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At least 19 recordsLinked to original sources
PubMed2024

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes.

BACKGROUND: The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. RESULTS: We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. CONCLUSION: We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Humans↗
PubMed2024

Comparison of the antibiotic resistance mechanisms in a gram-positive and a gram-negative bacterium by gene networks analysis.

Nowadays, the emergence of some microbial species resistant to antibiotics, both gram-positive and gram-negative bacteria, is due to changes in molecular activities, biological processes and their cellular structure in order to survive. The aim of the gene network analysis for the drug-resistant Enterococcus faecium as gram-positive and Salmonella Typhimurium as gram-negative bacteria was to gain insights into the important interactions between hub genes involved in key molecular pathways associated with cellular adaptations and the comparison of survival mechanisms of these two bacteria exposed to ciprofloxacin. To identify the gene clusters and hub genes, the gene networks in drug-resistant E. faecium and S. Typhimurium were analyzed using Cytoscape. Subsequently, the putative regulatory elements were found by examining the promoter regions of the hub genes and their gene ontology (GO) was determined. In addition, the interaction between milRNAs and up-regulated genes was predicted. RcsC and D920_01853 have been identified as the most important of the hub genes in S. Typhimurium and E. faecium, respectively. The enrichment analysis of hub genes revealed the importance of efflux pumps, and different enzymatic and binding activities in both bacteria. However, E. faecium specifically increases phospholipid biosynthesis and isopentenyl diphosphate biosynthesis, whereas S. Typhimurium focuses on phosphorelay signal transduction, transcriptional regulation, and protein autophosphorylation. The similarities in the GO findings of the promoters suggest common pathways for survival and basic physiological functions of both bacteria, including peptidoglycan production, glucose transport and cellular homeostasis. The genes with the most interactions with milRNAs include dpiB, rcsC and kdpD in S. Typhimurium and EFAU004_01228, EFAU004_02016 and EFAU004_00870 in E. faecium, respectively. The results showed that gram-positive and gram-negative bacteria have different mechanisms to survive under antibiotic stress. By deciphering their intricate adaptations, we can develop more effective therapeutic approaches and combat the challenges posed by multidrug-resistant bacteria.

Anti-Bacterial Agents↗
PubMed2026

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks↗
PubMed2026

Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.

Humans↗
PubMed2025

Prenatal pyrethroid exposure, placental gene network modules, and neonatal neurobehavior.

Prenatal pesticide exposure may adversely affect child neurodevelopment which may partly arise from impairing the placenta's vital role in fetal development. In a cohort of pregnant farmworkers from Thailand (N = 248), we examined the links between urinary metabolites of pyrethroid pesticides during pregnancy, placental gene expression networks derived from transcriptome sequencing, and newborn neurobehavior assessed using the NICU Network Neurobehavioral Scales (NNNS) at 5 weeks of age. Focusing on the 21 gene network modules in the placenta identified by Weighted Gene Co-expression Network Analysis, our analysis revealed significant associations between metabolites and nine distinct modules, and between thirteen modules and NNNS, with eight modules showing overlap. Notably, stress was negatively associated with the interferon alpha response and Myc target modules, and the interferon alpha response module was correlated positively with attention, and negatively with arousal, and quality of movement. The analysis also highlighted the early and late trimesters as critical periods for the exposures influence on placental function, with pyrethroid metabolites measured early in pregnancy significantly negatively associated with the protein secretion module, and those measured later in pregnancy negatively associated with modules related to oxidative phosphorylation (OXPHOS) and DNA repair. Additionally, the cumulative sum of 3-phenoxybenzoic acid across pregnancy was significantly negatively associated with the OXPHOS module. These findings suggest that prenatal exposure to pyrethroids may influence neonatal neurobehavior through specific placental mechanisms that impact gene expression of metabolic pathways, and these effects may be pregnancy period specific. These results offer valuable insights for future risk assessment and intervention strategies.

Prenatal Exposure Delayed Effects↗
PubMed2025

Analysis of Duplication and Potential Functional Divergence of Wing Gene Network Components in Pea Aphids.

A fundamental focus of evolutionary developmental biology is uncovering the genetic mechanisms responsible for the gain and loss of characters. One approach to this question is to investigate changes in the coordinated expression of a group of genes important for the development of a character of interest (a gene regulatory network). Here we consider the possibility that modifications to the wing gene regulatory network (wGRN), as defined by work primarily done in Drosophila melanogaster, were involved in the evolution of wing dimorphisms of the pea aphid (Acyrthosiphon pisum). We hypothesize that this may have occurred via changes in expression levels or by duplication followed by divergence of wGRN components. To test this, we annotated members of the wGRN in the pea aphid genome and assessed their expression levels in first and third nymphal instars of winged and wingless morphs of males and asexual females. We find that only 2 of the 32 assessed genes exhibit morph-biased expression. We also find that three wing genes (apterous (ap), warts (wts), and decapentaplegic (dpp)) have undergone gene duplication. In each case, the resulting paralogs show signs consistent with functional divergence, exhibiting either sex-, morph-, or stage-specific expression. Two gene duplicates, wts2 and dpp3, are of particular interest with respect to wing dimorphism, as they exhibit male morph-specific isoforms and wingless male-biased expression, respectively. These gene expression results provide an important first step toward identifying members of the pea aphid wGRN that may play a causative role in differentiating winged from wingless morphs. These findings supplement our understanding of trends in developmental gene network evolution, such as side-stepping pleiotropic constraint via duplication and sub-functionalization, underlying the emergence of novel phenotypes.

Animals↗
PubMed2025

Analysis of duplication and possible sub-functionalization of wing gene network components in pea aphids.

A fundamental focus of evolutionary-developmental biology is uncovering the genetic mechanisms responsible for the gain and loss of characters. One approach to this question is to investigate changes in the coordinated expression of a group of genes important for the development of a character of interest (a gene regulatory network). Here we consider the possibility that modifications to the wing gene regulatory network (wGRN), as defined by work primarily done in Drosophila melanogaster, were involved in the evolution of wing dimorphisms of the pea aphid (Acyrthosiphon pisum). We hypothesize that this may have occurred via changes in expression levels or duplication followed by sub-functionalization of wGRN components. To test this, we annotated members of the wGRN in the pea aphid genome and assessed their expression levels in first and third nymphal instars of winged and wingless morphs of males and asexual females. We find that only two of the 32 assessed genes exhibit morph-biased expression. We also find that three wing genes (apterous (ap), warts (wts), and decapentaplegic (dpp)) have undergone gene duplication. In each case, the resulting paralogs show signs of functional divergence, exhibiting either sex-, morph-, or stage-specific expression. Two gene duplicates, wts2 and dpp3, are of particular interest with respect to wing dimorphism, as they exhibit a wingless male-specific isoform and wingless male-biased expression, respectively. These results supplement our understanding of trends in developmental gene network evolution, such as side-stepping pleiotropic constraint via duplication and sub-functionalization, underlying the emergence of novel phenotypes.

dimorphism↗
PubMed2026

Epigenetic Gene Networks Governing Immune State Transitions Across the Lifespan.

Immune function across development, tissue repair, aging, and disease depends not only on signaling pathways but also on epigenetic architectures that determine whether coordinated transcriptional programs can be accessed and resolved. Increasing evidence indicates that epigenetic gene networks regulate the accessibility and reversibility of semi-stable immune states, shaping plastic, homeostatic, reparative, and degenerative configurations. We propose the concept of epigenetic transition windows, defined as temporally and contextually restricted intervals during which epigenetic constraints are relaxed, permitting coordinated and reversible transitions between immune states. During development, these windows are broad and support immune tolerance and adaptive plasticity. In adulthood they become spatially and temporally restricted, preserving stability while enabling conditional adaptation. With aging, they progressively narrow, contributing to chronic inflammation, impaired repair, and increased vulnerability to neurodegeneration. Conversely, pathological persistence of regulatory permissiveness may underlie immune evasion and sustained plasticity in cancer. We outline operational genomic readouts for quantifying transition windows, including chromatin accessibility variance, enhancer switching dynamics, reversibility metrics, and cross-cell coordination indices, and derive experimentally testable predictions that distinguish this model from pathway-centric or damage-centric explanations. By reframing immune dysfunction as a failure of regulated state transition rather than excessive signaling alone, this framework integrates inflammaging, trained immunity, immune resolution failure, and tumor immune escape within a unified regulatory architecture and provides a systems-level perspective on immune adaptability across the lifespan.

Epigenesis, Genetic↗
PubMed2025

AWGE-ESPCA: An edge sparse PCA model based on adaptive noise elimination regularization and weighted gene network for Hermetia illucens genomic data analysis.

Hermetia illucens is an important insect resource. Studies have shown that exploring the effects of Cu2+-stressed on the growth and development of the Hermetia illucens genome holds significant scientific importance. There are three major challenges in the current studies of Hermetia illucens genomic data analysis: firstly, the lack of available genomic data which limits researchers in Hermetia illucens genomic data analysis. Secondly, to the best of our knowledge, there are no Artificial Intelligence (AI) feature selection models designed specifically for Hermetia illucens genome. Unlike human genomic data, noise in Hermetia illucens data is a more serious problem. Third, how to choose those genes located in the pathway enrichment region. Existing models assume that each gene probe has the same priori weight. However, researchers usually pay more attention to gene probes which are in the pathway enrichment region. Based on the above challenges, we initially construct experiments and establish a new Cu2+-stressed Hermetia illucens growth genome dataset. Subsequently, we propose AWGE-ESPCA: an edge Sparse PCA model based on adaptive noise elimination regularization and weighted gene network. The AWGE-ESPCA model innovatively proposes an adaptive noise elimination regularization method, effectively addressing the noise challenge in Hermetia illucens genomic data. We also integrate the known gene-pathway quantitative information into the Sparse PCA(SPCA) framework as a priori knowledge, which allows the model to filter out the gene probes in pathway-rich regions as much as possible. Ultimately, this study conducts five independent experiments and compared four latest Sparse PCA models as well as representative supervised and unsupervised baseline models to validate the model performance. The experimental results demonstrate the superior pathway and gene selection capabilities of the AWGE-ESPCA model. Ablation experiments validate the role of the adaptive regularizer and network weighting module. To summarize, this paper presents an innovative unsupervised model for Hermetia illucens genome analysis, which can effectively help researchers identify potential biomarkers. In addition, we also provide a working AWGE - ESPCA model code in the address: https://github.com/yhyresearcher/AWGE_ESPCA.

Animals↗
PubMed2026

Protamine gene expression is associated with sperm motility in rams: An integrative experimental and gene network analysis.

Protamine 1 (PRM1) and protamine 2 (PRM2) are essential regulators of sperm chromatin condensation and genome integrity, and their dysregulation has been associated with impaired male fertility. However, their role in rams remains insufficiently characterized. This study investigated the relationship between protamine gene expression and semen quality in rams and explored their potential upstream regulatory mechanisms using gene regulatory network (GRN) analysis. Fifteen ejaculates from five rams were analyzed. Based on total sperm motility using computer-assisted sperm analysis (CASA), ejaculates were classified into a high-motility group (n&#x202f;=&#x202f;8) and a low-motility group (n&#x202f;=&#x202f;7). PRM1 and PRM2 expression levels were quantified by RT-qPCR. Following normality confirmation (p&#x202f;>&#x202f;0.05), parametric tests were applied using the ejaculate as the biological experimental unit. Samples with reduced motility showed significantly lower expression of both protamines (p&#x202f;<&#x202f;0.01). Moreover, progressive sperm motility was strongly correlated with both PRM1 (r&#x202f;=&#x202f;0.71, p&#x202f;=&#x202f;0.019) and PRM2 (r&#x202f;=&#x202f;0.69, p&#x202f;=&#x202f;0.03) transcript levels. Cross-species GRN inference using scGeneRAI and a reference human spermatogenesis dataset identified several hypothesis-generating candidate transcription factors, including HMGB4, HMGB1, H2AFZ, NKX6-1, and SMC3, consistently supported across multiple bootstrap resampling runs. These findings demonstrate a strong association between protamine expression and sperm motility in rams. While the identified candidate regulators provide a valuable framework for future species-specific validation, they also highlight promising candidate molecular biomarkers of male fertility in livestock.

Gene regulatory networks↗
PubMed2025

Dual transcriptional activities of PAX3 and PAX7 spatially encode spinal cell fates through distinct gene networks.

Understanding how transcription factors regulate organized cellular diversity in developing tissues remains a major challenge due to their pleiotropic functions. We addressed this by monitoring and genetically modulating the activity of PAX3 and PAX7 during the specification of neural progenitor pools in the embryonic spinal cord. Using mouse models, we show that the balance between the transcriptional activating and repressing functions of these factors is modulated along the dorsoventral axis and is instructive to the patterning of spinal progenitor pools. By combining loss-of-function experiments with functional genomics in spinal organoids, we demonstrate that PAX-mediated repression and activation rely on distinct cis-regulatory genomic modules. This enables both the coexistence of their dual activity in dorsal cell progenitors and the specific control of two major differentiation programs. PAX promote H3K27me3 deposition at silencers to repress ventral identities, while at enhancers, they act as pioneer factors, opening and activating cis-regulatory modules to specify dorsal-most identities. Finally, we show that this pioneer activity is restricted to cells exposed to BMP morphogens, ensuring spatial specificity. These findings reveal how PAX proteins, modulated by morphogen gradients, orchestrate neuronal diversity in the spinal cord, providing a robust framework for neural subtype specification.

Animals↗
PubMed2026

Integrative multi-omics analyses suggest a candidate microbial metabolite-associated host gene network in ulcerative colitis.

Ulcerative colitis (UC) is associated with gut microbial dysbiosis, but the host molecular alterations potentially linked to microbially derived metabolites remain incompletely understood. We integrated Mendelian randomization (MR), microbial metabolite annotation, computational target prediction, colonic transcriptomics, network analysis, and machine learning. MiBioGen microbiome GWAS data were used as exposures and FinnGen Release 12 ULCERENTER as the outcome. Metabolites linked to MR-prioritized taxa were retrieved from GutMGene, and human targets were predicted using SwissTargetPrediction and SEA. UC-related genes were defined by integrating differential expression analysis and WGCNA and then intersected with predicted metabolite targets. MR prioritized one family and eight genera showing nominal genetically supported associations with UC, but none remained significant after Benjamini-Hochberg FDR correction. Three prioritized genera were linked to 15 microbe-metabolite records, corresponding to 13 unique metabolites; nine were retained for target prediction, yielding 277 unique predicted human targets. Transcriptomic analysis identified 1,530 DEGs and a 312-gene MEgrey60 module, with 273 overlapping genes, producing 1,569 unique UC-related genes. Their intersection with the 277 predicted targets yielded 47 candidate genes. Enrichment analyses highlighted mainly metabolic and lipid-related processes. Random Forest showed the highest mean AUC across the two independent external benchmarking cohorts, and SHAP prioritized EPHX1, HSD17B2, IGFBP5, and MMP10. IBDome analysis showed inflammation-associated expression differences in these genes. This study provides a genomics-informed, hypothesis-generating framework that prioritizes candidate microbe-metabolite-host relationships in UC for future experimental validation.

Humans↗
PubMed2025

Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes.

MOTIVATION: The pathogenesis of diseases is closely associated with genes, and the discovery of disease genes holds significant importance for understanding disease mechanisms and designing targeted therapeutics. However, biological validation of all genes for diseases is expensive and challenging. RESULTS: In this study, we propose DGP-AMIO, a computational method based on graph attention networks, to rank all unknown genes and identify potential novel disease genes by integrating multi-omics and gene interaction networks from multiple data sources. DGP-AMIO outperforms other methods significantly on 20 disease datasets, with an average AUROC and AUPR exceeding 0.9. The superior performance of DGP-AMIO is attributed to the integration of multiomics and gene interaction networks from multiple databases, as well as triGAT, a proposed GAT-based method that enables precise identification of disease genes in directed gene networks. Enrichment analysis conducted on the top 100 genes predicted by DGP-AMIO and literature research revealed that a majority of enriched GO terms, KEGG pathways and top genes were associated with diseases supported by relevant studies. We believe that our method can serve as an effective tool for identifying disease genes and guiding subsequent experimental validation efforts. AVAILABILITY AND IMPLEMENTATION: DGP-AMIO is publicly available at https://github.com/yangkaiyuan1027/DGP-AMIO.

Gene Regulatory Networks↗
PubMed2025

Gene behaviors-based network enrichment analysis and its application to reveal immune disease pathways enriched with COVID-19 severity-specific gene networks.

MOTIVATION: Gene network analysis is essential for understanding the complex mechanisms underlying diseases, which often involve disruptions in molecular networks rather than individual genes. Despite the availability of large-scale omics datasets and computational tools for gene network analysis, interpretation of the biological relevance of these extensive networks remains challenging. RESULTS: We propose a novel computational strategy, gene behaviors-based network enrichment analysis, which systematically identifies functional pathways enriched in phenotype-specific gene networks. Our novel method incorporates comprehensive network characteristics, i.e. gene expression levels, edge strengths, and structural patterns of edges, to rank genes based on activity and assess pathway enrichment, effectively identifying functional pathways enriched within these networks. Through simulation studies, our strategy demonstrated superior performance compared with that of existing methods in identifying enriched pathways. We applied this strategy to whole-blood RNA-seq data from 1102 COVID-19 samples provided by the Japan COVID-19 Task Force. The analysis revealed immune disease pathways enriched with COVID-19 severity-specific gene networks, including "Systemic lupus erythematosus" in asymptomatic and severe samples and "Inflammatory bowel disease," "Primary immunodeficiency," and "Rheumatoid arthritis" in mild samples. Key biomarkers of COVID-19, such as CXCL8, S100A9, and HLA class I genes, have been identified as critical hub genes and the main players within these networks. AVAILABILITY AND IMPLEMENTATION: Code is available in Figshare (https://doi.org/10.6084/m9.figshare.29093648.v3).

COVID-19↗
PubMed2025

Integrated multi-omics analyses provide new insights into genomic variation landscape and regulatory network candidate genes associated with walnut endocarp.

Persian walnut (Juglans regia) is an economically important nut oil tree; the fruit has a hard endocarp/shell to protect seeds, thus playing a key role in its evolution, and the shell thickness is an important trait for walnut breeding. However, the genomic landscape and the gene regulatory networks associated with walnut shell development remain to be systematically elucidated. Here, we report a high-quality genome assembly of the walnut cultivar 'Xiangling' and construct a graphic structure pan-genome of eight Juglans species to reveal the genetic variations at the genome level. We re-sequence 285 accessions to characterize the genomic variation landscape. Through genome-wide association studies (GWAS), we identified 19 loci associated with more than 268 loci that underwent selection during walnut domestication and improvement. Multi-omics analyses, including transcriptomics, metabolomics, DNA methylation, and spatial transcriptomics across eleven developmental stages, revealed several candidate genes related to secondary cell biosynthesis and lignin accumulation. This integrated multi-omics approach revealed several candidate genes associated with secondary cell biosynthesis and lignin accumulation, such as UGP, MYB308, MYB83, NAC043, NAC073, CCoAOMT1, CCoAOMT7, CHS2, CESA7, LAC7, COBL4, and IRX12. Overexpression of JrUGP and JrMYB308 in Arabidopsis thaliana confirmed their roles in lignin biosynthesis and cell wall thickening. Consequently, our comprehensive multi-omics findings offer novel insights into walnut genetic variation and network regulation of endocarp development and shell thickness, which enable further genome-informed breeding strategies for walnut cultivar improvement.

Juglans↗
PubMed2025

A spectral framework to map QTLs affecting joint differential networks of gene co-expression.

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype &#xa0;&#x2192;&#xa0; expression &#xa0;&#x2192;&#xa0; phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype &#xa0;&#x2192;&#xa0; network &#xa0;&#x2192;&#xa0; phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Quantitative Trait Loci↗
PubMed2026

Causal assessment of Bayesian gene regulatory networks from single-cell transcriptomics.

Gene regulatory network (GRN) inference is an essential tool for revealing dysregulated relationships between genes in different cell types from single-cell transcriptomic (SCT) data. GRNs based on Bayesian networks (BNs) learned from SCT data can elucidate directed regulatory relationships representing complex disease mechanisms and their interplay through graphical modeling. However, software for learning BNs from SCT data is not widely available, nor is software for evaluating the BNs' structural accuracy in representing causal relationships between genes. Here, we describe the scstruc R package. This package provides a suite of BN structure learning algorithms specifically designed to handle SCT data, to evaluate the resulting networks based on the causal relationships they represent regardless of the availability of established molecular interaction networks, and to compare regulatory relationships between conditions. We demonstrated that scstruc can identify biologically relevant differential regulatory relationships between groups on a per-cell basis.

Bayesian networks↗