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Cited by 786Single-cell and spatial transcriptomicsBioinformatics and Genomic NetworksGene expression and cancer classification

hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data

Open access
Morabito, Samuel, Reese, Fairlie, Rahimzadeh, Negin, Miyoshi, Emily, Swarup, Vivek·2023·Cell Reports Methods
DOI 10.1016/j.crmeth.2023.100498PMC PMC10326379

Published version of a preprint

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Abstract

Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high-dimensional transcriptomics data such as single-cell and spatial RNA sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules. hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly 1 million cells.

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Written from the paper’s full text.

TL;DR

hdWGCNA is an R package/framework for co-expression network analysis in single-cell and spatial transcriptomics data, extending Seurat and adding metacell/metaspot aggregation, network inference, module detection, enrichment, and visualization. The authors demonstrate it across human brain, mouse brain spatial, long-read isoform, ASD, AD, and million-cell PBMC datasets, identifying disease- and cell-type-relevant modules.

Key findings

  • hdWGCNA identified 96 co-expression modules across six major cell types in 36,671 human PFC snRNA-seq cells from 11 cognitively normal donors; ODC modules were reproducible in an independent dataset, with all ODC-specific modules significantly preserved (Z summary preservation ≥5).
  • In mouse brain Visium ST, hdWGCNA found 12 spatial modules from 2,696 anterior and 3,353 posterior spots, including white-matter myelination module SM1 and ventricle/BBB-associated SM4 enriched for hemoglobin subunits.
  • Isoform-level analysis of 2,190 radial glia lineage cells from mouse hippocampus ScISOrSeq yielded 11 modules; 61.5% of retained genes had a single isoform, 18.2% had multiple isoforms all in the same module, and 20.4% had isoforms spread across several modules.
  • In ASD inhibitory neurons (20,249 INH nuclei), hdWGCNA found 14 modules; modules INH-M11, INH-M13, and INH-M3 were enriched for ASD-associated genes, and all modules except INH-M4 showed significant ME differences in at least one INH subpopulation. In AD microglia consensus analysis, four modules were identified, with MG-M1 containing DAM genes and MG-M2 containing homeostatic markers.

Methods

  • Design: hdWGCNA builds on WGCNA-style co-expression network analysis, using metacells for scRNA-seq or metaspots for spatial transcriptomics to reduce sparsity, then signed adjacency, soft-power thresholding, signed topological overlap matrix (TOM), and Dynamic Tree Cut for module detection.
  • Data: applications included human PFC snRNA-seq (36,671 cells), mouse brain Visium ST (6,049 spots total), mouse hippocampus ScISOrSeq (6,832 nuclei; 2,190 radial glia lineage cells), ASD snRNA-seq (20,249 inhibitory neurons), AD microglia from three snRNA-seq datasets (9,904 microglia nuclei from 189,127 total nuclei), and a PBMC scRNA-seq dataset (965,363 cells retained).
  • Analysis: module eigengenes (MEs) computed by SVD with optional Harmony correction; downstream tests included differential module eigengene analysis, module preservation with permutations, EGAD functional coherence, XGBoost hub-gene prediction, Monocle3 pseudotime, scDRS polygenic risk scoring, and projection of bulk RNA-seq modules into snRNA-seq.

Limitations

  • Stated: single-cell transcriptomic measurements are noisy, with technical noise from dropout events and experimental protocols; hdWGCNA attempts to address this by constructing networks in metacell or metaspot profiles and by using module preservation testing for reproducibility.
  • Evident from the text: the metacell algorithm requires a dimensional reduction of the input matrix, and technical artifacts in that reduction can influence metacell construction; the optimal number of cells per metacell may differ across cell types and tissues.
  • Evident from the text: pseudo-bulk aggregation is suggested as likely suitable for co-expression network analysis only given sufficient sample size, and several analyses rely on external datasets and parameter choices for validation.

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Integrity signals

Registry

Crossref · 10.1016/j.crmeth.2023.100498

Cites

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Statistics

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Claims the paper makes

89 of 95 cited sources checked · 64 supported · 0 flagged · accuracy not yet measured

  • Findingown data

    Iterative network analysis of six major cell types in the Zhou et al. human PFC snRNA-seq dataset yielded distinct network structures and sets of gene modules.

    “We constructed metacells and performed co-expression network analysis for each major cell type in the human PFC dataset11 using the standard hdWGCNA workflow, yielding distinct network structures and sets of gene modules (Data S2 and S3).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [11] Zhou Y. Song W.M. Andhey P.S. Swain A. Levy T. Miller K.R. Poliani P.L. Cominelli M. Grover S. Gilfillan S. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease Nat. Med. 26 2020 131 142 10.1038/s41591-019-0695-9 31932797 PMC6980793 source
  • Findingcitation

    Using long-read scRNA-seq data from the mouse hippocampus, hdWGCNA uncovered splicing isoform co-expression networks in the radial glia lineage involved in cell fate specification.

    “Using long-read scRNA-seq data from the mouse hippocampus,8 we uncovered splicing isoform co-expression networks in the radial glia lineage involved in cell fate specification.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [8] Joglekar A. Prjibelski A. Mahfouz A. Collier P. Lin S. Schlusche A.K. Marrocco J. Williams S.R. Haase B. Hayes A. A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain Nat. Commun. 12 2021 463 10.1038/s41467-020-20343-5 33469025 PMC7815907 source
  • Findingown data

    Consensus co-expression network analysis of microglia from three Alzheimer's disease snRNA-seq studies yielded multiple gene modules corresponding to disease-associated microglia and polygenic risk of AD.

    “We performed consensus co-expression network analysis of microglia from three Alzheimer’s disease (AD) snRNA-seq studies,10,11,12 yielding multiple gene modules corresponding to disease-associated microglia and polygenic risk of AD.”
    Quote in the paperSubstantial claim3 of 3 sources checked
    • cites [10] Mathys H. Davila-Velderrain J. Peng Z. Gao F. Mohammadi S. Young J.Z. Menon M. He L. Abdurrob F. Jiang X. Single-cell transcriptomic analysis of Alzheimer’s disease Nature 570 2019 332 337 10.1038/s41586-019-1195-2 31042697 PMC6865822 source
    • cites [11] Zhou Y. Song W.M. Andhey P.S. Swain A. Levy T. Miller K.R. Poliani P.L. Cominelli M. Grover S. Gilfillan S. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease Nat. Med. 26 2020 131 142 10.1038/s41591-019-0695-9 31932797 PMC6980793 source
    • cites [12] Morabito S. Miyoshi E. Michael N. Shahin S. Martini A.C. Head E. Silva J. Leavy K. Perez-Rosendahl M. Swarup V. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease Nat. Genet. 53 2021 1143 1155 10.1038/s41588-021-00894-z 34239132 PMC8766217 source
  • Findingown data

    Alternative gene scoring methods such as UCell or Seurat's AddModuleScore produce scores that are correlated with module eigengenes.

    “Optionally, hdWGCNA can use alternative gene scoring methods such as or UCell18 or Seurat’s AddModuleScore function, and we show that these scores are correlated with MEs (Figure S3).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [18] Andreatta M. Carmona S.J. UCell: robust and scalable single-cell gene signature scoring Comput. Struct. Biotechnol. J. 19 2021 3796 3798 10.1016/j.csbj.2021.06.043 34285779 PMC8271111 source
  • Findingown data

    All tested metacell approaches were suitable for downstream network analysis in the CD34+ hematopoietic stem and progenitor cell dataset.

    “We applied hdWGCNA to a dataset of CD34+ hematopoietic stem and progenitor stem cells16 using two additional metacell approaches14,16 and found that all approaches were suitable for downstream network analysis (Figure S2; Data S1).”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [14] Ben-Kiki O. Bercovich A. Lifshitz A. Tanay A. Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis Genome Biol. 23 2022 100 10.1186/s13059-022-02667-1 35440087 PMC9019975 source
    • cites [16] Persad S. Choo Z.-N. Dien C. Sohail N. Masilionis I. Chaligné R. Nawy T. Brown C.C. Sharma R. Pe’er I. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data Nat. Biotechnol. 2023 10.1038/s41587-023-01716-9 PMC10713451 36973557 source
  • Classified as probably not a substantial claim. 71
  • May combine two claims. 35

  • Classified as probably not a substantial claim.

    Premisecitation

    Co-expression networks are based on transformed pairwise correlations of input features, resulting in a quantitative measure of relatedness between genes.

    “Co-expression networks are based on transformed pairwise correlations of input features, resulting in a quantitative measure of relatedness between genes.1,2”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [1] Langfelder P. Horvath S. WGCNA: an R package for weighted correlation network analysis BMC Bioinf. 9 2008 559 10.1186/1471-2105-9-559 PMC2631488 19114008 source
    • cites [2] Yip A.M. Horvath S. Gene network interconnectedness and the generalized topological overlap measure BMC Bioinf. 8 2007 22 10.1186/1471-2105-8-22 PMC1797055 17250769 source

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Hierarchical clustering on the network structure allows uncovering functional modules of genes whose expression profiles are tightly intertwined, which typically correspond to specific biological processes and disease states.

    “Hierarchical clustering on the network structure allows us to uncover functional modules of genes whose expression profiles are tightly intertwined,3,4 which typically correspond to specific biological processes and disease states.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [3] Langfelder P. Zhang B. Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R Bioinformatics 24 2008 719 720 10.1093/bioinformatics/btm563 18024473 source
    • cites [4] Dong J. Horvath S. Understanding network concepts in modules BMC Syst. Biol. 1 2007 24 10.1186/1752-0509-1-24 17547772 PMC3238286 source

      The Background section describes defining modules as branches of a hierarchical-clustering dendrogram, and the Results section applies this method to gene co-expression networks. The Results also report that most modules are enriched for known gene ontologies, supporting a link to biological functions. The paper does not establish that these modules typically correspond to disease states. (LLM, conf=0.87)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingcitation

    ModuleEigengenes can optionally employ the harmony algorithm following SVD, which increases runtime but not memory usage.

    “Optionally, ModuleEigengenes can employ the harmony22 algorithm following SVD, which increases runtime but not memory usage.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [22] Korsunsky I. Millard N. Fan J. Slowikowski K. Zhang F. Wei K. Baglaenko Y. Brenner M. Loh P.-r. Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony Nat. Methods 16 2019 1289 1296 10.1038/s41592-019-0619-0 31740819 PMC6884693 source

      The Online Methods > Harmony section (page not provided) describes Harmony operating on a PCA embedding, supporting its use after dimensionality reduction. The Results > Harmony Scales to Large Data section reports Harmony’s runtime and memory usage, but does not assess its use in ModuleEigengenes or show that adding it increases runtime without increasing memory usage. (LLM, conf=0.85)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    The hdWGCNA R package extends the data structures and functionality of the Seurat package.

    “the hdWGCNA R package extends the data structures and functionality of the widely used Seurat package,5,6,7”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [5] Butler A. Hoffman P. Smibert P. Papalexi E. Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species Nat. Biotechnol. 36 2018 411 420 10.1038/nbt.4096 29608179 PMC6700744 source
    • cites [6] Stuart T. Butler A. Hoffman P. Hafemeister C. Papalexi E. Mauck W.M. Hao Y. Stoeckius M. Smibert P. Satija R. Comprehensive integration of single-cell data Cell 177 2019 1888 1902.e21 10.1016/j.cell.2019.05.031 31178118 PMC6687398 source
    • cites [7] Hao Y. Hao S. Andersen-Nissen E. Mauck W.M. Zheng S. Butler A. Lee M.J. Wilk A.J. Darby C. Integrated analysis of multimodal single-cell data Cell 184 2021 3573 3587.e29 10.1016/j.cell.2021.04.048 34062119 PMC8238499 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingcitation

    hdWGCNA was developed as an R package extending Seurat and has an extensive documentation website demonstrating its use on new datasets.

    “To maximize usability among the genomics community, the hdWGCNA R package extends the data structures and functionality of the widely used Seurat package,5,6,7 and we developed an extensive documentation website for hdWGCNA demonstrating its use on new datasets.”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [5] Butler A. Hoffman P. Smibert P. Papalexi E. Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species Nat. Biotechnol. 36 2018 411 420 10.1038/nbt.4096 29608179 PMC6700744 source
    • cites [6] Stuart T. Butler A. Hoffman P. Hafemeister C. Papalexi E. Mauck W.M. Hao Y. Stoeckius M. Smibert P. Satija R. Comprehensive integration of single-cell data Cell 177 2019 1888 1902.e21 10.1016/j.cell.2019.05.031 31178118 PMC6687398 source
    • cites [7] Hao Y. Hao S. Andersen-Nissen E. Mauck W.M. Zheng S. Butler A. Lee M.J. Wilk A.J. Darby C. Integrated analysis of multimodal single-cell data Cell 184 2021 3573 3587.e29 10.1016/j.cell.2021.04.048 34062119 PMC8238499 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Cd9 encodes a transmembrane protein and is a known glioblastoma biomarker.

    “Cd9 encodes a transmembrane protein and is a known glioblastoma biomarker,40”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [40] Podergajs N. Motaln H. Rajčević U. Verbovšek U. Koršič M. Obad N. Espedal H. Vittori M. Herold-Mende C. Miletic H. Transmembrane protein CD9 is glioblastoma biomarker, relevant for maintenance of glioblastoma stem cells Oncotarget 7 2016 593 609 10.18632/oncotarget.5477 26573230 PMC4808020 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Radial glia share transcriptomic similarities with mature astrocytes and are progenitor cells that give rise to numerous distinct cell fates, including neuronal cells, astrocytes, oligodendrocytes, and ependymal cells.

    “Radial glia, which share transcriptomic similarities with mature astrocytes, are progenitor cells that give rise to numerous distinct cell fates, including neuronal cells, astrocytes, oligodendrocytes, and ependymal cells.34,35”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [34] Pollen A.A. Nowakowski T.J. Chen J. Retallack H. Sandoval-Espinosa C. Nicholas C.R. Shuga J. Liu S.J. Oldham M.C. Diaz A. Molecular identity of human outer radial glia during cortical development Cell 163 2015 55 67 10.1016/j.cell.2015.09.004 26406371 PMC4583716 source

      The Results section, “Extensive Neurogenic Capacity of oRG Cells” (page unavailable), reports that outer radial glia share some genes with later astrocytes and that single cells produced neuronal and glial daughters. It does not establish broad transcriptomic similarity to mature astrocytes or identify astrocytes, oligodendrocytes, and ependymal cells as distinct daughter-cell fates. (LLM, conf=0.87)

      Experimental — accuracy not yet measured
    • cites [35] Eze U.C. Bhaduri A. Haeussler M. Nowakowski T.J. Kriegstein A.R. Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia Nat. Neurosci. 24 2021 584 594 10.1038/s41593-020-00794-1 33723434 PMC8012207 source

      The Results (RNA-Velocity Analysis of Lineage; page unavailable) describe radial glia giving rise to neurons, and the Discussion (page unavailable) describes progenitors giving rise to neuronal and glial diversity. Our copy does not establish transcriptomic similarity between radial glia and mature astrocytes or identify astrocytes, oligodendrocytes, and ependymal cells as the specific fates claimed. (LLM, conf=0.85)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Premisecitation

    ST enables the investigation of biological patterns that might otherwise be hidden in other -omics technologies, such as scRNA-seq or bulk RNA-seq.

    “ST enables the investigation of biological patterns that might otherwise be hidden in other -omics technologies, such as scRNA-seq or bulk RNA-seq.26,27”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [26] Moses L. Pachter L. Museum of Spatial Transcriptomics Preprint at bioRxiv 2021 10.1101/2021.05.11.443152 35273392 source
    • cites [27] Moffitt J.R. Lundberg E. Heyn H. The emerging landscape of spatial profiling technologies Nat. Rev. Genet. 23 2022 741 759 10.1038/s41576-022-00515-3 35859028 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    CHD2 de novo variants have been identified in individuals with ASD.

    “One of the INH-M13 hub genes is CHD2, whose de novo variants have been identified in individuals with ASD.42,43”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [42] Carvill G.L. Heavin S.B. Yendle S.C. McMahon J.M. O’Roak B.J. Cook J. Khan A. Dorschner M.O. Weaver M. Calvert S. Targeted resequencing in epileptic encephalopathies identifies de novo mutations in CHD2 and SYNGAP1 Nat. Genet. 45 2013 825 830 10.1038/ng.2646 23708187 PMC3704157 source
    • cites [43] Neale B.M. Kou Y. Liu L. Ma’ayan A. Samocha K.E. Sabo A. Lin C.-F. Stevens C. Wang L.-S. Makarov V. Patterns and rates of exonic de novo mutations in autism spectrum disorders Nature 485 2012 242 245 10.1038/nature11011 22495311 PMC3613847 source

      The Abstract (page unavailable) reports de novo variants in ASD cases and highlights CHD8, but it does not identify a CHD2 variant. Searches of our copy for “CHD2” and “chromodomain helicase DNA binding protein 2” found no passage establishing the gene-specific claim. (LLM, conf=0.73)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Microglia are implicated in the pathology and genetic risk of several central nervous system diseases, including Alzheimer's disease.

    “Microglia, the resident immune cells of the brain, are implicated in the pathology and genetic risk of several CNS diseases, including AD.46,47,48,49”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [46] Jansen I.E. Savage J.E. Watanabe K. Bryois J. Williams D.M. Steinberg S. Sealock J. Karlsson I.K. Hägg S. Athanasiu L. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk Nat. Genet. 51 2019 404 413 10.1038/s41588-018-0311-9 30617256 PMC6836675 source

      The Results section (no page provided) reports suggestive associations between microglial gene expression and Alzheimer’s disease risk, and the Discussion links microglia to a hypothesis about AD pathogenesis. The study does not establish the same implication for several other CNS diseases; its microglia associations also did not survive the stringent Bonferroni correction. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
    • cites [47] Kunkle B.W. Grenier-Boley B. Sims R. Bis J.C. Damotte V. Naj A.C. Boland A. Vronskaya M. van der Lee S.J. Amlie-Wolf A. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates A, tau, immunity and lipid processing Nat. Genet. 51 2019 414 430 10.1038/s41588-019-0358-2 30820047 PMC6463297 source

      The Results section (Candidate gene prioritization at genome-wide loci; page unavailable) links microglial gene expression to Alzheimer’s disease risk loci and describes microglia as involved in the pathological immune response in late-onset Alzheimer’s disease. The paper does not establish the same link for several CNS diseases, so the claim is supported for Alzheimer’s disease but not for the broader group. (LLM, conf=0.87)

      Experimental — accuracy not yet measured
    • cites [48] Schwartzentruber J. Cooper S. Liu J.Z. Barrio-Hernandez I. Bello E. Kumasaka N. Young A.M.H. Franklin R.J.M. Johnson T. Estrada K. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes Nat. Genet. 53 2021 392 402 10.1038/s41588-020-00776-w 33589840 PMC7610386 source

      The Results section (‘AD risk is enriched near genes with high microglial gene expression’; page not provided) reports enrichment of Alzheimer’s disease risk near genes highly expressed in microglia. The paper also discusses a possible role for microglial activation in AD, but the passages do not extend that finding to several CNS diseases. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Rare loss-of-function mutations have been reported in the SCN2A gene.

    “Similarly, rare loss-of-function mutations have been reported in the SCN2A gene, a hub gene of the INH-M11 module.44”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [44] Carroll L.S. Woolf R. Ibrahim Y. Williams H.J. Dwyer S. Walters J. Kirov G. O’Donovan M.C. Owen M.J. Mutation screening of SCN2A in schizophrenia and identification of a novel loss-of-function mutation Psychiatr. Genet. 26 2016 60 65 10.1097/ypg.0000000000000110 26555645 PMC4756433 source

  • Classified as probably not a substantial claim.

    Premisecitation

    Multiple cell states of microglia represent a spectrum between homeostatic and disease-associated microglia.

    “Transcriptomic and epigenomic studies in human tissue and AD mouse models have identified multiple cell states of microglia, representing a spectrum between homeostatic and disease-associated microglia (DAMs).12,50,51”
    Quote in the paperSubstantial claim3 of 3 sources checked
    • cites [12] Morabito S. Miyoshi E. Michael N. Shahin S. Martini A.C. Head E. Silva J. Leavy K. Perez-Rosendahl M. Swarup V. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease Nat. Genet. 53 2021 1143 1155 10.1038/s41588-021-00894-z 34239132 PMC8766217 source
    • cites [50] Keren-Shaul H. Spinrad A. Weiner A. Matcovitch-Natan O. Dvir-Szternfeld R. Ulland T.K. David E. Baruch K. Lara-Astaiso D. Toth B. A unique microglia type associated with restricting development of Alzheimer’s disease Cell 169 2017 1276 1290.e17 10.1016/j.cell.2017.05.018 28602351 source
    • cites [51] Sala Frigerio C. Wolfs L. Fattorelli N. Thrupp N. Voytyuk I. Schmidt I. Mancuso R. Chen W.-T. Woodbury M.E. Srivastava G. The major risk factors for Alzheimer’s disease: age, sex, and genes modulate the microglia response to A plaques Cell Rep. 27 2019 1293 1306.e6 10.1016/j.celrep.2019.03.099 31018141 PMC7340153 source

      The Discussion (page not provided) describes multiple microglial states on a continuous spectrum from homeostatic cells along two response branches. It says the ARMs response overlaps with DAMs, but cautions that calling these cells “disease-associated” is misleading because they also occur in healthy mice. The paper supports the spectrum claim, but not a simple homeostatic-to-DAM continuum. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    CD163 is known to be involved in the breakdown of the blood-brain barrier.

    “CD163, a hub gene of MG-M4, is known to be involved in the breakdown of the blood-brain barrier.53,54”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [53] Borda J.T. Alvarez X. Mohan M. Hasegawa A. Bernardino A. Jean S. Aye P. Lackner A.A. CD163, a marker of perivascular macrophages, is up-regulated by microglia in simian immunodeficiency virus encephalitis after haptoglobin-hemoglobin complex stimulation and is suggestive of breakdown of the blood-brain barrier Am. J. Pathol. 172 2008 725 737 10.2353/ajpath.2008.070848 18276779 PMC2258269 source

      The section “Co-Localization of CD163 Expression and Its Ligand Hp-Hb Complex” reports that blood-brain barrier breakdown was associated with microglial CD163 expression in encephalitic macaque brain. It proposes that barrier damage leads to CD163 up-regulation, not that CD163 causes or contributes to the breakdown; the claim overstates that relationship. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [54] Pey P. Pearce R.K.B. Kalaitzakis M.E. Griffin W.S.T. Gentleman S.M. Phenotypic profile of alternative activation marker CD163 is different in Alzheimer’s and Parkinson’s disease Acta Neuropathol. Commun. 2 2014 21 10.1186/2051-5960-2-21 24528486 PMC3940003 source

      The Discussion (page not supplied) reports that some CD163-positive microglia coincided with fibrinogen leakage, an indication of blood–brain barrier breakdown. It suggests that vascular compromise might allow CD163-positive macrophages to enter the brain, but does not establish that CD163 itself contributes to the breakdown or that such a role is known. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Premisecitation

    A previous study defined a set of transcription factors, genes, and cis-regulatory elements involved in the shift between homeostatic and DAM cell states in human AD, identifying shared and distinct signatures compared with the DAM signature from 5xFAD mice.

    “Our previous study defined a set of transcription factors, genes, and cis-regulatory elements involved in the shift between homeostatic and DAM cell states in human AD, identifying shared and distinct signatures compared with the DAM signature from 5xFAD mice.12”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [12] Morabito S. Miyoshi E. Michael N. Shahin S. Martini A.C. Head E. Silva J. Leavy K. Perez-Rosendahl M. Swarup V. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease Nat. Genet. 53 2021 1143 1155 10.1038/s41588-021-00894-z 34239132 PMC8766217 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Co-expression networks have been successful for analyzing bulk proteomics datasets in human disease samples.

    “Co-expression networks have been successful for analyzing bulk proteomics datasets in human disease samples,67,68”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [67] Swarup V. Chang T.S. Duong D.M. Dammer E.B. Dai J. Lah J.J. Johnson E.C.B. Seyfried N.T. Levey A.I. Geschwind D.H. Identification of conserved proteomic networks in neurodegenerative dementia Cell Rep. 31 2020 107807 10.1016/j.celrep.2020.107807 PMC8221021 32579933 source
    • cites [68] Johnson E.C.B. Dammer E.B. Duong D.M. Ping L. Zhou M. Yin L. Higginbotham L.A. Guajardo A. White B. Troncoso J.C. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation Nat. Med. 26 2020 769 780 10.1038/s41591-020-0815-6 32284590 PMC7405761 source

  • May combine two claims.

    Premisecitation

    The two bulk RNA-seq studies used AD samples and controls from the same patient cohorts but took unique approaches for co-expression network analysis.

    “These studies both used AD samples and controls from the same patient cohorts (Religious Orders Study and Memory and Aging Project, Mayo Clinic, Mount Sinai School of Medicine),64,65,66 but they took unique approaches for co-expression network analysis.”
    Quote in the paperNot a substantial claim3 of 3 sources checked
    • cites [64] Mostafavi S. Gaiteri C. Sullivan S.E. White C.C. Tasaki S. Xu J. Taga M. Klein H.-U. Patrick E. Komashko V. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease Nat. Neurosci. 21 2018 811 819 10.1038/s41593-018-0154-9 29802388 PMC6599633 source

      The Results section, “Data origin and phenotypes” (page unavailable), identifies this study’s participants as coming from the Religious Orders Study and Rush Memory and Aging Project; the RNA-Seq results describe analysis of 478 participants. The Introduction and “Constructing the nodes of our module-trait network” describe its co-expression network approach and how it differs from prior approaches. Our copy does not establish that both studies used the same cohorts, including Mayo Clinic and Mount Sinai, so that comparison is not supported by this source. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [65] Allen M. Carrasquillo M.M. Funk C. Heavner B.D. Zou F. Younkin C.S. Burgess J.D. Chai H.-S. Crook J. Eddy J.A. Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases Sci. Data 3 2016 160089 10.1038/sdata.2016.89 PMC5058336 27727239 source
    • cites [66] Wang M. Beckmann N.D. Roussos P. Wang E. Zhou X. Wang Q. Ming C. Neff R. Ma W. Fullard J.F. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease Sci. Data 5 2018 180185 10.1038/sdata.2018.185 PMC6132187 30204156 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In another study, the authors showed that scWGCNA was suitable for network analysis using scRNA-seq alone.

    “and in another study we showed that scWGCNA was suitable for network analysis using scRNA-seq alone.28”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [28] Kiani Shabestari S. Morabito S. Danhash E.P. McQuade A. Sanchez J.R. Miyoshi E. Chadarevian J.P. Claes C. Coburn M.A. Hasselmann J. Absence of microglia promotes diverse pathologies and early lethality in Alzheimer’s disease mice Cell Rep. 39 2022 110961 10.1016/j.celrep.2022.110961 PMC9285116 35705056 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The implementation of scWGCNA contained a single tutorial to cover the basics of network analysis using the WGCNA package with the metacell matrix.

    “Contrasting the hdWGCNA package, the implementation of scWGCNA was an R package containing a single function for metacell construction, and a single tutorial to cover the basics of network analysis using the WGCNA package1 with the metacell matrix.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [1] Langfelder P. Horvath S. WGCNA: an R package for weighted correlation network analysis BMC Bioinf. 9 2008 559 10.1186/1471-2105-9-559 PMC2631488 19114008 source

  • May combine two claims.

    Interpretationnone given

    hdWGCNA is a purely unsupervised approach and does not require prior knowledge or databases in the inference procedure.

    “Unlike other network analysis pipelines such as single-cell regulatory network inference and clustering (SCENIC)70 or CellChat,71 hdWGCNA is a purely unsupervised approach and does not require prior knowledge or databases in the inference procedure.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [70] Aibar S. González-Blas C.B. Moerman T. Huynh-Thu V.A. Imrichova H. Hulselmans G. Rambow F. Marine J.-C. Geurts P. Aerts J. SCENIC: single-cell regulatory network inference and clustering Nat. Methods 14 2017 1083 1086 10.1038/nmeth.4463 28991892 PMC5937676 source
    • cites [71] Jin S. Guerrero-Juarez C.F. Zhang L. Chang I. Ramos R. Kuan C.-H. Myung P. Plikus M.V. Nie Q. Inference and analysis of cell-cell communication using CellChat Nat. Commun. 12 2021 1088 10.1038/s41467-021-21246-9 33597522 PMC7889871 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The AMP-AD study from Wan et al. performed network analysis separately from each brain region.

    “The AMP-AD study from Wan et al.63 performed network analysis separately from each brain region, while, in our previous study,56 we performed consensus network analysis across the different brain regions.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [63] Wan Y.-W. Al-Ouran R. Mangleburg C.G. Perumal T.M. Lee T.V. Allison K. Swarup V. Funk C.C. Gaiteri C. Allen M. Wang M. Meta-analysis of the Alzheimer’s disease human brain transcriptome and functional dissection in mouse models Cell Rep. 32 2020 107908 10.1016/j.celrep.2020.107908 PMC7428328 32668255 source

  • Classified as probably not a substantial claim.

    Premisereasoning

    hdWGCNA greatly extends upon scWGCNA, the authors' previous method for co-expression network analysis in single-cell transcriptomics data.

    “hdWGCNA greatly extends upon scWGCNA,12 our previous method for co-expression network analysis in single-cell transcriptomics data.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [12] Morabito S. Miyoshi E. Michael N. Shahin S. Martini A.C. Head E. Silva J. Leavy K. Perez-Rosendahl M. Swarup V. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease Nat. Genet. 53 2021 1143 1155 10.1038/s41588-021-00894-z 34239132 PMC8766217 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The previous study performed consensus network analysis across the different brain regions.

    “The AMP-AD study from Wan et al.63 performed network analysis separately from each brain region, while, in our previous study,56 we performed consensus network analysis across the different brain regions.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [56] Morabito S. Miyoshi E. Michael N. Swarup V. Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer’s disease Hum. Mol. Genet. 29 2020 2899 2919 10.1093/hmg/ddaa182 32803238 PMC7566321 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    scWGCNA was originally used to identify co-expression networks using bulk and single-cell RNA-seq together.

    “scWGCNA was originally used to identify co-expression networks using bulk and single-cell RNA-seq together,12”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [12] Morabito S. Miyoshi E. Michael N. Shahin S. Martini A.C. Head E. Silva J. Leavy K. Perez-Rosendahl M. Swarup V. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease Nat. Genet. 53 2021 1143 1155 10.1038/s41588-021-00894-z 34239132 PMC8766217 source

  • Classified as probably not a substantial claim.

    Premisecitation

    Connected genes in biological networks are potentially involved in the same processes.

    “Connected genes in biological networks are potentially involved in the same processes, and EGAD evaluates this network property given a set of gene-process annotations.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [23] Ballouz S. Weber M. Pavlidis P. Gillis J. EGAD: ultra-fast functional analysis of gene networks Bioinformatics 33 2017 612 614 10.1093/bioinformatics/btw695 27993773 PMC6041978 source

  • Classified as probably not a substantial claim.

    Premisecitation

    EGAD evaluates the network property of functional coherence given a set of gene-process annotations.

    “Connected genes in biological networks are potentially involved in the same processes, and EGAD evaluates this network property given a set of gene-process annotations.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [23] Ballouz S. Weber M. Pavlidis P. Gillis J. EGAD: ultra-fast functional analysis of gene networks Bioinformatics 33 2017 612 614 10.1093/bioinformatics/btw695 27993773 PMC6041978 source

  • Classified as probably not a substantial claim.

    Premisecitation

    A gene set by Jansen et al. provided the set of genes associated with Alzheimer's disease.

    “among which a gene set by Jansen et al.46 provided the set of genes associated with AD.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Jansen et al.46 Jansen I.E. Savage J.E. Watanabe K. Bryois J. Williams D.M. Steinberg S. Sealock J. Karlsson I.K. Hägg S. Athanasiu L. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk Nat. Genet. 51 2019 404 413 10.1038/s41588-018-0311-9 30617256 PMC6836675 source

  • May combine two claims.

    Premisecitation

    SEACells and Metacell2 do not account for cell labels in their aggregation procedures, which may result in a number of metacells containing transcriptomes from differently labeled cells.

    “Notably, SEACells and Metacell2 do not account for cell labels in their aggregation procedures, which may result in a number of metacells containing transcriptomes from differently labeled cells.”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [14] Ben-Kiki O. Bercovich A. Lifshitz A. Tanay A. Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis Genome Biol. 23 2022 100 10.1186/s13059-022-02667-1 35440087 PMC9019975 source

      The Methods sections on graph construction and the divide-and-conquer algorithm describe forming Metacell-2 metacells from gene-expression correlations and graph partitions, without using cell labels. That supports the Metacell-2 portion and makes mixed-label metacells a plausible possibility, but the paper does not establish how many such metacells occur or address SEACells. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
    • cites [16] Persad S. Choo Z.-N. Dien C. Sohail N. Masilionis I. Chaligné R. Nawy T. Brown C.C. Sharma R. Pe’er I. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data Nat. Biotechnol. 2023 10.1038/s41587-023-01716-9 PMC10713451 36973557 source

  • Classified as probably not a substantial claim.

    Premisecitation

    Of the 121,451 cells in the Velmeshev et al. ASD snRNA-seq dataset, 20,249 were labeled as inhibitory neurons based on marker gene expression profiles.

    “Of the 121,451 cells in this dataset, 20,249 were labeled as inhibitory neurons based on marker gene expression profiles.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Velmeshev et al.9 Velmeshev D. Schirmer L. Jung D. Haeussler M. Perez Y. Mayer S. Bhaduri A. Goyal N. Rowitch D.H. Kriegstein A.R. Single-cell genomics identifies cell type–specific molecular changes in autism Science 364 2019 685 689 10.1126/science.aav8130 31097668 PMC7678724 source

      The paper’s clustering passage (section labeled Abstract; page unavailable) says it annotated cell types, including interneurons, using known cell-type markers. Its sequencing passage reports 104,559 ASD-and-control nuclei plus 21,984 additional epilepsy-and-control profiles; our copy does not establish the claimed total of 121,451 cells or the count of 20,249 inhibitory neurons. (LLM, conf=0.85)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given

    hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data.

    “Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given

    hdWGCNA is directly compatible with Seurat.

    “hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Findingnone given

    hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization.

    “hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingcitation

    hdWGCNA computes module eigengenes using specific accommodations for high-dimensional data, allowing for batch correction and regression of continuous covariates.

    “hdWGCNA computes MEs using specific accommodations for high-dimensional data, allowing for batch correction and regression of continuous covariates (STAR Methods, Algorithm 2).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    The runtime of the MetacellsByGroups function increased steadily with the number of cells, but its memory usage plateaus.

    “The runtime of the MetacellsByGroups function increased steadily with the number of cells, but the memory usage plateaus.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    hdWGCNA was used to analyze a single-cell RNA sequencing dataset consisting of 1 million cells, showcasing its scalability in large datasets.

    “we used hdWGCNA to analyze a single-cell RNA sequencing (scRNA-seq) dataset consisting of 1 million cells, showcasing the scalability of hdWGCNA in large datasets.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The inhibitory neuron network performed best for functional coherence with a median neighbor-voting AUC of 0.592, while the oligodendrocyte network was lowest with a median AUC of 0.549.

    “The inhibitory neuron network performed the best for functional coherence with a median neighbor-voting AUC of 0.592, while the lowest-performing network was from the oligodendrocytes with a median AUC of 0.549.”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: Co-expressed genes are involved in similar biological processes, so co-expression network structures should be predictive of biological pathway membership.

  • Classified as probably not a substantial claim.

    Findingreasoning

    hdWGCNA was designed to facilitate multi-scale analysis of cellular and spatial hierarchies.

    “we designed hdWGCNA to facilitate multi-scale analysis of cellular and spatial hierarchies.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    TestSoftPowers generally had a low memory footprint but was the slowest individual function based on these tests.

    “While TestSoftPowers generally had a low memory footprint, it was the slowest individual function based on these tests.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The efficiency of ConstructNetwork varies with the number of input cells and features, slowing as more cells and features are included.

    “The efficiency of ConstructNetwork varies both with the number of input cells and features, where this calculation will slow down as more cells and features are included.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning

    ModuleConnectivity computes eigengene-based connectivity as product-moment correlation coefficients between the sparse gene expression matrix and the MEs matrix, resulting in fast calculations with low memory usage.

    “Finally, ModuleConnectivity computes eigengene-based connectivity as product-moment correlation coefficients between the sparse gene expression matrix and the MEs matrix, which resulted in fast calculations with low memory usage.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    hdWGCNA projected gene modules from two bulk RNA-seq studies of AD patients into an snRNA-seq dataset of the AD brain, showing that the approach allows interrogation of previously identified gene modules and networks.

    “we used hdWGCNA to project gene modules from two bulk RNA-seq studies of AD patients into an snRNA-seq dataset of the AD brain, showing that our approach allows for interrogation of gene modules and networks that have been previously identified.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    hdWGCNA scales to large datasets, demonstrated by iterative network analysis on a peripheral blood mononuclear cell scRNA-seq dataset of nearly 1 million cells.

    “we performed a similar iterative network analysis on a peripheral blood mononuclear cell (PBMC) scRNA-seq dataset of nearly 1M cells, highlighting the scalability of hdWGCNA in large datasets (Figure S5; Data S1).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given

    The efficiency of ModuleEigengenes varies with the number of co-expression modules detected and with the number of features in each module.

    “Further, the efficiency of this function varies with the number of co-expression modules detected and with the number of features in each modules.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Pairwise gene set overlap analysis of the 96 co-expression modules found some significant overlaps across cell types, but the gene sets were overall quite distinct, with a maximum Jaccard index of 0.297 and a median of 0.005.

    “we performed a pairwise gene set overlap analysis of the 96 co-expression modules, and, while we did find that some modules had significant overlaps across the different cell types, the gene sets comprising these modules were overall quite distinct, with a maximum Jaccard index between two modules of 0.297 and a median of 0.005”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Module hub gene expression was generally predictive of module member gene expression across all modules in the six cell-type co-expression networks, with the best average test set RMSE of 0.0159 and the worst of 0.209.

    “Overall, we found that module hub gene expression was generally predictive of module member gene expression across all modules in the six cell-type co-expression networks, where the module with the best performance had an average test set RMSE of 0.0159 and the module with the worst performance had an average test set RMSE of 0.209 (Figure 2E).”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Genes within the same co-expression modules derived from specific cell types should be functionally related or co-regulated.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given

    hdWGCNA provides a rich suite of functions for data analysis and visualization, providing biological context for co-expression networks by leveraging a variety of biological knowledge databases.

    “hdWGCNA provides a rich suite of functions for data analysis and visualization, providing biological context for co-expression networks by leveraging a variety of biological knowledge databases.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given

    hdWGCNA is capable of processing any number of ST samples in the same co-expression network analysis by constructing metaspots separately for each sample.

    “hdWGCNA is capable of processing any number of ST samples in the same co-expression network analysis by constructing metaspots separately for each sample.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The hub genes of SM4 include hemoglobin subunits Hba-a1, Hba-a2, and Hbb-bt, and SM4 was enriched for biological processes associated with brain vasculature.

    “Further, the hub genes of SM4 include hemoglobin subunits (Hba-a1, Hba-a2, Hbb-bt), and we show that SM4 was enriched for biological processes associated with brain vasculature (Figures 3B and S7C).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Modules RGL-M1 and RGL-M11 were associated with ependymal and neuronal cell fates respectively.

    “Different isoforms of the histone H3.3 subunit gene H3f3b were hubs for modules RGL-M1 and RGL-M11, which were associated with ependymal and neuronal cell fates respectively, suggesting that alternative TES usage in H3f3b plays a role in regulating epigenetic factors in murine hippocampal development (Figure 4F).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The module eigengene algorithm in hdWGCNA accounts for batch effects and additional covariates in the input dataset.

    “the module eigengene algorithm in hdWGCNA (Algorithm 2) accounts for batch effects and additional covariates in the input dataset”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Expression of these modules was significantly correlated with AD genetic risk, with the strongest correlation in alternative activation module MG-M4.

    “We show that expression of these modules was significantly correlated with AD genetic risk (Pearson correlation p <0.05), with the strongest correlation in alternative activation module MG-M4 (Figure 6I).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Findingnone given

    hdWGCNA is completely technology agnostic and can be adapted to handle high-dimensional counts matrices from any single-cell or spatial transcriptomics platform.

    “hdWGNCA is completely technology agnostic, and can be adapted to handle high dimensional counts matrices from any single-cell or spatial transcriptomics platform.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    hdWGCNA contains functions to handle spatial transcriptomics datasets.

    “and hdWGCNA contains functions to handle spatial transcriptomics datasets.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingnone given

    hdWGCNA is compatible with single-cell and spatial transcriptomics datasets and can be easily adapted for novel transcriptomics approaches such as ScISOrSeq.

    “In this study, we demonstrated that hdWGCNA is compatible with single-cell and ST datasets and can be easily adapted for novel transcriptomics approaches such as ScISOrSeq.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    Unlike scWGCNA, the metacell construction algorithm in hdWGCNA includes new parameters to avoid redundant metacells.

    “Unlike scWGCNA, the metacell construction algorithm (Algorithm 1) in hdWGCNA includes new parameters to avoid redundant metacells”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Findingnone given

    The individually processed datasets were merged into a single Seurat object comprising 189,127 nuclei.

    “The individually processed datasets were merged into a single Seurat object comprising 189,127 nuclei”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The major cell compartments recovered in the 1M PBMC analysis were similar to those reported by Parse Biosciences, including T-cells, B-cells, monocytes, dendritic cells, basophils, and plasmablasts.

    “The major cell compartments recovered in this analysis were similar to those reported by Parse Biosciences in their analysis, including as T-cells, B-cells, monocytes, dendritic cells, basophils, and plasmablasts.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Findingnone given

    Genes expressed in at least 5% of microglia from any of the three studies were retained (7,900 genes).

    “genes expressed in at least 5% of microglia from any of the three studies were retained (7,900 genes).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The metacell aggregation step had a runtime of 85 minutes and 59 seconds.

    “The metacell aggregation step had a runtime of 85 min and 59 s.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Findingown data

    All nuclei labeled microglia were retained for network analysis based on expression of canonical marker genes such as CSF1R (9,904 nuclei).

    “We retained all nuclei labeled microglia for network analysis based on expression of canonical marker genes such as CSF1R (9,904 nuclei)”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    hdWGCNA includes functions for projecting co-expression networks into a variety of external datasets.

    “As shown throughout this manuscript, hdWGCNA includes functions for projecting co-expression networks into a variety of external datasets.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given

    While ST spots were clustered based on transcriptomic information alone, the authors were able to annotate them based on anatomical features.

    “While ST spots were clustered based on transcriptomic information alone, we were able to annotate them based on anatomical features.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    For the largest cell population (T-cells, 555,417 cells), the runtime for the network construction step was 186 seconds.

    “We note that for the largest cell population (T-cells, 555,417 cells), the runtime for the network construction step was 186 s.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given

    The number of input genes and other network analysis parameters also have an effect on runtime and memory usage.

    “Importantly, the number of input genes and other network analysis parameters also have an effect on runtime and memory usage.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The MetacellsByGroups algorithm terminates early if the target number of metacells is reached, explaining the plateau in memory usage.

    “This function attempts to construct a target number of metacells within each biological replicate and each cell population, and the algorithm terminates early if this target is reached, thus explaining the plateau in the memory usage graph.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationnone given

    hdWGCNA is a comprehensive framework for constructing and analyzing co-expression networks in high-dimensional transcriptomic data.

    “Here we describe hdWGCNA, a comprehensive framework for constructing and analyzing co-expression networks in high-dimensional transcriptomic data (Figure 1A).”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    A typical hdWGCNA workflow in scRNA-seq data accounts for sparsity and noise by collapsing highly similar cells into metacells to reduce sparsity while retaining cellular heterogeneity and by allowing for a modular design to perform separate network analyses in specified cell populations.

    “A typical hdWGCNA workflow in scRNA-seq data accounts for these considerations by collapsing highly similar cells into “metacells” to reduce sparsity while retaining cellular heterogeneity and by allowing for a modular design to perform separate network analyses in specified cell populations.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Sparsity and noise inherent in single-cell data can lead to spurious gene-gene correlations, thereby complicating co-expression network analysis.
    • Rests on an assumption: The correlation structure of single-cell or spatial transcriptomic data varies greatly for different subsets (cell types, cell states, anatomical regions).

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    The hdWGCNA metacell algorithm requires a dimensional reduction of the input expression matrix, and technical artifacts in these reductions can influence the effectiveness of metacell construction.

    “For example, the hdWGCNA metacell algorithm requires a dimensional reduction of the input expression matrix, but these reductions often contain technical artifacts. The choice of dimensionality reduction method and handling of technical artifacts would then influence the effectiveness of metacell construction.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Gfap encodes a key intermediate filament protein in astrocytes that is involved in astrocytic reactivity during central nervous system injuries or neurodegeneration.

    “Gfap encodes a key intermediate filament protein in astrocytes that is involved in astrocytic reactivity during central nervous system (CNS) injuries or neurodegeneration,39”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [39] Hol E.M. Pekny M. Glial fibrillary acidic protein (GFAP) and the astrocyte intermediate filament system in diseases of the central nervous system Curr. Opin. Cell Biol. 32 2015 121 130 10.1016/j.ceb.2015.02.004 25726916 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    The optimal number of cells to merge together to form a single metacell may differ across cell types and tissues.

    “Further, the optimal number of cells to merge together to form a single metacell may differ across cell types and tissues, attempting to balance between increasing information content of the aggregated group while avoiding merging of dissimilar cells.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    These modules are functional and provide new insights into the role of inhibitory neurons in ASD.

    “We also find enrichment of several ASD-associated genes such as TSC1 (INH-M8), SMARCA4 (INH-M8), SHANK2 (INH-M4), and CPEB4 (INH-M1), highlighting that these modules are functional and provide new insights into the role of inhibitory neurons in ASD.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The hdWGCNA R package enables researchers to rapidly incorporate network analysis into their own workflows, going beyond cell clustering and differential gene expression analysis toward systems-level insights.

    “The hdWGCNA R package directly extends the familiar Seurat pipeline and the SeuratObject data structure, enabling researchers to rapidly incorporate network analysis into their own workflows, going beyond cell clustering and differential gene expression analysis toward systems-level insights.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    The metacell bagging algorithm is implemented as part of the hdWGCNA R package in the ConstructMetacells function.

    “This metacell bagging algorithm is implemented as part of the hdWGCNA R package in the ConstructMetacells function, and the pseudocode for this algorithm is defined in Algorithm 1.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    hdWGCNA is highly modular, allowing for multi-scale analyses of different cellular or spatial hierarchies in a technology-agnostic manner.

    “We designed hdWGCNA to be highly modular, allowing for multi-scale analyses of different cellular or spatial hierarchies in a technology-agnostic manner.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    The implementation of scWGCNA was an R package containing a single function for metacell construction.

    “Contrasting the hdWGCNA package, the implementation of scWGCNA was an R package containing a single function for metacell construction, and a single tutorial to cover the basics of network analysis using the WGCNA package1 with the metacell matrix.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    In the signed topological overlap matrix (TOM), negative correlations negatively reinforce the network connection, unlike in the unsigned TOM.

    “In the signed TOM, negative correlations serve to negatively reinforce the network connection, which is not the case in the unsigned TOM.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    The hdWGCNA R package directly extends the familiar Seurat pipeline and the SeuratObject data structure.

    “The hdWGCNA R package directly extends the familiar Seurat pipeline and the SeuratObject data structure, enabling researchers to rapidly incorporate network analysis into their own workflows, going beyond cell clustering and differential gene expression analysis toward systems-level insights.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationnone given

    Projecting co-expression modules derived from bulk RNA-seq patient cohorts into single-cell datasets can layer disease-related information onto the single-cell dataset and attribute cell-state-specific expression patterns to the bulk RNA-seq data.

    “By projecting co-expression modules derived from bulk RNA-seq patient cohorts into single-cell datasets, we can layer disease-related information onto the single-cell dataset and attribute cell-state-specific expression patterns to the bulk RNA-seq data.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: It remains cost-prohibitive for most researchers to perform high-dimensional -omics studies of large patient cohorts.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    hdWGCNA provides a succinct methodology for investigating systems-level changes in the transcriptome in single-cell or spatial transcriptomics datasets.

    “We developed hdWGCNA to provide a succinct methodology for investigating systems-level changes in the transcriptome in single-cell or ST datasets.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    hdWGCNA includes built-in functions to leverage external biological knowledge sources to provide insight for co-expression networks.

    “hdWGCNA includes built-in functions to leverage external biological knowledge sources to provide insight for co-expression networks, for example by comparing gene modules with functional gene sets such as disease-associated genes from GWAS expression quantitative trait loci (eQTLs), or transcription factor target genes.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    The co-expression information computed by hdWGCNA can be easily retrieved from the Seurat object to facilitate custom downstream analyses beyond the hdWGCNA package.

    “The co-expression information computed by hdWGCNA can be easily retrieved from the Seurat object to facilitate custom downstream analyses beyond the hdWGCNA package.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Harmony was applied to correct module eigengenes based on the dataset of origin in the consensus microglia analysis.

    “we applied Harmony22 to correct MEs based on the dataset of origin.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    Module eigengenes were computed for each bulk RNA-seq module in the snRNA-seq dataset using ProjectModules, with Harmony used to correct MEs based on sequencing batch.

    “Module eigengenes were computed for each of these bulk RNA-seq modules in the snRNA-seq dataset using the hdWGCNA function ProjectModules, using Harmony to correct MEs based on sequencing batch.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The set of modules from Morabito et al. 2020 was based on a consensus network analysis across six brain regions.

    “the set of modules from Morabito et al. 202056 was based on a consensus network analysis across six brain regions”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Morabito et al. 202056

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    A separate parameter sweep was performed for the three expression matrices using TestSoftPowerConsensus, ensuring an appropriate β value for each dataset (Mathys et al.: β=6, Zhou et al.: β=8, Morabito & Miyoshi et al.: β=6).

    “We performed a separate parameter sweep for the three expression matrices using the hdWGCNA function TestSoftPowerConsensus, ensuring that we used an appropriate β value for each dataset (Mathys et al.: β=6, Zhou et al.: β=8, Morabito & Miyoshi et al.: β=6).”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The datasets were integrated into a common dimensionally-reduced space using PCA and Harmony.

    “the datasets were integrated into a common dimensionally-reduced space using PCA and Harmony.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Harmony was applied to correct module eigengenes based on sequencing batch in the inhibitory neuron analysis.

    “we applied Harmony22 to correct MEs based on sequencing batch.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The other set of modules from the AMP-AD study were constructed separately for seven different brain regions.

    “the other set of modules from the AMP-AD study63 were constructed separately for seven different brain regions.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites AMP-AD study63

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    A soft-power threshold β=9 was selected based on a parameter sweep performed with the TestSoftPowers function.

    “We selected a soft-power threshold β=9 based on the parameter sweep performed with the TestSoftPowers function.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The start of pseudotime was oriented based on the expression of homeostatic microglia marker genes, such as P2RY12, CX3CR1, and CSF1R.

    “We oriented the start of pseudotime based on the expression of homeostatic microglia marker genes, such as P2RY12, CX3CR1, and CSF1R.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    Individual TOMs were scaled based on the 80th percentile to alleviate different statistical properties specific to each dataset rather than the underlying biology.

    “Individual TOMs were computed for each dataset, and they were scaled based on the 80th percentile in order to alleviate different statistical properties specific to each dataset rather than the underlying biology.”
    Quote in the paperSubstantial claim
90 of 90

  • The paper relies on this without support.

    Popular bioinformatic tools do not support systems-level analysis.

    “While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis.”

  • The paper relies on this without support.

    Sparsity and noise inherent in single-cell data can lead to spurious gene-gene correlations, thereby complicating co-expression network analysis.

    “The sparsity and noise inherent in single-cell data can lead to spurious gene-gene correlations, thereby complicating co-expression network analysis.”

  • The paper relies on this without support.

    The correlation structure of single-cell or spatial transcriptomic data varies greatly for different subsets (cell types, cell states, anatomical regions).

    “Additionally, the correlation structure of single-cell or spatial transcriptomic data varies greatly for different subsets (cell types, cell states, anatomical regions).”

  • The paper relies on this without support.

    Data sparsity is inherent to the current generation of sequencing-based spatial transcriptomics technologies.

    “Data sparsity is also inherent to the current generation of these technologies, therefore we propose a metaspot aggregation approach prior to network analysis (Figure S6).”

  • The authors state this as an assumption.

    Co-expressed genes are involved in similar biological processes, so co-expression network structures should be predictive of biological pathway membership.

    “In principle, we expect that co-expressed genes are involved in similar biological processes, and therefore co-expression network structures should be predictive of biological pathway membership.”

  • The authors state this as an assumption.

    Genes within the same co-expression modules derived from specific cell types should be functionally related or co-regulated.

    “In principle, genes within the same co-expression modules derived from specific cell types should be functionally related or co-regulated.”

  • The paper relies on this without support.

    Co-expression networks behave as functional biological units.

    “Co-expression networks behave as functional biological units; therefore, we reason that the hub genes and other members of MG-M1 represent candidates for an expanded set of human DAM genes including ACTB, TPT1, and EEF1A1.”

  • The paper relies on this without support.

    It remains cost-prohibitive for most researchers to perform high-dimensional -omics studies of large patient cohorts.

    “To date, it remains cost-prohibitive for most researchers to perform high-dimensional -omics studies of large patient cohorts, but there are numerous large-scale disease-relevant bulk RNA-seq datasets containing thousands of samples from consortia such as the Encyclopedia of DNA Elements (ENCODE),60 the Genotype-Tissue Expression (GTEx) project,61 and The Cancer Genome Atlas (TCGA).62”

  • The paper relies on this without support.

    Classical bioinformatic approaches such as differential gene expression do not provide information about the broader context of genes in specific pathways or regulatory regimes.

    “Classical bioinformatic approaches for transcriptomics analysis such as differential gene expression are useful for finding individual genes that are altered in a particular disease or condition of interest, but they do not provide information about the broader context of these genes in specific pathways or regulatory regimes.”

  • The paper relies on this without support.

    Biological processes such as development or regeneration require coordination of distinct sets of genes in certain cell types with spatial specificity.

    “For example, biological processes such as development or regeneration require coordination of distinct sets of genes in certain cell types with spatial specificity.”

  • The paper relies on this without support.

    Single-cell gene expression datasets typically contain many more zero-valued entries than non-zero-valued entries, meaning that these datasets are sparse.

    “Single-cell gene expression datasets typically contain many more zero valued entries than non-zero valued entries, meaning that these datasets are sparse.”

  • The paper relies on this without support.

    Conventional single-cell gene expression assays yield sparse gene expression matrices.

    “Conventional single-cell gene expression assays yield sparse gene expression matrices.”

  • The paper relies on this without support.

    Correlations of sparse vectors may lead to downstream conclusions that are not robust or reproducible.

    “In general, correlations of sparse vectors may lead to downstream conclusions that are not robust or reproducible.”

  • The paper relies on this without support.

    Transcriptomic measurements of single cells are generally noisy, imposing challenges and limitations in the analysis of these datasets.

    “Transcriptomic measurements of single cells are generally noisy, imposing challenges and limitations in the analysis of these datasets.”

  • The paper relies on this without support.

    Technical noise may arise from dropout events or from various steps in the experimental protocols, potentially making downstream data analysis and interpretation more difficult.

    “Technical noise may arise from dropout events or from various steps in the experimental protocols, potentially making downstream data analysis and interpretation more difficult.”

  • The paper relies on this without support.

    Sequencing-based spatial transcriptomics approaches such as 10x Genomics Visium yield sparse transcriptomic profiles, introducing the same potential pitfalls as single-cell data for co-expression network analysis.

    “Sequencing-based ST approaches such as the 10× Genomics Visium platform also yield sparse transcriptomic profiles, thus introducing the same potential pitfalls as single-cell data for co-expression network analysis.”

  • The paper relies on this without support.

    The widespread adoption of single-cell genomics has led to many biologists running their own computational analysis.

    “The widespread adoption of single-cell genomics has led to many biologists running their own computational analysis, and we designed the hdWGCNA R package with these individuals in mind through our various step-by-step tutorials and detailed documentation.”
17 of 17

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • hdWGCNA identified synaptic, immune, and lipid-metabolism modules that demonstrated the most pronounced alterations in the combined 5XFAD;PS19 model.

    “Using high‐dimensional weighted gene co‐expression network analysis (hdWGCNA), 23 we identified synaptic, immune, and lipid‐metabolism modules that demonstrated the most pronounced alterations in the combined 5XFAD;PS19 model.”

    From Unique transcriptomic alterations in 5XFAD;PS19 mouse model identify glial lipid dysregulation and coordinated microglial–oligodendrocyte responses (2026) · cites this paper as [23]

  • For cells collected at 6 hrs and longer after replacing the medium, transcriptional states are distinct from control cells, with affected genes enriched in hippo signaling and cAMP-related pathways.

    “For cells collected at 6 hrs and longer after replacing the medium, their transcriptional states are distinct from control cells: the affected genes are enriched in hippo signaling and cAMP-related pathways46 (Fig. 1d, and Extended Data Fig. 2d–f).”

    From Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability (2025) · cites this paper as [46]

    The Results section on microglia in Alzheimer’s disease reports differences between disease-associated and control brain samples, not cells collected after medium replacement. Searches for a 6-hour collection time, medium replacement, Hippo signaling, and cAMP found no passage in our copy addressing the claimed experiment or pathway enrichment. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Figure 5D is cited for differential eigengene results but depicts eigengene maps

    Minor

    The text cites Figure 5D for ASD-versus-control differential module eigengene results, while the caption describes panel D as UMAP plots colored by module eigengenes. The caption places the ASD-versus-control comparisons in panel E.

  • Figure 6H is cited for module preservation instead of Figure 6J

    Minor

    The preservation-analysis paragraph cites Figure 6H, but the caption describes panel H as an AD disease-relevance-score analysis. Preservation categories are described for panel J.

  • Figure 5E is cited for ASD-gene enrichment but shows eigengene comparisons

    Minor

    The text cites Figure 5E for enrichment of ASD-associated genes in modules. The caption describes panel E as eigengene comparisons and panel C as the ASD-gene overlap analysis.

  • Metacell algorithm uses a union where it describes overlap

    Minor

    Algorithm 1 labels the cardinality of a union of neighbor sets as the number of overlapping neighbors. A union counts neighbors in either set; their overlap is counted by an intersection, contrary to the stated purpose of the parameter.

  • Figure 5C is cited for module eigengenes but describes gene overlap

    Minor

    The text cites Figure 5C for the distribution of module eigengenes across inhibitory-neuron groups. The caption identifies panel C as a gene-overlap analysis; it identifies panel D as the eigengene plot.

5 of 5

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Fleming, 2019, CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets, bioRxiv

Cited by

Sorted by citations

Related

Authors
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V.
Affiliations
University of California, Irvine (US)
Corresponding author
Swarup, Vivek
ORCID
Venue
Cell Reports Methods
Publisher
Elsevier BV
Published
2023-06
Volume
3
Issue
6
Pages
100498
ISSN
2667-2375 (ISSN-L)
DOI
10.1016/j.crmeth.2023.100498
PMC
PMC10326379
OpenAlex
W4380370818
Cited by
773
References
92
Citations by year
2026: 267 · 2025: 285 · 2024: 180 · 2023: 40 · 2022: 1
FWCI
58.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell Reports Methods (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • Uganda Cancer Institute
  • NIDA (1U01DA053826)
  • National Institute on Aging (1RF1AG071683, 3U19AG068054-02S, U54 AG054349-06)
  • NINDS (P01NS084974- 06A1)
  • Dr. Miriam and Sheldon G. Adelson Medical Research Foundation
  • University of California, Irvine
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
co-expression network, single-cell RNA sequencing, spatial transcriptomics, autism spectrum disorder, Alzheimer's disease, brain transcriptomics
MeSH terms
Autism Spectrum Disorder, Alzheimer Disease, Humans, Gene Expression Profiling, Gene Regulatory Networks, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-13.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-13.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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