more stable.

Local CARNIVAL scores showed configuration-stable and configuration-sensitive model-derived profiles

Stabilized local CARNIVAL virtual knockout was evaluated across the prespecified local-network configurations (Fig 5). In the primary configuration, HLA-DRB1 had the highest model-derived CARNIVAL score (2.25), followed by HSP90AA1 (1.25); both exceeded the prespecified threshold of 0.1.

Model-derived score decomposition attributed the HLA-DRB1 and HSP90AA1 scores to reductions in disease signal, network activity, and active-node burden, whereas CTSK had a score of 0 (Fig 5B). Among the remaining leading proteins, some were evaluable with scores below the prespecified threshold, whereas others were absent from an evaluable local network (Fig 5C).

Configuration-sensitivity analysis evaluated 20 candidate proteins across six combinations of measurement-set size and connector limit, yielding 120 protein-configuration combinations (S3 Fig and S4 Table). Sixty-two combinations produced evaluable baseline and knockout solutions, 54 were non-evaluable because the candidate was absent from the reconstructed local network, and four failed the minimum-input precheck. HLA-DRB1 exceeded the model-derived threshold in all four evaluable configurations, with an invariant model-derived CARNIVAL score of 2.25; the two configurations using four measurements were not evaluable because the knockout network did not retain the minimum required input. HSP90AA1 was evaluable in all six configurations and exceeded the model-derived threshold in four, with a score of 1.25 under the measurement-set sizes of eight and six but a score of 0 under both four-measurement configurations. CTSK was evaluable in all six configurations but had a model-derived CARNIVAL score of 0 throughout. The four configurations using measurement-set sizes of eight or six produced identical model-derived score profiles and rankings (pairwise Spearman ρ = 1.0). Rank correlation was not defined for the two four-measurement configurations because all evaluable model-derived CARNIVAL score were zero.

Matched-null calibration identified context-specific single-cell perturbation outliers

Single-cell analysis characterized candidate expression and calibrated perturbation across four disease-relevant cellular compartments (Fig 6, Table 3). Candidate expression was summarized across OA fibroblasts, OA macrophages, OP bone marrow mesenchymal stromal cells, and OP osteoclast precursors. The raw scTenifoldKnk perturbation scores were then calibrated against 200 expression- and wild-type network-degree-matched control genes for each of the 39 evaluable candidate-compartment pairs. Fig 6C shows the resulting matched-null Z scores, whereas Fig 6D summarizes the leading calibrated pairs separately for OA and OP. Full matched-null distributions and empirical significance results are provided in S4 Fig and S5 Table.

Fig 6 — (A) Cellular composition of the OA and OP single-cell datasets used for contextual analysis. (B) Candidate expression in OA fibroblasts, OA macrophages, OP bone marrow mesenchymal stromal cells, and OP osteoclast precursors. Tile labels show mean log-normalized expression, and color indicates within-gene relative expression. (C) Matched-null Z scores for 39 evaluable candidate-compartment pairs. Each observed score was compared with scores from 200 control genes matched on mean log-normalized expression and wild-type weighted out-degree within the corresponding 600- or 601-gene network stratum. Asterisks indicate global FDR < 0.05, and NE indicates that no evaluable result was obtained. (D) Five leading calibrated pairs shown separately for OA and OP. One-sided empirical P values used the plus-one correction and were adjusted across all 39 evaluable pairs. OA, osteoarthritis; OP, osteoporosis; BM-MSC, bone marrow mesenchymal stromal cell; FDR, false discovery rate. External quality-control sensitivity results, including cell-retention statistics and comparison with the primary candidate-compartment findings, are provided in S7 Fig and S8 Table.

Table 3. Primary and external quality-control sensitivity results for single-cell candidate-compartment perturbation pairs.

Disease	Dataset	Compartment	Primary analysis	Cells after QC	QC sensitivity analysis
OA	GSE216651	Macrophage	10 evaluable; CTSK, CLEC3B, TIMP1, PRG4, and HSP90AA1 supported	6,983	11 evaluable; RPL7, TIMP1, HLA-DRB1, HSP90AA1, and HLA-DRA supported
OA	GSE216651	Fibroblast	10 evaluable; RPL7 supported	24,830	10 evaluable; CLEC3B, CTSK, and FHL1 supported
OP	GSE147287	BM-MSC	10 evaluable; CLEC3B supported	2,444	9 evaluable; no globally FDR-supported pair
OP	GSE147287	Osteoclast precursor	9 evaluable; TIMP1 and RPL7 supported	128	9 evaluable; MMP9 and CTSK supported

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Primary and quality-controlled perturbation scores were calibrated separately against 200 control genes matched on mean log-normalized expression and wild-type weighted out-degree. One-sided empirical P values used the plus-one correction and were adjusted across the 39 evaluable pairs within each analysis using the Benjamini-Hochberg method. All quality-controlled pairs listed in the table had global FDR = 0.019. Complete pair-level results and sample-level quality-control counts are provided in S8 Table.

Across the 39 evaluable candidate-compartment pairs, 11 had one-sided empirical P values below 0.05 and nine remained supported after Benjamini-Hochberg correction across all 39 tests. In OA macrophages, the supported pairs were CTSK (null Z = 3.61, q = 0.024), CLEC3B (Z = 2.65, q = 0.024), TIMP1 (Z = 2.62, q = 0.024), PRG4 (Z = 1.90, q = 0.024), and HSP90AA1 (Z = 1.26, q = 0.024). In OA fibroblasts, RPL7 was retained (Z = 1.96, q = 0.024). Although FHL1 showed a positive OA macrophage signal (Z = 2.41, empirical P = 0.0249), it did not remain significant after global correction (q = 0.097).

In the OP compartments, CLEC3B was supported in bone marrow mesenchymal stromal cells (null Z = 2.09, q = 0.024). TIMP1 (Z = 2.37, q = 0.043) and RPL7 (Z = 1.68, q = 0.024) were supported in osteoclast precursors. Several candidates with comparatively high raw perturbation scores, including MMP9, HLA-DRA, HLA-DRB1, and CTSK in OP compartments, did not exceed their matched-null backgrounds after global correction. These comparisons showed that several high raw perturbation scores were not outliers relative to expression- and network-degree-matched control genes.

Restricting the null distribution to the 100 closest matched control genes retained the same nine globally FDR-supported pairs and produced concordant significance classifications for all 39 evaluated pairs. Matching distances varied among candidate-compartment pairs. The median matching distances for OA macrophage PRG4 and HSP90AA1 were 2.98 and 2.21, respectively.

External quality-control sensitivity analysis qualified cell-compartment assignments

External quality control retained 125,090 of 161,470 cells (77.47%). The retained cell counts were 76,655 of 105,786 for GSE216651, 34,267 of 36,918 for GSE152805, and 14,168 of 18,766 for GSE147287. After quality control, the four perturbation compartments contained 24,830 OA fibroblasts, 6,983 OA macrophages, 2,444 OP bone marrow mesenchymal stromal cells, and 128 OP osteoclast precursors. Dataset- and sample-level retention statistics are provided in S7 Fig and S8 Table.

The quality-controlled analysis produced 39 evaluable candidate-compartment pairs, of which 10 remained supported at global FDR < 0.05. Supported pairs comprised CLEC3B, CTSK, and FHL1 in OA fibroblasts; RPL7, TIMP1, HLA-DRB1, HSP90AA1, and HLA-DRA in OA macrophages; and MMP9 and CTSK in OP osteoclast precursors. No pair remained globally supported in OP bone marrow mesenchymal stromal cells. Among the 38 pairs evaluable in both analyses, correlations between the primary and quality-controlled results were 0.105 for raw perturbation scores and 0.015 for matched-null Z scores. HSP90AA1 and TIMP1 in OA macrophages were the two primary pairs retained in the same compartment, whereas CTSK, CLEC3B, and RPL7 remained supported but shifted between compartments.

When the quality-controlled cell-context evidence was substituted in a ranking sensitivity analysis, all 10 primary top-ranked proteins and 19 of the primary top 20 proteins were retained. The overall rank correlation was greater than 0.999, and HLA-DRB1 and HSP90AA1 remained ranked first and second. Thus, external quality control materially affected pair-level cellular localization but had negligible influence on the overall protein-prioritization hierarchy.

Discussion

This study presents a disease-first, multilayer framework for investigating shared OA-OP pathology from public datasets. Overall, the findings suggest that OA and OP may converge through interconnected inflammatory, extracellular matrix-remodeling, immune, and bone-homeostasis programs rather than representing entirely unrelated disease processes. This interpretation is supported by the convergence of disease-specific signatures, layered shared-axis definition, protein-level evidence integration, virtual knockout analysis, and single-cell contextualization accompanied by matched-null calibration and external quality-control sensitivity testing.

Our findings are broadly consistent with the evolving view that OA and OP are not merely opposing skeletal phenotypes, but may share inflammation-, immune-, stress-, and remodeling-related mechanisms in ageing musculoskeletal tissues. OA is increasingly recognized as a whole-joint disorder involving synovial inflammation, cartilage degeneration, subchondral bone remodeling, matrix turnover, cellular senescence, and impaired autophagy, whereas OP is driven by imbalanced bone remodeling under the influence of osteoimmune activation, oxidative stress, and altered osteoblast–osteoclast coupling. In this context, the convergence of OA and OP signatures on antigen presentation, cytokine-related regulation, extracellular matrix organization, collagen-associated processes, osteoclast differentiation, and ossification is biologically plausible. The prioritized proteins identified in the present study also fit this background. HLA-DRB1 and HLA-DRA reflect antigen-presentation and immune-activation programs [28]; CTSK and TIMP1 are closely related to bone resorption and matrix remodeling [29,30]; PRG4, CLEC3B, and SPP1 point to extracellular matrix and tissue-repair biology [31–33]; and APOE may connect lipid handling, inflammation, and joint–bone homeostasis [16]. Therefore, rather than indicating a simple overlap between two disease gene lists, the present results support a shared osteoimmune–matrix-remodeling axis linking OA and OP across joint and bone compartments.

Local virtual knockout assigned the two highest model-derived model-derived CARNIVAL score to HLA-DRB1 and HSP90AA1. Across evaluable configurations, the HLA-DRB1 score was more stable than the HSP90AA1 score. HLA-DRB1 encodes a major histocompatibility complex class II molecule involved in antigen presentation and CD4-positive T-cell activation [28]. Its prioritization suggests that immune recognition and antigen-presentation programs may contribute to the shared OA-OP state, particularly in synovial inflammatory and marrow-remodeling contexts. This interpretation is consistent with the increasing appreciation that osteoimmune interactions can influence both pathological bone resorption and inflammatory joint remodeling [34]. However, HLA-DRB1 should not be interpreted as a direct therapeutic target at this stage. Instead, it may represent an immune-context marker or model-prioritized network node that captures antigen-presentation activity within the shared disease network. HLA-DRB1 retained the same rescue score of 2.25 in all four configurations in which its knockout network remained evaluable. The two four-measurement configurations were non-evaluable because the knockout network did not meet the minimum-input requirement. HLA-DRB1 therefore showed the greatest configuration stability among candidates exceeding the model-derived threshold.

HSP90AA1 represents a different type of candidate. As a molecular chaperone, HSP90AA1 participates in proteostasis, stress adaptation, autophagy-related regulation, and stabilization of multiple signaling proteins [35,36]. It retained a rescue score of 1.25 under the four configurations using measurement-set sizes of eight or six but had a score of 0 under the two four-measurement configurations. This configuration dependence limits the evidence for a stable model-based network signal. Impaired autophagy, oxidative stress, inflammation, and senescence are implicated in OA progression, whereas heat-shock proteins and chaperone-mediated mechanisms also influence osteoblast and osteoclast biology [37,38]. HSP90AA1 may therefore represent a proteostasis- and stress-response component of the shared OA-OP program. Its loss of cross-disease direction consistency after exclusion of GSE35958 further identified it as both cohort-sensitive and configuration-sensitive. HSP90AA1 consequently remains a secondary candidate for experimental validation.

A major strength of this study is its disease-first order of inference. Instead of directly intersecting DEGs or pooling heterogeneous OA and OP datasets, we first derived disease-specific signatures and network features within each condition, and introduced cross-disease comparison only after within-disease evidence had been established. This strategy reduces the risk that apparent OA-OP convergence is driven by tissue composition or platform heterogeneity. The shared axis integrated concordant dysregulation, shared pathway enrichment, and module-correspondence evidence. Permutation calibration indicated that statistical support within the module layer was concentrated in selected pair-specific correspondences rather than a global excess across all OA-OP module combinations. These findings support localized cross-disease module correspondence but not global enrichment across all OA-OP module combinations.

By integrating protein-interaction, signaling, annotation, tissue-support, disease-association, and genetic evidence, the workflow further moved from transcript-level overlap to protein-level prioritization. The resulting candidates represented complementary biological programs, including antigen presentation and immune activation, signaling-network stabilization, bone resorption, extracellular matrix remodeling, and immune-bone homeostasis. Local CARNIVAL analysis added a model-based functional prioritization layer. HLA-DRB1 showed the highest and most configuration-stable model-derived CARNIVAL score, whereas the high primary score of HSP90AA1 was not retained under the two smallest measurement configurations. Candidates such as CTSK remained highly ranked through complementary protein-feasibility, disease-relevance, and cell-context evidence despite having a CARNIVAL score of 0.

The primary single-cell analysis provided cellular context for the prioritized proteins, but external quality-control sensitivity analysis showed that precise candidate-compartment assignments were not uniformly stable. HSP90AA1 and TIMP1 retained support in OA macrophages, whereas CTSK, CLEC3B, and RPL7 remained supported but shifted between compartments. Five of the six genes supported in the primary analysis retained evidence in at least one quality-controlled compartment, but only two of the nine primary candidate-compartment pairs were retained without a change in cellular context. In contrast, recalculation of the cell-context dimension retained the complete primary top 10 protein set and preserved HLA-DRB1 and HSP90AA1 as the two leading candidates. The single-cell findings therefore support candidate-level cellular relevance but do not establish fixed disease-associated cell-type localization.

The moderate association between the integrated score and STRING degree indicated that network topology contributed to the overall ranking. However, HLA-DRB1 and HSP90AA1 remained ranked first and second when centrality was degree-corrected or omitted, consistent with substantial support from the other evidence dimensions. In contrast, removal of genetic evidence and protein feasibility moved SPP1 and APOE to ranks 34 and 38, respectively, showing that their prioritization depended more strongly on these external evidence sources. The leading proteins therefore differed in both the composition and robustness of their supporting evidence.

The recurrent prioritization of MHC-II and ribosomal proteins requires cautious interpretation. Most candidate–cohort effects retained their direction after adjustment for inferred cellular abundance, arguing against composition as the sole explanation for the bulk signals. Nevertheless, residual associations with inferred cell populations remained for several candidates, and HLA-DRA and HLA-DRB1 were attenuated after adjustment for the broader MHC-II transcriptional program. These proteins may therefore reflect both disease-associated immune activity and variation in antigen-presenting-cell abundance or activation state. RPL7 retained its direction in all evaluable cohorts after ribosomal-program adjustment, but its biological interpretation remains linked to general translational activity rather than a disease-specific regulatory function. Accordingly, the MHC-II and ribosomal candidates are best regarded as context-sensitive components of the shared axis rather than confirmed OA–OP-specific regulators.

Overall, this study supports a shared osteoimmune–matrix-remodeling protein axis linking OA and OP, providing candidates for future validation.

Limitation

Several limitations should also be considered. First, although the configuration-sensitivity analysis evaluated score stability across all six prespecified local-network settings, the virtual-knockout results remain dependent on the OmniPath prior, the selected biological output panels, and the local-network representation. HLA-DRB1 was non-evaluable under the two smallest measurement configurations because its knockout network failed the minimum-input requirement, whereas the HSP90AA1 score decreased to 0 under these settings. These dependencies limit the CARNIVAL results to computational functional prioritization and require experimental evaluation of the predicted network-state changes.

Second, despite the disease-first design, the public datasets remain heterogeneous in tissue source, platform, and cohort composition, and one OP WGCNA block was excluded because stable module construction could not be justified. In addition, permutation calibration did not show a global excess of criteria-positive module pairs over the size-preserving null, although 53 individual pairs retained empirical FDR < 0.05. The module analysis therefore supported 53 pair-specific correspondences but did not demonstrate global enrichment of OA-OP module similarity. Exclusion of GSE35958 preserved overall rank concordance and Layer 1 + 2/3 candidate counts but altered several upper-ranked features, indicating that HSP90AA1 and RPL7 were sensitive to cohort composition.

Third, the single-cell analyses remain computational and should be interpreted as contextual support rather than experimental validation. External quality control removed low-complexity cells, cells with excessive mitochondrial transcript fractions, and predicted doublets, but the candidate-compartment results remained sensitive to the retained cellular composition. Only two primary pairs remained supported in the same compartment, although five of the six primary supported genes retained evidence in at least one compartment. Ambient-RNA correction could not be applied consistently because unfiltered droplet matrices containing empty droplets were not available for all datasets. The limited number of OP osteoclast precursors and variation in sample-level cell retention further constrain compartment-specific interpretation. Independent single-cell datasets and experimental perturbation studies are therefore required to confirm the cellular localization of these candidates. The bulk composition analysis was restricted to four eligible mixed-tissue cohorts and relied on transcriptome-derived abundance estimates rather than directly measured cell fractions. These adjustments cannot fully separate changes in cell abundance from changes in cellular activation state, and residual composition-related confounding may therefore remain.

Fourth, the protein ranking was influenced by network topology and the coverage of public annotation resources. Although HLA-DRB1 and HSP90AA1 remained the leading candidates in degree-aware analyses, SPP1 and APOE were more sensitive to removal of genetic and protein-feasibility evidence. These candidate-specific differences require validation using evidence independent of the resources included in the prioritization framework.

Conclusion

This study supports a disease-first, multilayer framework for investigating shared biology linking osteoarthritis and osteoporosis. The results suggest that OA and OP may converge through overlapping inflammatory, extracellular matrix-remodeling, immune, and bone-homeostasis programs rather than reflecting simple coexistence of two entirely independent disorders. By preserving disease-specific structure before cross-disease comparison and then integrating protein-level evidence with local virtual knockout and single-cell contextual analysis with matched-null calibration and external quality-control sensitivity testing, we prioritized a focused set of shared candidate proteins for follow-up.

Within this framework, HLA-DRB1 and HSP90AA1 had the highest model-derived CARNIVAL scores in the selected directed prior-knowledge network, whereas CTSK, PRG4, CLEC3B, SPP1, TIMP1, and APOE remained notable because complementary evidence from biological plausibility, protein feasibility, genetic support, or cellular context converged on them. These findings do not by themselves establish definitive causal regulators of shared OA-OP pathology, but they provide a biologically informed shortlist and a practical analytical framework for future mechanistic and translational validation.

Methods

Ethics statement

This study analyzed only publicly available, de-identified transcriptomic datasets obtained from GEO (GSE55235, GSE55457, GSE82107, GSE117999, GSE56814, GSE56815, GSE35958, GSE230665, GSE216651, GSE152805, and GSE147287) together with CELLxGENE reference resources. No new participant recruitment, intervention, or biospecimen collection was performed by the authors, and no directly identifiable participant information was accessed. Therefore, no new informed consent was obtained for this secondary analysis.

Public datasets and overall analytical design

This study was a secondary reanalysis of publicly available, de-identified transcriptomic datasets; no new transcriptomic or participant-level data were generated. Public transcriptomic datasets were collected from GEO. OA bulk cohorts included GSE55235, GSE55457, GSE82107, and GSE117999, whereas OP bulk cohorts included GSE56814, GSE56815, GSE35958, and GSE230665. Single-cell datasets used for contextual validation were GSE216651, GSE152805, and GSE147287, with CELLxGENE used as a reference resource for cell-type interpretation. The analytical design followed a disease-first strategy: OA and OP were modeled separately before any cross-disease integration. Cohorts from different platforms or tissue sources were therefore retained as independent analytical units and were integrated only after disease-level ranking or network construction, rather than by direct early-stage pooling of heterogeneous matrices.

Differential analysis and disease-signature construction

Each bulk cohort was modeled independently using limma. Cohort-specific gene rankings were integrated separately within OA and OP by robust rank aggregation [39], and Gene Ontology biological-process enrichment was used to characterize the resulting disease-level signatures [40]. Sensitivity to the small OP stromal cohort was assessed by repeating the OP integration after exclusion of GSE35958. Probe mapping, rank-construction rules, enrichment thresholds, and leave-one-dataset-out comparison procedures are detailed in S1 Text.

Bulk cell-composition and transcriptional-program sensitivity analysis

Cell-composition sensitivity analysis was conducted in four eligible mixed-tissue cohorts with available case–control contrasts and informative abundance estimates: GSE55457, GSE82107, GSE117999, and GSE230665. MCP-counter was used to estimate ten immune and stromal population scores from gene-level expression matrices. The ten leading candidate genes were removed from the marker input before abundance estimation. Samples without finite expression measurements were excluded before analysis; four such samples were excluded from GSE117999, leaving 10 cases and 10 controls. Population scores were standardized within each cohort, and case–control differences were estimated using empirical-Bayes linear models. Benjamini–Hochberg correction was applied across the 40 population-by-cohort comparisons.

Candidate-expression effects were re-estimated after adjustment for the first two principal components of the standardized population-score matrix. Directional stability was defined as retention of the sign of the case–control coefficient before and after adjustment. Residual candidate–population associations were evaluated using Spearman correlation after removing the group effect from both variables, with global false-discovery-rate correction across 400 candidate–population comparisons.

To distinguish candidate-specific associations from broader transcriptional activity, matched gene-program scores were calculated as the mean gene-wise standardized expression of Gene Ontology-defined MHC-II antigen-presentation, ribosomal, and protein-folding gene sets. The evaluated candidate was excluded from its corresponding program. HLA-DRA and HLA-DRB1 were adjusted for the MHC-II program, RPL7 for the ribosomal program, and HSP90AA1 for the protein-folding program. Effect direction and magnitude were compared before and after program-score adjustment across all evaluable cohort–candidate combinations.

Shared pathological axis and disease-associated module matching

The shared OA-OP axis comprised three ordered evidence layers. For each mapped gene, cohort-specific logFC values were averaged separately within OA and OP. Layer 1 required identical signs for the resulting OA and OP mean effects: positive means in both diseases denoted concordant upregulation, whereas negative means denoted concordant downregulation; genes with opposing signs were excluded. Direction was therefore determined from within-cohort case-control effects rather than by directly comparing expression intensities across platforms. Layer 2 required support from biological processes enriched in both diseases. Layer 3 incorporated correspondence between disease-associated OA and OP coexpression modules constructed using WGCNA within tissue- and platform-consistent blocks [41]. Criteria-positive module pairs were identified by shared-pathway-gene overlap, functional-term overlap, or a joint gene-overlap and Jaccard criterion. Pair-specific statistical support was evaluated using block-stratified permutation calibration with false-discovery-rate correction. Complete WGCNA settings, module-retention criteria, matching thresholds, and permutation procedures are provided in S1 Text.

Protein-level evidence integration and final prioritization

Shared-axis candidates were mapped to proteins and evaluated across six evidence dimensions: shared-disease robustness; interaction-network centrality based on STRING [20]; model-derived virtual-knockout support based on OmniPath and CARNIVAL [21,27]; cell-context support; genetic evidence from Open Targets, GWAS Catalog, and DisGeNET [24–26]; and protein feasibility based on UniProt and Human Protein Atlas annotations [22,23]. The dimensions were independently normalized and combined using prespecified weights because no labeled outcome was available for empirical weight estimation. Direct GWAS Catalog and DisGeNET evaluation was restricted to the first 200 proteins after protein-layer integration. Definitions, normalization rules, and weights for all six dimensions are reported in S1 Text and S2 Table.

Sensitivity to network hubness and annotation density was evaluated using degree-corrected centrality, omission of network centrality, and omission of selected external-evidence dimensions. Rank concordance and retention of leading proteins were compared with the primary ranking. The degree-adjustment model and annotation definitions are provided in S1 Text.

Virtual knockout analysis

Model-based virtual knockout was performed using directed, signed OmniPath relationships [21] and local CARNIVAL networks [27] reconstructed around shared OA-OP measurements. Candidate removal was represented by deletion of its incident prior-knowledge relationships and corresponding measurement constraint. The primary configuration was defined as the first evaluable baseline-knockout pair in the prespecified configuration sequence. Model-derived CARNIVAL scores summarized changes in inflammatory, extracellular-matrix, osteoclast, osteoblast, and network-state measures, with a score of at least 0.1 classified as exceeding the prespecified model-derived threshold.

Configuration sensitivity was evaluated across six combinations of measurement-set size and connector limit. Candidate evaluability, model-derived scores, above-threshold classifications, and rank concordance were compared across configurations. Network-construction parameters, solver settings, biological output panels, score calculations, and configuration definitions are provided in S1 Text and S2 Table.

Single-cell contextual analysis, external quality control, and scTenifoldKnk analysis

Single-cell contextual analysis evaluated a prespecified candidate panel using scTenifoldKnk [42] in OA fibroblasts, OA macrophages, OP bone marrow mesenchymal stromal cells, and OP osteoclast precursors. Candidate expression and perturbation scores were calibrated against control genes matched by expression and wild-type network connectivity, with false-discovery-rate correction across evaluable candidate-compartment pairs.

An external quality-control sensitivity analysis excluded low-complexity cells or nuclei, cells with excessive mitochondrial transcript fractions, and predicted doublets [43] before recalculating candidate-compartment evidence and the cell-context component of the protein ranking. Complete cell-filtering thresholds, network-inference parameters, matched-null procedures, and sample-level retention counts are provided in S1 Text and S8 Table.

Statistical analysis and reproducibility

All analyses were performed in R. Analytical thresholds, fallback rules, evidence-component definitions, protein-ranking weights, CARNIVAL settings, single-cell perturbation parameters, and software versions are detailed in S1 Text and S2 Table. The fully anonymized analysis code, including the primary analysis scripts and independent sensitivity-analysis scripts, is provided in S1 File. The reproducibility archive includes ranked disease signatures, shared-axis candidates, criteria-positive module pairs, pair-level empirical P values and FDR estimates, global and block-pair null summaries, protein-level evidence tables, virtual-knockout outputs, primary and quality-controlled single-cell perturbation results, cell-retention statistics, and protein-ranking sensitivity results. The main figures display selected genes and biological processes, whereas the complete ranked results and accompanying derived data are provided in S2 File.

The fully anonymized analysis code and non-identifying derived data are available from Figshare at https://doi.org/10.6084/m9.figshare.33320325. The accompanying derived data include the ranked disease signatures, shared-axis candidates, module-pair calibration results, protein-level evidence tables, virtual-knockout outputs, single-cell perturbation results, and ranking-sensitivity analyses.

Supporting information

S1 Table. Leave-one-dataset-out sensitivity analysis excluding GSE35958.

(XLSX)

pone.0359583.s001.xlsx^{(18.5KB, xlsx)}

S2 Table. Analytical parameters, evidence definitions, weighting scheme, and software versions.

(XLSX)

pone.0359583.s002.xlsx^{(24.2KB, xlsx)}

S3 Table. Permutation-based calibration of cross-disease module matching.

(XLSX)

pone.0359583.s003.xlsx^{(126.1KB, xlsx)}

S4 Table. Configuration-level stability of model-derived local CARNIVAL virtual-knockout results.

(XLSX)

pone.0359583.s004.xlsx^{(24KB, xlsx)}

S5 Table. Matched-null calibration of single-cell scTenifoldKnk perturbation scores.

(XLSX)

pone.0359583.s005.xlsx^{(1.7MB, xlsx)}

S6 Table. Bulk cell-composition and transcriptional-program sensitivity analyses of prioritized proteins.

(XLSX)

pone.0359583.s006.xlsx^{(193.6KB, xlsx)}

S7 Table. Hubness and annotation-density sensitivity of protein prioritization.

(XLSX)

pone.0359583.s007.xlsx^{(2.1MB, xlsx)}

S8 Table. External quality-control sensitivity of single-cell perturbation results.

(XLSX)

pone.0359583.s008.xlsx^{(994.4KB, xlsx)}

S1 Text. Supplementary methods: detailed analytical parameters and decision rules.

(DOCX)

pone.0359583.s009.docx^{(38.7KB, docx)}

S1 Fig. Leave-one-dataset-out sensitivity analysis excluding GSE35958.

(A) Rank concordance between the primary OP robust rank aggregation and the analysis excluding GSE35958. (B) Rank concordance for the shared-axis candidates. (C) Stability of the primary top-10 protein ranking; crosses indicate proteins that were not retained because cross-disease direction consistency was lost. (D) Retention of primary top-ranked features at different ranking depths. For the conditional protein ranking, shared-disease robustness was recalculated from the leave-one-dataset-out results, and the other evidence dimensions were held constant.

(TIF)

pone.0359583.s010.tif^{(621KB, tif)}

S2 Fig. Permutation-based calibration of cross-disease module matching.

(A) Null distribution of the total number of criteria-positive OA-OP module pairs across 10,000 block-stratified module-label permutations preserving each block-specific gene universe and exact module sizes. The orange line indicates the observed count of 234, the dashed blue line indicates the null mean of 254.83, and blue shading indicates the 95% null interval of 242–267. The upper-tail empirical P value was 0.9995. (B) Observed and null-calibrated counts for the nine OA-OP block combinations. Blue points and horizontal intervals indicate null means and 95% intervals; orange points indicate observed counts. Numbers indicate pair-specific correspondences with empirical FDR < 0.05. (C) Pair-specific empirical FDR across all 464 tested module combinations. Cells are colored by pair-level -log10(empirical FDR) for the 234 criteria-positive pairs; white cells did not meet the deterministic criteria. Black dots indicate the 53 pairs with empirical FDR < 0.05. OA, osteoarthritis; OP, osteoporosis; FDR, false discovery rate.

(TIF)

pone.0359583.s011.tif^{(3.3MB, tif)}

S3 Fig. Configuration sensitivity of local CARNIVAL virtual knockout.

(A) Model-derived CARNIVAL scores for 20 candidate proteins across six combinations of measurement-set size and connector limit. Gray cells marked “NE” indicate that no evaluable local network was obtained; “KO<min” indicates that the knockout network contained fewer than the required number of measurement inputs. Bold values met the rescue-associated threshold of 0.1. (B) Configuration-level rescue profiles of HLA-DRB1, HSP90AA1, and CTSK. Crosses indicate non-evaluable configurations. (C) Numbers of attempted, evaluable, and rescue-associated configurations for the top 10 proteins. Non-evaluable configurations are reported separately from evaluable zero scores.

(TIF)

pone.0359583.s012.tif^{(3.5MB, tif)}

S4 Fig. Matched-null calibration of single-cell perturbation scores.

(A) Null-calibrated Z scores across 39 evaluable candidate-compartment pairs. Each observed score was compared with 200 control genes matched on mean log-normalized expression and wild-type weighted out-degree within the corresponding 600- or 601-gene network stratum. Asterisks indicate global FDR < 0.05, and NE indicates that no evaluable result was obtained. (B) Observed perturbation scores and corresponding matched-null means and 2.5th–97.5th percentile intervals. (C) Null-calibrated Z scores and one-sided empirical P values. Triangles indicate pairs with global FDR < 0.05. Restriction to the 100 closest controls retained the same nine globally FDR-supported pairs.

(TIF)

pone.0359583.s013.tif^{(486.8KB, tif)}

S5 Fig. Bulk cell-composition and transcriptional-program sensitivity analysis of prioritized proteins.

(A) Standardized case–control differences in MCP-counter abundance scores across four eligible mixed-tissue cohorts. (B) Ratios of composition-adjusted to unadjusted candidate effects; crosses indicate reversal of effect direction, and dots indicate adjusted effects with global FDR < 0.05. (C) Numbers of residual candidate–population associations reaching global FDR < 0.05 after removal of the case–control group effect. (D) Ratios of transcriptional-program-adjusted to unadjusted effects for HLA-DRA, HLA-DRB1, RPL7, and HSP90AA1. Crosses indicate direction reversal, and dots indicate adjusted effects with global FDR < 0.05.

(TIF)

pone.0359583.s014.tif^{(2.9MB, tif)}

S6 Fig. Hubness and annotation-density sensitivity of protein prioritization.

(A) Primary integrated score versus STRING degree. (B) Primary integrated score versus generic UniProt/HPA annotation count among the externally evaluated top 200 proteins. (C) Concordance between the primary and degree-corrected rankings. (D) Leading-protein ranks after degree correction and omission of selected evidence dimensions.

(TIF)

pone.0359583.s015.tif^{(2.7MB, tif)}

S7 Fig. External quality-control sensitivity analysis of single-cell perturbation results.

(A) Dataset-level cell counts retained and excluded by external quality control. (B) Sample-level proportions of retained cells. (C) Comparison of matched-null Z scores for the 38 candidate-compartment pairs evaluable in both the primary and quality-controlled analyses. (D) Numbers of FDR-supported compartments per candidate before and after external quality control. QC, quality control; FDR, false discovery rate.

(TIF)

pone.0359583.s016.tif^{(786.1KB, tif)}

S1 File. Analytical code and reproducibility resources.

Archive containing the primary analysis scripts, revision-analysis scripts, scoring weights, software-version records, session information, and file manifest.

(ZIP)

pone.0359583.s017.zip^{(160.9KB, zip)}

S2 File. Processed data underlying the primary analyses.

Minimal dataset containing the derived data underlying the main findings, including ranked disease signatures, shared-axis candidates, module-pair calibration results, protein-level evidence tables, virtual-knockout outputs, single-cell perturbation results, and ranking-sensitivity analyses.

(ZIP)

pone.0359583.s018.zip^{(4.3MB, zip)}

Data Availability

The minimal dataset and anonymized code underlying the findings of this study are available in Figshare at https://doi.org/10.6084/m9.figshare.33320325. The public transcriptomic datasets reanalyzed in this study are available from the NCBI Gene Expression Omnibus under accessions GSE55235, GSE55457, GSE82107, GSE117999, GSE56814, GSE56815, GSE35958, GSE230665, GSE216651, GSE152805, and GSE147287. CELLxGENE was used as a public reference resource. No directly identifying participant information is included in the deposited files.

Funding Statement

This work was supported by a grant from the Guangdong Provincial Second Hospital of Traditional Chinese Medicine Scientific Research Innovation Foundation (No. SEZYY2023B12). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0359583.r001

Decision Letter 0

Jung-Eun Kim

6 Jul 2026

Dear Dr. Yang,

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Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This is an interesting scientific manuscript and was read with pleasure. This omics study investigates shared between Osteoarthritis and Osteoporosis.

Strengths

- The topic is highly relevant

- Novelty; prior literature on the topic is sparse and the concept is a well thought question

- Potential to promote to academia beyond clinical care; this will help understand pathophysiology better acknowleding that the two disorders frequently overlap

- The approach of using "disease first", initial independent analysis of disorders and then later assessing the shared ones sounds good

- "Disease-first strategy" is intellectual choice

- Methodology of bioinformatics and the analysis are robust and apprpriate

- Transparent reporting - acknowldment in discussion that findings are hyothesis-generating than mechanistic. The careful text to not include causal terms is appreciated. The findings are approrpiately described rather than overclaims that cannot be claimed with the data

- Use of multiple independent datasets used - adds heterogeneity

- Providing datasets, codes and transparency in reporting - all these combined provide high credibility of the results

- Multiomic integration is appreciative and comprehensive data

- The identified pathways aligns with literature present

Comments -

My major comments or concerns are already acknowledged in the discussion by the authors as limitations. They have consistently avoided causal language - this is an excellent paper and well written.

Minor comments-

1. The small datasets might act as outliers and introduce random noise. OP stromal dataset is only 9 patients. Would consider dropping - the results from these may be spurious

2. How were weights assigned or computed, and at which stage was weighting done?

3. If authors can add more granular methodological details like thrsholds, parameters, protein ranking weights and CARNIVAL settings. This is not a concern of the study. This study is worthy of future work that several scientists might conduct, sharing details in supplement will help in reproducibilty of this work. This is because frequently validation studies' results gets diluted based on if methods were exactly the same as the original paper.

Reviewer #2: This manuscript presents an integrative, multi-layer computational framework to identify a shared molecular axis linking osteoarthritis (OA) and osteoporosis (OP). The work combines bulk transcriptomic meta-analysis (RRA signatures), WGCNA module matching, network-based protein prioritization, CARNIVAL-based virtual knockout, and single-cell perturbation analysis (scTenifoldKnk). The breadth of the integration is genuinely commendable, and the accompanying minimal dataset and code archive substantially improve transparency and reproducibility. The pipeline is logically organized, the processed outputs are traceable, and the analytical intent is clearly articulated.

My assessment is that this is a valuable hypothesis-generating resource. However, in line with the PLOS ONE criterion that conclusions must be supported by the data and that the analysis must be performed to a high technical standard, several core inferences currently rest on deterministic or permissive criteria that lack statistical calibration. I therefore recommend major revision. None of the points below question the value of the underlying idea, rather, they aim to strengthen the rigor so the conclusions can be stated with appropriate confidence. The concerns are addressable without new data generation.

Major Points

1. Statistical null models for cross-disease module matching

The identification of 234 matched module pairs (matched_module_pairs.tsv) is, as implemented, a deterministic procedure based on overlap and Jaccard thresholds. While the criteria are internally consistent, the manuscript does not establish that the observed degree of cross-disease module overlap exceeds what would be expected by chance given the module-size distributions and the shared background gene universe.

My suggestion is to introduce a permutation-based null model (e.g., label shuffling or degree/size-preserving randomization of module membership) to assign empirical significance and an FDR to the matched pairs. This would convert a descriptive overlap into a statistically supported claim and would materially strengthen the central "shared axis" narrative.

2. CARNIVAL virtual-knockout transparency and sensitivity

The virtual knockout is run in local_m8_c3 mode, and the rescue signal is highly localized, being active for only 2 of the 10 top proteins (HLA-DRB1 and HSP90AA1). Two issues arise. First, the solver configuration (ILP solver used, optimality gap, time limits, regularization) is not disclosed, which limits independent reproduction since CARNIVAL results can depend on solver and parameterization. Second, basing the rescue interpretation on a single subnetwork without sensitivity analysis leaves the result vulnerable to instability.

What can be done for adressing these points are: (a) Report the full solver configuration and version. (b) Provide a sensitivity/stability analysis, for example by varying the input subnetwork, perturbing edge weights, or repeating across alternative module selections, and report how consistently the rescue signal for HLA-DRB1 and HSP90AA1 is recovered.

3. Calibration of single-cell perturbation scores

The scTenifoldKnk perturbation scores (range ≈ 1.5–2.8 in single_cell_knockout_results.tsv / Table3) are defined in R/06_single_cell_validation.R as the mean absolute Z over the top-ranked affected genes, mean(abs(dr$Z[seq_len(top_n)])). This is a magnitude-of-disruption metric without a calibrated null distribution, so the absolute values are difficult to interpret as biologically meaningful effect sizes, and there is no threshold separating signal from background.

You can provide a null distribution for the perturbation score, for instance by knocking out matched random/control genes (matched on expression level and network degree) and reporting an empirical p-value or z-score for each candidate. This directly addresses whether the prioritized genes are perturbation outliers.

4. Cell-composition and abundance confounding

Several high-ranking proteins recur across compartments and analyses (notably HLA-DRA/DRB1, RPL7, HSP90AA1). MHC class II genes and ribosomal proteins are classic markers of cell-type composition shifts and global transcriptional/translational activity rather than disease-specific regulators. Without explicit control, the prioritized axis risks reflecting composition or abundance artifacts.

Here is my recommendation: (a) Examine and report whether candidate signals track cell-type proportion changes between conditions; consider a composition-aware or deconvolution-based check on the bulk layer. (b) Discuss, and where possible mitigate, the recurrent appearance of MHC-II and ribosomal genes as a potential confounder rather than a finding.

5. Hubness and annotation bias in prioritization

The network prioritization appears to reward high-degree nodes (e.g., HSP90AA1) and well-annotated genes. Degree centrality and annotation density are known to bias such rankings toward generic hubs independent of disease relevance.

It might be beneficial to report a degree-aware control, such as comparing candidate ranks against a degree-matched background or applying a degree-corrected centrality, and quantify the correlation between final priority scores and node degree / annotation count. If the ranking survives degree correction, this would considerably strengthen the specificity claim.

Single-Cell Quality Control

Code inspection of R/06_single_cell_validation.R confirms QC thresholds of ≥80 cells per group and ≥200 genes, with library-size normalization to 1e4, but the scTenifoldKnk step runs with qc = FALSE, and there is no mitochondrial-content or doublet filtering. Given that ambient RNA and doublets can inflate exactly the kind of housekeeping/ribosomal and MHC signals observed here, this is worth addressing.

Please add standard mitochondrial-fraction and doublet filtering (or justify their omission), and report cell counts before and after QC per dataset.

Minor Points and Reproducibility

- The minimal dataset and code archive are a real strength. To complete reproducibility, please specify exact package versions (a sessionInfo() or lockfile), since WGCNA, CARNIVAL, and scTenifoldKnk outputs are version-sensitive. This is already partly captured in R/00_install_packages.R; pinning versions would close the gap.

- Please clarify in the Methods that the workflow is a reanalysis of public data (the GEO series in source_datasets.tsv / Table1) rather than raw-data generation, so readers correctly interpret the scope of the validation.

- A brief statement of the directionality logic in shared_direction_consistent_genes.tsv (how concordant up/down direction is defined across heterogeneous platforms) would help readers assess the shared-direction claim.

- Consider softening causal language ("regulatory mechanism", "rescue") to reflect that the evidence is associative and model-based until the null-model and sensitivity analyses above are in place.

This is a thoughtfully constructed integrative study with a transparent codebase and a clear biological motivation. I look forward to a revised version and am confident the authors can address these points with the data and infrastructure they already have in hand.

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Reviewer #1: No

Reviewer #2: Yes: Nika Abdollahi

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PLoS One. 2026 Oct 1;21(10):e0359583. doi: 10.1371/journal.pone.0359583.r002

Author response to Decision Letter 1

26 Aug 2026

1. PLOS style and file naming

Response: We have revised the submission materials to follow the PLOS ONE file-naming and supporting-information conventions. The existing title page, author list, affiliations, and corresponding-author information have been retained unchanged.

Changes made: Main figures are designated Fig1.tif–Fig6.tif. Supporting files and their manuscript captions use the S1 Text, S1 File, S1–S8 Table, and S1–S7 Fig conventions.

2. Financial disclosure and Acknowledgments

Response: The funding source remains unchanged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We request that the online Funding Statement be updated to include this complete wording.

Changes made: The funding sentence was removed from the manuscript Acknowledgments. The complete funding information and role-of-funder statement are provided in the cover letter and will be entered in the Financial Disclosure field.

3. Stable public repository

Response: The minimal dataset and accompanying reproducibility code have been deposited in Figshare and are available at https://doi.org/10.6084/m9.figshare.33320325. The deposited files contain the derived, non-identifying data underlying the reported results and the fully anonymized analytical code.

Changes made: The repository record contains a minimal-data archive, an anonymized code archive, README documentation, and file manifests.

4. Identifying information in the previous code archive

Response: We rebuilt the code archive using only analytical scripts, configuration files, and software-version records required for reproducibility. Names, contact details, participant-level clinical fields, dates, addresses, and institution-specific identifiers were excluded. Public GEO and GSM identifiers were retained only as identifiers of public source datasets.

Changes made: A fully anonymized replacement archive is supplied as S1 File and through the public repository. The previously removed archive has not been reused.

5. Replacement figures

Response: We replaced all six main figures with clearer, high-resolution, individually uploaded TIFF files. These revisions concern image quality, terminology, and file presentation only; no numerical result, analytical definition, protein score, ranking, or conclusion was changed.

Changes made: The previous figure files have been replaced by Fig1.tif–Fig6.tif.

6. Supporting-information captions and citations

Response: Captions for S1 Text, S1 File, S1–S8 Tables, and S1–S7 Figs are now listed at the end of the manuscript. Their in-text citations, captions, and upload file names use matching designations.

Changes made: Supporting-information names and citations were standardized throughout the manuscript.

7. Citation recommendations

Response: Neither reviewer recommended citation of a specific publication. We therefore did not add any reference solely in response to this administrative item.

Changes made: None.

Reviewer #1:

Minor comments-

1. The small datasets might act as outliers and introduce random noise. OP stromal dataset is only 9 patients. Would consider dropping - the results from these may be spurious.

Response:

We agree that the limited sample size of GSE35958 requires explicit evaluation. GSE35958 comprised nine samples, including five OP samples and four controls. It was retained in the primary within-disease robust rank aggregation because this stage was designed to integrate evidence across heterogeneous OP cohorts while limiting dependence on any single dataset. However, GSE35958 did not enter the WGCNA analysis because its sample size was below the predefined minimum of 12 samples required for module construction.

In response to the reviewer’s suggestion, we performed an independent leave-one-dataset-out sensitivity analysis in which GSE35958 was excluded and the OP robust rank aggregation was repeated using GSE56814, GSE56815, and GSE230665. The resulting OP signature was then propagated through the shared-direction, shared-pathway, and protein-prioritization steps. For the conditional protein-ranking comparison, only the shared-disease robustness component was updated, whereas the network, virtual-knockout, single-cell, genetic, and protein-feasibility components were retained at their primary-analysis values.

Exclusion of GSE35958 produced Spearman rank correlations of 0.916 for the OP robust rank aggregation, 0.895 for the shared-axis ranking, and 0.918 for the conditional final protein ranking relative to the primary analysis. All 234 matched module pairs were unchanged because GSE35958 had already been excluded from module construction. Eight of the primary top ten proteins remained within the conditional top ten. HSP90AA1 and RPL7 were not retained because their cross-disease direction consistency was not preserved after exclusion of GSE35958. These results indicate that the overall integrated prioritization was reasonably robust, while also identifying HSP90AA1 and RPL7 as relatively cohort-sensitive candidates that should be interpreted more cautiously.

Changes made:

We added a concise description of this sensitivity analysis to the subsection “Differential analysis and disease-signature construction” in the Methods, immediately after the description of within-disease robust rank aggregation.

We added the following result to “OA and OP each exhibited robust disease signatures before shared-axis construction”, immediately after the paragraph ending with “platform-specific noise or tissue-specific outliers”:

To assess the influence of the small OP stromal-cell dataset, we repeated the OP integration after excluding GSE35958. The resulting OP, shared-axis, and conditional final protein rankings remained correlated with the primary analysis (Spearman ρ = 0.916, 0.895, and 0.918, respectively). All 234 matched module pairs were retained, and eight of the primary top ten proteins remained in the conditional top ten. HSP90AA1 and RPL7 were not retained after exclusion because their cross-disease direction consistency was not preserved (Supplementary Fig. S1 and Supplementary Table S1).

In the limitations paragraph of the Discussion, we added a brief statement indicating that the overall prioritization was stable after exclusion of GSE35958 but that HSP90AA1 and RPL7 showed greater cohort sensitivity.

The GSE35958 entry in Table 1 was clarified as five OP and four control samples, with the analytical role specified as bulk differential analysis and rank aggregation only; the dataset was not used for WGCNA because the total sample size was below 12.

A new Supplementary Figure S1 and Supplementary Table S1 provide the complete sensitivity-analysis results. No corresponding statement was added to the Abstract or Conclusion to avoid overemphasizing a secondary sensitivity analysis.

2. How were weights assigned or computed, and at which stage was weighting done?

Response:

We agree that the original statement that the evidence dimensions were “rescaled and combined” did not provide sufficient methodological detail. Because no labeled clinical outcome or experimentally validated reference ranking was available, the weights were not statistically fitted, learned from the observed results, or optimized to favor particular proteins. Instead, they represented a fixed rule-based evidence hierarchy defined before the final weighted integration.

Shared-disease robustness and virtual-knockout support were assigned weights of 0.25 each because they represented the principal cross-disease reproducibility and network-perturbation evidence. Network centrality and genetic evidence were assigned weights of 0.15 each as intermediate network-level and external human-disease evidence. Cell-type specificity and protein feasibility were assigned weights of 0.10 each as complementary cellular-context and follow-up-feasibility evidence.

Before integration, each of the six evidence dimensions was independently normalized to the 0–1 interval across the candidate-protein set. A dimension without finite variation contributed zero after normalization. Weighting was applied only during the final protein-prioritization step. It was not used in cohort-level differential analysis, robust rank aggregation, pathway enrichment, shared-axis construction, or module matching.

Changes made:

We replaced the original paragraph under “Protein-level evidence integration and final prioritization”, beginning with “Shared-axis candidates were mapped to proteins” and ending with “the final ranking reported in Table 2,” with an expanded description of the six evidence dimensions, their normalization, the rationale for their relative weights, and the stage at which weighting was applied.

The revised text explicitly reports the following weights:

Shared-disease robustness, 0.25; network centrality, 0.15; virtual-knockout support, 0.25; cell-type specificity, 0.10; genetic evidence, 0.15; and protein feasibility, 0.10.

The complete definitions, normalization rules, weight-assignment rationale, and weighting stage are now reported in Supplementary Methods S1 and Supplementary Table S2.

Response:

We agree and have substantially expanded methodological reporting without changing the original analytical framework. The revised Methods now specify the decision rules governing differential analysis, robust rank aggregation, pathway analysis, WGCNA, cross-disease module matching, protein-level evidence integration, local CARNIVAL reconstruction, virtual-knockout scoring, and single-cell contextual analysis.

The supplementary description now reports, among other details, the probe-to-gene collapse rule; the ordering and inclusion criteria used for robust rank aggregation; multiple-testing thresholds for biological-process enrichment; WGCNA block-eligibility criteria, soft-threshold selection, module size and merging settings, module–trait criteria, and cross-disease module-matching thresholds; the definitions and normalization of all six protein-level evidence dimensions; and the complete weighting scheme.

For CARNIVAL, we now report the software version, solver, regularization setting, candidate pool, local-network construction sequence, connector limits, minimum network requirements, four biological output panels, knockout implementation, and rescue-score composition. CARNIVAL version 2.7.2 was used with the lpSolve solver and a beta regularization weight of 0.2. No customized solver time limit or optimality-gap threshold was introduced.

For the single-cell layer, we now report the normalization procedure, marker-supported annotation rule, evaluated compartments, minimum number of cells, sampling limit and random seed, gene-detection and variability filters, network number, maximum cells per network, manifold dimensions, iteration limit, processing cores, and perturbation-score definition.

Changes made:

We expanded the corresponding main-text Methods subsections while keeping the principal manuscript concise:

• “Differential analysis and disease-signature construction”

• “Shared pathological axis and disease-associated module matching”

• “Protein-level evidence integration and final prioritization”

• “Virtual knockout analysis”

• “Single-cell validation and scTenifoldKnk analysis”

• “Statistical analysis and reproducibility”

We added Supplementary Methods S1, organized into six directly corresponding methodological sections:

1. Bulk differential analysis and robust rank aggregation

2. Gene Ontology enrichment and shared-axis construction

3. WGCNA construction and cross-disease module matching

4. Protein-level evidence integration and weighted prioritization

5. Local CARNIVAL network reconstruction and virtual knockout

6. Single-cell contextual and perturbation analysis

We also added Supplementary Table S2, which provides a structured record of the evidence-dimension definitions, weighting scheme, bulk and pathway parameters, WGCNA and module-matching criteria, CARNIVAL settings and biological output panels, single-cell settings, and exact software versions. These additions improve reproducibility and reporting transparency but do not change the primary analytical definitions or results.

Reviewer #2:

Major Points

1. Statistical null models for cross-disease module matching

Response

We thank the reviewer for this important and constructive comment. We agree that the original deterministic matching criteria, although prespecified and internally consistent, were not sufficient to establish whether the observed cross-disease module correspondence exceeded that expected by chance after accounting for module-size distributions and the shared gene background.

In the original analysis, module matching was designed as a structured Layer 3 evidence component rather than as a formal statistical enrichment test. The 234 module pairs were retained when they satisfied at least one prespecified overlap criterion involving shared-pathway genes, enriched functional terms, or joint gene overlap and Jaccard similarity. This approach allowed module-level evidence to contribute to the multilayer protein-prioritization framework while preserving the independently constructed OA and OP network structures. However, we recognize that referring to these pairs simply as “matched module pairs” could be interpreted as implying that all 234 pairs represented statistically significant cross-disease correspondence. The original manuscript did not distinguish sufficiently between deterministic criteria-positive pairs and statistically calibrated pair-specific correspondence. We therefore accepted the reviewer’s recommendation.

We added an independent permutation-based null analysis without changing the original WGCNA modules, deterministic matching thresholds, Layer 3 definitions, candidate pool, scoring weights, or protein ranking. The original 234 criteria-positive pairs were first reconstructed exactly from the frozen primary outputs. All 29 retained OA modules and 16 retained OP modules were then evaluated, corresponding to 464 possible OA-OP module combinations.

For the gene-membership null, module labels were shuffled independently within each WGCNA block over 10,000 permutations. This procedure preserved the block-specific gene universe, the number of modules, and the exact observed size of every module while disrupting the association between gene identity and module assignment. Shared-pathway overlap and joint gene-overlap/Jaccard support were recalculated after each permutation. Functional-term overlap was calibrated using term-count-matched randomization over the 2,145 significant Gene Ontology terms observed across the retained modules.

Empirical P values were calculated separately for the three original matching components. Only components for which the observed module pair satisfied the corresponding prespecified matching criterion were eligible to contribute to pair-level significance. The smallest eligible component P value was Bonferroni-adjusted across the three matching components, followed by Benjamini-Hochberg correction across all 464 tested module pairs.

Of the 234 pairs meeting the original deterministic criteria, 53 retained pair-specific support at empirical FDR < 0.05. An independent repeat using a second random seed recovered the same 53 pairs, indicating stable identification of the statistically supported subset. However, the total number of criteria-positive pairs did not exceed the global null expectation: 234 pairs were observed compared with a null mean of 254.83 and a 95% null interval of 242-267; the upper-tail empirical P value was 0.9995.

We therefore revised the interpretation of the module layer. The 234 pairs are now described as “criteria-positive module pairs,” whereas the 53 pairs meeting empirical FDR < 0.05 are described as “statistically supported pair-specific correspondences.” We no longer interpret the total number of 234 pairs as evidence of globally enriched OA-OP module similarity. Instead, the revised manuscript states that statistical module-level support is concentrated in selected cross-disease module pairs.

We retained the original protein scores and ranking because the permutation analysis was designed to calibrate the inferential strength of the module correspondences, not to redefine the prespecified primary scoring framework after observing the results. Retrospectively changing the candidate pool, weights, or score calculation on the basis of the new null results would introduce a post hoc modification to the primary analysis. The calibration was therefore added as an independent statistical annotation. This decision also reflects the multilayer nature of the ranking: module correspondence was one contributory dimension and was not the sole basis for protein prioritization.

Among the top 10 proteins, HLA-DRB1, HSP90AA1, CTSK, RPL7, CLEC3B, and SPP1 occurred in at least one FDR-supported module pair. PRG4, HLA-DRA, TIMP1, and APOE did not receive an FDR-supported module-pair annotation but retained their original ranks through complementary evidence from disease-level robustness, network context, cell-context support, genetic evidence, protein feasibility, or local virtual-knockout analysis. Consequently, the revised manuscript retains the overall multilayer protein-prioritization conclusion while narrowing the specific network-level claim from global module convergence to selected pair-specific correspondence.

This calibrated interpretation is also the reason that the revised manuscript continues to describe a shared OA-OP pathological axis. That conclusion does not depend solely on the total number of module pairs; it is supported jointly by concordant disease-level expression, shared pathway enrichment, selected statistically supported module correspondences, protein-level evidence integration, local CARNIVAL analysis, and single-cell contextualization. The revised text therefore preserves the overall hypothesis-generating framework while avoiding an unsupported claim of globally significant module similarity.

Changes Made

1. Abstract: We replaced the statement that cross-disease comparison “identified 234 matched module pairs” with a calibrated description distinguishing 234 criteria-positive pairs from the 53 pairs retaining empirical FDR < 0.05. We also clarified that the total number of criteria-positive pairs did not exceed the global null expectation.

2. Results: We rewrote the Layer 3 results to report all 464 tested OA-OP module combinations, the 234 criteria-positive pairs, the 53 FDR-supported pairs, the second-seed stability result, and the global null statistics. The revised Results now describe selected pair-specific correspondence rather than globally enriched module matching.

3. Protein-level Results: We added the FDR-supported module-pair counts for the top 10 proteins and clarified that the independent calibration did not alter the original composite scores or ranking.

4. Methods: We corrected and fully stated the three original deterministic module-matching criteria. We then added the complete permutation procedure, including preservation of block-specific gene universes and exact module sizes, functional-term randomization, empirical P-value calculation, within-pair Bonferroni correction, Benjamini-Hochberg correction across 464 pairs, and the independent second-seed stability analysis.

5. Discussion: We revised the interpretation of the module layer to state that statistical support was concentrated in selected module pairs and did not demonstrate a global excess of OA-OP module similarity.

6. Limitations: We added the absence of global module-pair enrichment as an explicit limitation and clarified that the module layer should be interpreted as pair-specific concordance.

7. Conclusion: We replaced the broad phrase “matched modules” with “selected pair-specific module correspondences.” The overall multilayer and hypothesis-generating conclusion was retained.

8. Figure 1: Panel B now includes “criteria-positive module pairs” followed by “pair-specific permutation calibration.” Panel C now identifies Layer 3 as deterministic module evidence and states that pair-level significance was calibrated independently. The original Layer 3 count of 810 was retained because the permutation analysis did not redefine the primary candidate set.

9. Figure 3: Panel A now identifies the displayed counts as cumulative evidence coverage under deterministic criteria. Panel C distinguishes 234 criteria-positive pairs from 53 FDR-supported pairs. Panel D now displays the original criteria-positive counts as light bars and the FDR-supported subset as dark overlays, together with the global null result.

10. Figure 4 and Table 2: The original protein scores were retained. Table 2 now reports the number of FDR-supported module pairs for each leading protein, and the figure/table notes clarify that permutation calibration was not used to reweight or rerank candidates.

11. Supplementary Figure S2: We added the global permutation distribution, block-pair calibration, and pair-specific empirical FDR matrix across all 464 module combinations.

12. Supplementary Table S3: We added the global null summary, block-pair results, empirical P values and FDR estimates for the 234 criteria-positive pairs, and the mapping between FDR-supported module pairs and the leading proteins.

13. Supplementary Methods: We added a dedicated section describing the null-model construction, preserved quantities, empirical significance calculation, multiple-testing correction, and stability assessment.

14. Reproducibility statement: We expanded the listed reproducible outputs to include criteria-positive module pairs, pair-level empirical P values and FDR estimates, and global and block-pair null summaries.

2. CARNIVAL virtual-knockout transparency and sensitivity

Response: We thank the reviewer for identifying the need to distinguish the primary local CARNIVAL result from scores that remain stable across alternative network configurations. We expanded both solver reporting and configuration-sensitivity assessment.

The primary analysis used CARNIVAL version 2.7.2 with lpSolve version 5.6.23, a beta regularization weight of 0.2, and the software-default linear-programming settings. No customized solver time limit or optimality-gap criterion was imposed.

The 20 prespecified candidates were evaluated across six combinations of measurement-set size and connector limit. Of 120 protein-configuration combinations, 62 were evaluable. HLA-DRB1 exceeded the prespecified model-derived threshold in all four evaluable configurations and retained a score of 2.25. HSP90AA1 exceeded the threshold in four of six configurations, with scores of 1.25 under the eight- and six-measurement settings and 0 under both four-measurement settings. CTSK was evaluable in all six configurations and had a score of 0 throughout. These results identify HLA-DRB1 as the more configuration-stable model-derived candidate and qualify the HSP90AA1 result as configuration-dependent.

Changes made: Complete solver settings, score definitions, evaluability criteria, and configuration-level results were added to the Methods, S1 Text, S2 Table, S3 Fig, and S4 Table. Figure 5 and Table 2 were revised to use model-derived CARNIVAL terminology. No analytical score, threshold, or protein ranking was changed.

3. Calibration of single-cell perturbation scores

Response

We thank the reviewer for highlighting that the original scTenifoldKnk perturbation scores lacked a gene-matched reference distribution and could therefore be influenced by baseline expression and network connectivity. We agree that raw perturbation scores alone cannot establish whether a candidate produces an unusually strong network effect relative to genes with comparable expression and topological properties.

We therefore performed a matched-null calibration for all 39 evaluable candidate–compartment pairs. For each candidate, 200 control genes were selected from the same 600- or 601-gene network-size stratum and matched according to mean log-normalized expression and log-transformed wild-type weighted out-degree. Each matched control was evaluated using the same cell subsample, network-inference procedure, manifold-alignment settings, affected-gene ordering, and top-50 perturbation-score definition as the corresponding candidate. The observed score was then compared with its matched-null distribution. We calculated a null-calibrated Z score and a one-sided empirical P value using the plus-one correction, followed by Benjamini–Hochberg adjustment across all 39 evaluable pairs.

Eleven candidate–compartment pairs had nominal empirical P values below 0.05, and nine remained supported after global false-discovery-rate correction. These comprised CTSK, CLEC3B, TIMP1, PRG4, and HSP90AA1 in OA macrophages; RPL7 in OA fibroblasts; CLEC3B in OP bone marrow mesenchymal stromal cells; and TIMP1 and RPL7 in OP osteoclast precursors. In contrast, several candidates with relatively high raw perturbation scores, including MMP9, HLA-DRA, HLA-DRB1, and CTSK in OP compartments, did not exceed their matched-null backgrounds after correction. Thus, the calibration distinguished absolute perturbation magnitude from effects that were unusually large relative to expression- and degree-matched genes.

We also performed a sensitivity analysis restricted to the 100 closest matched controls. This analysis retained the same nine globally FDR-supported pairs and produced concordant significance classifications for all 39 evaluable pairs. Matching diagnostics were additionally examined and are now reported transparently, including the comparatively larger matching distances for OA macrophage PRG4 and HSP90AA1. These results refine the single-cell findings as calibrated, compartment-specific contextual evidence rather than direct mechanistic validation.

Changes made

1. We expanded the Single-cell contextual analysis subsection of the Methods to specify the evaluated cellular compartments, candidate panel, scTenifoldKnk settings, and definition of the raw perturbation score.

2. We added a dedicated Matched-null calibration of single-cell perturbation scores subsection to Supplementary Methods S1. This section now describes the network-size strata, matching variables, standardized matching distance, selection of 200 matched controls, null Z-score calculation, plus-one empirical P-value calculation, Benjamini–Hochberg correction across 39 tests, and nearest-100-control sensitivity analysis.

3. We rewrote the corresponding Results subsection to report the number of evaluable pairs, nominally supported pairs, globally FDR-supported pairs, compartment-specific results, matching diagnostics, and sensitivity-analysis results.

4. We revised Figure 6C–D to present matched-null Z scores and the leading calibrated OA and OP candidate–compartment pairs instead of relying only on raw perturbation scores.

5. We added Supplementary Figure S4, which reports the complete matched-null calibration, observed scores relative to matched-null intervals, empirical significance results, and the nearest-100-control sensitivity analysis.

6. We revised Table 3 to report the nine globally FDR-supported candidate–compartment pairs, including observed scores, matched-null summaries, Z scores, empirical P values, adjusted P values, and matching diagnostics.

7. We added Supplementary Table S5, containing results for all 39 evaluable pairs, nearest-100 sensitivity results, matching diagnostics, identities of all 7,800 matched controls, complete matched-control score distributions, group information, and variable definitions.

8. We revised the Discussion and limitations to distinguish raw perturbation magnitude from matched-null outlier status and to describe the single-cell findings as computational, compartment-specific contextual support requiring independent experimental confirmation.

4. Cell-composition and abundance confounding

Response

We agree that the recurrent prioritization of MHC-II and ribosomal proteins could partly reflect differences in cellular abundance or broader transcriptional activity rather than disease-specific regulation. We therefore performed two complementary sensitivity analyses. First, inferred immune and stromal abundance scores were examined in four eligible mixed-tissue cohorts, and candidate effects were re-estimated after adjustment for the leading abundance-score components. None of the 40 population-by-cohort differences remained significant after global FDR correction, and 36 of 40 candidate–cohort effects retained their original direction. However, 50 of 400 residual candidate–population associations remained significant, indicating that some candidates continued to track cellular abundance. Second, HLA-DRA, HLA-DRB1, RPL7, and HSP90AA1 were adjusted for matched MHC-II, ribosomal, or protein-folding transcriptional programs. Direction was retained in 24 of 27 evaluable comparisons, although the MHC-II candidates showed greater attenuation. We have therefore revised the manuscript to interpret MHC-II and ribosomal proteins as context-sensitive candidates rather than confirmed disease-specific regulators.

Change made

We added a bulk cell-composition and transcriptional-program sensitivity analysis to the Methods and Results, qualified the interpretation of HLA-DRA, HLA-DRB1, and RPL7 in the Discussion and Limitations, and added revised Supplementary Figure S5 and Supplementary Table S6 containing the complete four-cohort results.

5. Hubness and annotation bias in prioritization

Response:

We thank the reviewer for highlighting the potential influence of network hubness and annotation density on protein prioritization. We agree that betweenness centrality may favor highly connected proteins and that well-characterized proteins may receive greater support from public annotation resources. We therefore performed independent degree-aware and annotation-aware sensitivity analyses using the original candidate pool and prespecified evidence weights.

The primary integrated score was moderately correlated with STRING degree (Spearman ρ = 0.476, P < 2.2 × 10−16). After replacing the original centrality dimension with degree-corrected centrality, 9 of the primary top 10 and 165 of the top 200 proteins were retained, with a rank correlation of ρ = 0.882 among the primary top 200. Complete omission of network centrality retained 9 of the top 10 and 190 of the top 200 proteins. HLA-DRB1 and HSP90AA1 remained ranked first and second in both analyses.

Among the 200 proteins evaluated using external resources, the primary score correlated with generic UniProt/HPA annotation count (ρ = 0.388, P = 1.36 × 10−8). After omission of protein feasibility, this association decreased to ρ = 0.106 and was no longer statistically significant (P = 0.133). Simultaneous omission of genetic evidence and protein feasibility retained 7 of the primary top 10 and 185 of the top 200 proteins. HLA-DRB1 and HSP90AA1 remained first and second, whereas SPP1 and APOE moved to ranks 34 and 38, respectively. These results indicate that the two leading candidates were not prioritized solely because of network degree, while the positions of SPP1 and APOE depended more strongly on external evidence dimensions.

Change made:

We added the degree-correction procedure, annotation-density definitions, and evidence-dimension omission analyses to the Methods and Supplementary Methods S1. The corresponding correlations, rank-retention results, and candidate-specific rank changes were added to the protein-prioritization Results. The Discussion now distinguishes candidates supported across multiple evidence dimensions from those more dependent on specific external evidence sources, and the relevant limitation was expanded. Complete results are presented in Supplementary Figure S6 and Supplementary Table S7. The primary protein ranking and the values reported in Figure 4 and Table 2 were not changed.

Single-Cell Quality Control

Please add standard mitochondrial-fraction and doublet filtering (or justify their omission), and report cell counts before and after QC per dataset.

Response

We thank the reviewer for identifying the limitations of the original single-cell quality-control procedure. We agree that filtering based only on detected-gene and compartment-size criteria did not directly address excessive mitochondrial transcript fractions or predicted doublets. We therefore performed an independent external quality-control sensitivity analysis before normalization and network reconstruction.

Cells or nuclei with fewer than 200 detected genes were excluded. Mitochondrial-transcript thresholds were set at 20% for single-cell RNA-sequencing data and 5% for single-nucleus RNA-sequencing data. Doublets were identified separately within each sample using scDblFinder version 1.20.2 and removed before downstream analysis. The quality-controlled matrices were then processed using the same normalization, compartment definitions, candidate panel, cell-subsampling limits, gene-selection criteria, scTenifoldKnk parameters, perturbation-score definition, and matched-null calibration procedure as the primary analysis.

External quality control retained 125,090 of 161,470 cells. The quality-controlled analysis identified 10 globally FDR-supported candidate-compartment pairs. Pair-level localization was sensitive to quality control: only HSP90AA1 and TIMP1 in OA macrophages remained supported in the same compartment, although five of the six genes supported in the primary analysis retained evidence in at least one quality-controlled compartment. Importantly, recalculation of the cell-context evidence retained all primary top 10 proteins and 19 of the primary top 20 proteins, with an overall rank correlation greater than 0.999; HLA-DRB1 and HSP90AA1 remained ranked first and second.

Ambient-RNA correction could not be applied consistently because unfiltered droplet matrices containing empty droplets were not available for all three public datasets. We now report this constraint explicitly. The revised interpretation therefore treats the single-cell results as quality-control-sensitive cellular contextual evidence rather than fixed cell-type localization or experimental validation.

Changes made

We added the external quality-control thresholds, sample-wise doublet detection, and quality-controlled reanalysis procedure to the Methods. We added dataset-, sample-, and compartment-level cell-retention statistics and the comparison between primary and quality-controlled perturbation results to the Results. The Discussion and Limitations now state that precise candidate-compartment assignments were not uniformly stable, whereas the overall protein-ranking hierarchy remained stable. Table 1 now specifies the role of each single-cell dataset, and Table 3 directly compares primary and quality-controlled candidate-compartment results. Figure 6 remains the primary matched-null analysis, while Supplementary Figure S7 and Supplementary Table S8 report the complete external quality-control sensitivity analysis.

Minor Points and Reproducibility

Response

Thank you for highlighting the importance of version-specific reproducibility. We agree that WGCNA, CARNIVAL, and scTenifoldKnk outputs can depend on the software environment. We have therefore reported the exact R and package versions used for the analyses in Supplementary Methods S1 and included the complete session information and package-version manifest in the reproducibility archive. These additions document the computational environment without altering the analytical definitions, parameters, or results.

Changes made

We added a dedicated “Software environment” subsection to Supplementary Methods S1, specifying R version 4.4.1 and the exact versions of the principal packages, including WGCNA 1.74, CARNIVAL 2.7.2, scTenifoldKnk 1.0.3, limma 3.60.6, RobustRankAggreg 1.2.1, clusterProfiler 4.12.6, and the supporting single-cell and network-analysis packages. Complete sessionInfo() and package-version records were also included in the reproducibility archive.

Response

We agree that the scope of the study should be explicit. The opening paragraph of the Methods now states that this study was a secondary reanalysis of publicly available, de-identified GEO transcriptomic datasets and that no new participant recruitment, biospecimen collection, or transcriptomic data generation was undertaken. The GEO series and their analytical roles are listed in Table 1.

Changes made

We revised the opening of the Methods to distinguish secondary public-data reanalysis from primary data generation and retained the complete dataset-level description in Table 1.

Response

Thank you for requesting clarification of the cross-platform directionality rule. We have clarified that directionality was determined from cohort-specific case-control log-fold-change estimates rather than by directly comparing expression intensities across platforms. For each mapped gene, cohort-specific effects were averaged separately within OA and OP. Positive mean effects in both diseases defined concordant upregulation, negative mean effects in both diseases defined concordant downregulation, and genes with opposing signs were excluded from the shared axis. The full direction-score definition remains available in Supplementary Methods S1.

Changes made

We added a concise definition of concordant upregulation and downregulation to the main Methods and retained the mathematical direction-score rule in Supplementary Methods S1.

- Consider softening causal language ("regulatory mechanism", "rescue") to reflect that the evidence is associative and model-based until the null-model and sensitivity analyses above are in place.

Response

We agree that the computational outputs should not be interpreted as experimental evidence of causality or biological rescue. We therefore replaced unqualified “rescue” claims with “model-derived CARNIVAL score” or “above the prespecified model-derived threshold” throughout the Abstract, Results, Discussion, Methods, tables, and figure legends. “Virtual knockout” was retained as the established name of the computational procedure, but its outputs are now consistently described as model-derived. The Limitations and Conclusion explicitly state that these analyses provide functional prioritization and do not establish causal regulators or therapeutic effects.

Changes made

We revised the manuscript-facing terminology for CARNIVAL results, removed statements implying confirmed network rescue, and updated the corresponding Table 2 categories and Figure 4, Figure 5, and Supplementary Figure S3 labels. No scores, thresholds, rankings, or analytical results were changed.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0359583.r003

Decision Letter 1

Jung-Eun Kim

15 Sep 2026

<p>Disease-first public-data integration with local virtual knockout prioritizes shared proteins linking osteoarthritis and osteoporosis

PONE-D-26-26842R1

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PLoS One. doi: 10.1371/journal.pone.0359583.r004

Acceptance letter

Jung-Eun Kim

PONE-D-26-26842R1

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This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Leave-one-dataset-out sensitivity analysis excluding GSE35958.

(XLSX)

pone.0359583.s001.xlsx^{(18.5KB, xlsx)}

S2 Table. Analytical parameters, evidence definitions, weighting scheme, and software versions.

(XLSX)

pone.0359583.s002.xlsx^{(24.2KB, xlsx)}

S3 Table. Permutation-based calibration of cross-disease module matching.

(XLSX)

pone.0359583.s003.xlsx^{(126.1KB, xlsx)}

S4 Table. Configuration-level stability of model-derived local CARNIVAL virtual-knockout results.

(XLSX)

pone.0359583.s004.xlsx^{(24KB, xlsx)}

S5 Table. Matched-null calibration of single-cell scTenifoldKnk perturbation scores.

(XLSX)

pone.0359583.s005.xlsx^{(1.7MB, xlsx)}

S6 Table. Bulk cell-composition and transcriptional-program sensitivity analyses of prioritized proteins.

(XLSX)

pone.0359583.s006.xlsx^{(193.6KB, xlsx)}

S7 Table. Hubness and annotation-density sensitivity of protein prioritization.

(XLSX)

pone.0359583.s007.xlsx^{(2.1MB, xlsx)}

S8 Table. External quality-control sensitivity of single-cell perturbation results.

(XLSX)

pone.0359583.s008.xlsx^{(994.4KB, xlsx)}

S1 Text. Supplementary methods: detailed analytical parameters and decision rules.

(DOCX)

pone.0359583.s009.docx^{(38.7KB, docx)}

S1 Fig. Leave-one-dataset-out sensitivity analysis excluding GSE35958.

(TIF)

pone.0359583.s010.tif^{(621KB, tif)}

S2 Fig. Permutation-based calibration of cross-disease module matching.

(TIF)

pone.0359583.s011.tif^{(3.3MB, tif)}

S3 Fig. Configuration sensitivity of local CARNIVAL virtual knockout.

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pone.0359583.s012.tif^{(3.5MB, tif)}

S4 Fig. Matched-null calibration of single-cell perturbation scores.

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pone.0359583.s013.tif^{(486.8KB, tif)}

S5 Fig. Bulk cell-composition and transcriptional-program sensitivity analysis of prioritized proteins.

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pone.0359583.s014.tif^{(2.9MB, tif)}

S6 Fig. Hubness and annotation-density sensitivity of protein prioritization.

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pone.0359583.s015.tif^{(2.7MB, tif)}

S7 Fig. External quality-control sensitivity analysis of single-cell perturbation results.

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pone.0359583.s016.tif^{(786.1KB, tif)}

S1 File. Analytical code and reproducibility resources.

Archive containing the primary analysis scripts, revision-analysis scripts, scoring weights, software-version records, session information, and file manifest.

(ZIP)

pone.0359583.s017.zip^{(160.9KB, zip)}

S2 File. Processed data underlying the primary analyses.

(ZIP)

pone.0359583.s018.zip^{(4.3MB, zip)}

Attachment

Submitted filename: Point-by-point response.docx

pone.0359583.s020.docx^{(36.8KB, docx)}

Data Availability Statement

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PERMALINK

Disease-first public-data integration with local virtual knockout prioritizes shared proteins linking osteoarthritis and osteoporosis

Jiayin Pang

Yu Li

Zhichao Qi

Wenbin Yang

Roles

Abstract

Introduction

Results

Disease-first cohort organization preserved within-disease structure before cross-disease inference

Fig 1. Study design and analytical framework for disease-first multilayer integration of shared osteoarthritis-osteoporosis pathology.

Table 1. Public datasets included in the study. Summary of bulk and single-cell public datasets used in the disease-first workflow, including their analytical role in the manuscript.

OA and OP each exhibited robust disease signatures before shared-axis construction

Fig 2. Robust disease signatures and enriched biological programs in osteoarthritis and osteoporosis.

A three-layer framework integrated shared OA-OP evidence

Fig 3. Layered shared-axis evidence and permutation-calibrated OA-OP module correspondence.

Protein-level integration prioritized proteins across complementary evidence dimensions

Fig 4. Protein-level evidence integration and prioritization of shared OA-OP candidates.

Table 2. Top 10 prioritized proteins in shared OA-OP pathology.

Composition-aware analyses qualified recurrent MHC-II and ribosomal signals

Local CARNIVAL scores showed configuration-stable and configuration-sensitive model-derived profiles

Fig 5. Virtual-knockout results in the selected local CARNIVAL configuration.

Matched-null calibration identified context-specific single-cell perturbation outliers

Fig 6. Primary single-cell localization and matched-null calibration of perturbation scores.

Table 3. Primary and external quality-control sensitivity results for single-cell candidate-compartment perturbation pairs.

External quality-control sensitivity analysis qualified cell-compartment assignments

Discussion

Limitation

Conclusion

Methods

Ethics statement

Public datasets and overall analytical design

Differential analysis and disease-signature construction

Bulk cell-composition and transcriptional-program sensitivity analysis

Shared pathological axis and disease-associated module matching

Protein-level evidence integration and final prioritization

Virtual knockout analysis

Single-cell contextual analysis, external quality control, and scTenifoldKnk analysis

Statistical analysis and reproducibility

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Jung-Eun Kim

Roles

Author response to Decision Letter 1

Decision Letter 1

Jung-Eun Kim

Roles

Acceptance letter

Jung-Eun Kim

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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