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Biology subjects

Tan, C. W.

Publications and source records attributed to Tan, C. W..

At least 19 recordsLinked to original sources

CXCL9-Dependent Immune Networks of Spontaneous Tertiary Lymphoid Structures in a Murine Subcutaneous Tumor Model

Recent discoveries have identified human tumor tertiary lymphoid structures (TLS) as organized clusters of innate and adaptive immune cells and stromal cells. Cancer-associated TLS can promote tumor clearance and enhance responses to immune checkpoint blockade (ICB) immunotherapy. However, the absence of TLS in conventional mouse tumor models, particularly subcutaneous transplant models, limits experimental manipulation and study of TLS development. By tuning tumor antigen expression, we have generated a subcutaneous tumor model that naturally forms TLS within the immune-hot, vasculature-rich peritumoral adipose tissue. These TLS were identified as clusters of B and T cells in close proximity to high endothelial venules. TLS organization was underpinned by spatially compartmentalized chemokine expression, with CCL21 produced in the T cell zone of perivascular TLS and CXCL13 present in the B cell region. Single cell transcriptomic sequencing showed that the immune composition in the TLS forming peritumoral tissue resembled human cancer TLS signatures. Upon anti-PD-L1 ICB treatment, TLS underwent expansion, which was associated with increased interferon responses in macrophages and coincided with tumor clearance. We further identified the interferon-inducible chemokine CXCL9 as a key regulator of the TLS microenvironment. CXCL9-expressing macrophages populated TLS, interacting with B cells following ICB treatment. CXCL9 deficiency reduced B cell recruitment to peritumoral tissue and TLS formation. Further T and B cell receptor sequencing indicated a CXCL9-dependent promotion of antigen-specific responses within the tumor and TLS niche. Combined, these findings establish a tractable subcutaneous model of naturally arising tumor TLS and define the cellular and molecular networks that regulate their formation and function. This provides a platform to dissect how TLS develop and test strategies that promote TLS formation to enhance cancer therapy.

immunology↗

bgnorm: A Generative Statistical Framework for Background Correction, Normalisation, and Quality Control in Multiplex Spatial Proteomics

Multiplex spatial proteomics enables highly multiplexed in situ profiling but remains limited by technical variation arising from autofluorescence, non-specific antibody binding, instrument noise, and staining variability, affecting downstream biological tasks like cell typing. We present bgnorm, a statistical framework that describes fluorescence measurements using a generative mixture model of background, non-specific binding, and biological signal components. As natural statistical consequences, the model yielded three new methods: a background-correction method through probabilistic deconvolution of protein intensities, quality control metrics, and a quantile normalisation approach to unify measurements across markers, samples, and sequential slices. Across multiple multiplex imaging technologies, bgnorm improves signal separation and downstream marker positivity classification compared with existing preprocessing approaches. In expert-annotated datasets comprising over 406,000 marker positivity annotations, bgnorm achieved the highest classification performance and enabled accurate use of a single global positivity threshold across markers and samples. The method is implemented in the bgnormR and bgnormpy packages.

bioinformatics↗

Mitigation of imprinted antibody responses in elderly COVID-19 highly vaccinated individuals

SARS-CoV-2 continues to evolve from the Omicron serotype, with BA.2.86 sublineage JN.1 and descendants predominating in 2025-26 and recent emergence of the highly divergent BA.3.2 saltation variant. Elderly individuals continue to be at greatest risk of clinical complications from COVID-19, yet contemporary data on kinetics of immune potency and breadth following multiple vaccinations remain limited in this group. We studied a cohort of forty-three healthy older adults (median age = 85 years, IQR 75-88, 40% female). Using both pseudotyped virus (PVNT) and surrogate virus (SVNT) neutralization-based assays, we demonstrate that JN.1 and KP.2 vaccinations six months apart elicit high potency neutralization across all studied variants except BA.3.2.2, where titres were around 2-3 fold lower. Waning of serum neutralizing activity was modest between vaccine doses, suggesting sustained immunity following multiple vaccines. Importantly, the half-life of serum neutralization against Wu-1 (179 days) was substantially longer compared to JN.1 subvariants (43.7-70.1days), consistent with imprinted and longer-lived Wu-1-specific responses. Furthermore, half-life for BA.3.2.2 neutralisation was prolonged at 107 days compared to JN.1 subvariants, implicating imprinted ancestral variant targeting antibodies in neutralization of this saltation variant. While absolute neutralization titers remained highest against ancestral Wu-1 at all timepoints due to multiple historical exposures and accumulation, the recall responses after the recent KP.2 vaccine revealed a shift in immunodominance: neutralization against full-length Wu-1 spike was not boosted, whereas all tested JN.1 descendants and BA.3.2.2 showed significant boosts, indicating that immune imprinting against ancestral Wu-1 was partially overcome. These data indicate that in the elderly, protective neutralizing antibody responses against recent VOC can be achieved, with alleviation of immune imprinting and reduction of waned responses through biannual, strain-updated booster vaccines. Imprinted antibodies against ancestral pre Omicron variants may still be relevant for protection against further highly immune evasive variants such as BA.3.2.2, highlighting that careful characterisation of population level COVID-19 humoral immunity is still warranted.

microbiology↗

Improved marker detection for rare population in single-cell transcriptomics through text mining-inspired scoring approach

Accurate identification of cell types and states is a crucial step when analysing single cell RNA-seq data. Inaccurate cell type annotation leads to spurious results and biological interpretations in subsequent analyses. Cell type identification is traditionally done using established marker genes of each cell population. However, many existing methods do not perform well for imbalanced cell populations, which often occurs in the presence of rare cell types. Most existing methods tend to be biased towards the dominant cell populations or highly expressed genes, leading to inaccurate results. Here, we introduce the package smartid, which accurately identifies markers from even imbalanced data across batches by employing a modified Term Frequency-Inverse Document Frequency approach with Gaussian Mixture Model. smartid is also a gene-set scoring method which is able to distinguish the target group of interest. smartid is implemented in R and is freely available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/smartid.html.

bioinformatics↗

Bioengineered human tonsil organoids as an immuno-engineering platform for evaluating immune functions

Although animal models remain the preclinical gold standard, they are constrained by interspecies differences in adaptive immunity, including antibody class-switching, affinity maturation, and multicellular interactions. To address this gap, we established a more physiologically relevant platform, a bioengineered tonsil organoid (BTO) system that combines autologous tonsil-derived stromal cells with immune cells to generate highly uniform organoids, preserving multicellular complexity and sustained stromal-immune interactions. BTO cultures mounted robust, antigen-specific recall responses to influenza, tetanus, and COVID-19 antigens. Notably, stimulation with ovalbumin and SARS-CoV-1 spike protein expanded antigen-recognizing B cells, indicating capacity for naive responses. Digital spatial transcriptomics and proteomics analysis confirmed immune-stromal crosstalk underpinning both humoral and cellular responses of BTO after challenge. These data highlight stromal cells as active scaffolds that not only maintain 3D architecture but also facilitate immune activation, memory recall, and recognition of naive antigens. Together, BTO offers a human-relevant model for mechanistic immunology and for pre-clinical evaluation on immunotherapies and vaccines not achievable with 2D cultures or animal models.

bioengineering↗

SM3DD with Segmented PCA: A Comprehensive Method for Interpreting 3D Spatial Transcriptomics

We developed Standardised Minimum 3D Distance (SM3DD), an entirely cell segmentation/annotation-free approach to the analysis of spatial RNA datasets, using it to compare lung tissue from 16 clinically normal individuals to those of 18 SARS-CoV-2 patients who died from acute respiratory distress syndrome. RNA spatial coordinates were determined using the CosMx Spatial Molecular Imager (Bruker Spatial Biology, US). For each individual transcript location, we calculated the three-dimensional distances to the nearest transcript of each transcript type, standardising the distances to each transcript type. Mean SM3DDs were compared between normal and SARS-CoV-2 patients. Notably, hierarchical clustering of the directional log10(P) values organized genes by functionality, making it easier to interpret biological contexts and for FKBP11, where a decrease in distance to MZT2A was the most significant difference, suggesting a role in interferon signaling. Using a segmented principal components analysis of the entire SM3DD dataset, we identified multiple pathways, including SARS-CoV-2 infection, even though the assay did not include any SARS-CoV-2 transcripts.

bioinformatics↗

PRISM: A Python Package for Interactive and Integrated Analysis of Multiplexed Tissue Microarrays

Tissue microarrays (TMAs) enable researchers to analyse hundreds of tissue samples simultaneously by embedding multiple samples into single arrays, enabling conservation of valuable tissue samples and experimental reagents. Moreover, profiling TMAs allows efficient screening of tissue samples for translational and clinical applications. Multiplexed imaging technologies allow for spatial profiling of proteins at single cell resolution, providing insights into tumour microenvironments (TMEs) and disease mechanisms. High-plex spatial single cell protein profiling is a powerful tool for biomarker discovery and translational cancer research, however, there remain limited options for end-to-end computational analysis of this type of data. Here, we introduce PRISM, a Python package for interactive, end-to-end analyses of TMAs with a focus on translational and clinical research using multiplexed proteomic data from the CODEX, Phenocycler Fusion (Akoya Biosciences), Comet (Lunaphore), MACSima (Miltenyi Biotec), CosMx and Cellscape (Bruker Spatial Biology) platforms. PRISM leverages the SpatialData framework to standardise data storage and ensure interoperability with single cell and spatial analysis tools. It consists of two main components: TMA Image Analysis for marker-based tissue masking, TMA dearraying, cell segmentation, and single cell feature extraction; and AnnData Analysis for quality control, clustering, iterative cell-type annotation, and spatial analysis. Integrated as a plugin within napari, PRISM provides an intuitive and purely interactive graphical interface for real-time and human-in-the-loop analyses. PRISM supports efficient multi-resolution image processing and accelerates bioinformatics workflows using efficient scalable data structures, parallelisation and GPU acceleration. By combining modular flexibility, computational efficiency, and a completely interactive interface, PRISM simplifies the translation of raw multiplexed images to actionable clinical insights, empowering researchers to explore and interact effectively with spatial omics data.

bioinformatics↗

IdentifiHR: predicting homologous recombination deficiency in high-grade serous ovarian carcinoma using gene expression

BackgroundApproximately half of all high-grade serous ovarian carcinomas (HGSCs) have a therapeutically targetable defect in the homologous recombination (HR) DNA repair mechanism. While there are genomic and transcriptomic methods, developed for other cancer types, to identify HR deficient (HRD) samples, there are no gene expression-based tools to predict HR repair status in HGSC specifically. We have built the first HGSC-specific model to predict HR repair status using gene expression. MethodsWe separated The Cancer Genome Atlas (TCGA) cohort of HGSCs (n = 361) into training (n = 288) and testing (n = 73) sets and labelled each case as HRD or HR proficient (HRP) based on the clinical standard for classification, being a score of HRD genomic damage. Using the training set, we performed differential gene expression analysis between HRD and HRP cases. The 2604 significantly differentially expressed genes were then used to tune and train a penalised logistic regression model. ResultsIdentifiHR is an elastic net penalised logistic regression model that uses the expression of 209 genes to predict HR status in HGSC. These genes capture known regions of HR-specific copy number alteration, which impact gene expression levels, and preserve the genomic damage signal. IdentifiHR has an accuracy of 85% in the TCGA test set and of 91% in an independent cohort of 99 samples, collected from primary tumours before (n = 74/99) and after autopsy (n = 6/99), in addition to ascites (n = 12/99) and normal fallopian tube samples (n = 7/99). Further, IdentifiHR is 84% accurate in pseudobulked single-cell HGSC sequencing from 37 patients and outperforms existing gene expression-based methods to predict HR status, being BRCAness, MutliscaleHRD and expHRD. ConclusionsIdentifiHR is an accurate model to predict HR status in HGSC using gene expression alone, that is available as an R package from https://github.com/DavidsonGroup/IdentifiHR.

cancer biology↗

Transient inhibition of type I interferon enhances CD8+ T cell stemness and vaccine protection

Developing vaccines that promote CD8+ T cell memory is a challenge for infectious disease and cancer immunotherapy. TCF-1+ stem cell-like memory T (TSCM) cells are important determinants of long-lived memory. Yet, the developmental requirements for TSCM formation are unclear. Here, we identify the temporal window for type I interferon (IFN-I) receptor (IFNAR) blockade to drive TSCM cell generation. TSCM cells were transcriptionally distinct and emerged from a transitional precursor of exhausted (TPEX) cellular state concomitant with viral clearance. TSCM differentiation correlated with T cell retention within the lymph node paracortex, due to increased CXCR3 chemokine abundance which disrupted gradient formation. These affects were due a counterintuitive increase in IFN{psi}, which controlled cell location. Combining IFNAR inhibition with mRNA-LNP vaccination promoted specific TSCM differentiation and enhanced protection against chronic infection. These finding propose a new approach to vaccine design whereby modulation of inflammation promotes memory formation and function. HIGHLIGHTSO_LIEarly, transient inhibition of the type I interferon (IFN) receptor (IFNAR) during acute viral infection promotes stem cell-like memory T (TSCM) cell differentiation without establishing chronic infection. C_LIO_LITSCM and precursor of exhausted (TPEX) cellular states are distinguished transcriptionally and by cell surface markers. C_LIO_LIDevelopmentally, TSCM cell differentiation occurs via a transition from a TPEX state coinciding with viral clearance. C_LIO_LITransient IFNAR blockade increases IFN{psi} production to modulate the ligands of CXCR3 and couple TSCM differentiation to cell retention within the T cell paracortex of the lymph node. C_LIO_LISpecific promotion of TSCM cell differentiation with nucleoside-modified mRNA-LNP vaccination elicits enhanced protection against chronic viral challenge. C_LI

immunology↗

Divergent cytokine and transcriptional signatures control functional T follicular helper cell heterogeneity

Adaptive immune responses protect against multiple classes of pathogens, including viral, bacterial, fungal, and helminth infections. In all these settings, CD4+ T follicular helper (Tfh) cells tailor high-affinity class-switched B cells responses. How Tfh lineage sovereignty is established while allowing for this context-specific functional heterogeneity is unclear. Here, we identify Tfh transcriptional networks in response to diverse infections. While Bcl-6 is the transcriptional linchpin of the core Tfh signature, this is overlayed with pathogen-specific transcriptional modules that shape Tfh function. Cytokine-transcriptional Tfh programing in mouse and human lymphoid tissue demonstrated that type I interferon and TGF{beta} signaling direct individual Tfh subpopulations to instruct B cell output. Here, we provide a transcriptional map and cell surface resource to interrogate Tfh diversity in humans and mice. This resource can be leveraged to further understand the origins of immune flexibility, perform immune monitoring in infection and antibody-mediated diseases and to develop context-specific vaccines. ONE-SENTENCE SUMMARYDalit, Tan and colleagues provide a resource that functionally and transcriptionally profiles T follicular helper cells (Tfh) during diverse pathogen responses to reveal a blueprint for transcriptional flexibility and new tools to interrogate Tfh heterogeneity in mice and humans.

immunology↗

mastR: Marker Automated Screening Tool for multi-omics data

MotivationBiomarker discovery and utilization is important and offers insight into potential underlying mechanisms of disease. Existing marker identification methods primarily focus on single cell RNA sequencing (scRNA-seq) data, with no specific automated methods designed to learn from the bulk RNA-seq data. Furthermore, when adapting scRNA-seq methods to bulk RNA-seq, the background expressions of non-targeted cell types are not accounted for. Here we bridge this gap with an automated marker identification method that works for bulk RNA sequencing data. ResultsWe developed mastR, a novel computational tool for accurate marker identification from omics data. It leverages robust pipelines from edgeR and limma R/Bioconductor packages, performing pairwise comparisons between groups, and aggregating the results through rank-product-based permutation test. A signal-to-noise ratio approach is implemented to minimize background signals. We assess the performance of a mastR-derived NK cell signature against curated published signatures and find our derived signature performs as well if not better than published signatures. We also demonstrate the utility of mastR on simulated scRNA sequencing data and provide examples of mastR outperforming Seurat in marker selection. Availability and implementationAll statistical analyses were carried out using R (version 4.3.0 or higher) and Bioconductor (version 3.17 and higher). MastR is available as an R/Bioconductor package with a comprehensive vignette for ease of use (https://bioconductor.org/packages/release/bioc/html/mastR.html) and a guide hosted on GitHub: https://davislaboratory.github.io/mastR/.

bioinformatics↗

hoodscanR: profiling single-cell neighborhoods in spatial transcriptomics data

Understanding complex cellular niches and neighborhoods have provided new insights into tissue biology. Thus, accurate neighborhood identification is crucial, yet existing methodologies often struggle to detect informative neighborhoods and generate cell-specific neighborhood profiles. To address these limitations, we developed hoodscanR, a Bioconductor package designed for neighborhood identification and downstream analyses using spatial data. Applying hoodscanR to breast and lung cancer datasets, we showcase its efficacy in conducting detailed neighborhood analyses and identify subtle transcriptional changes in tumor cells from different neighborhoods. Such analyses can help researchers gain valuable insights into disease mechanisms and potential therapeutic targets.

bioinformatics↗

mRNA vaccines encoding membrane-anchored receptor-binding domains of SARS-CoV-2 mutants induce strong humoral responses and can overcome immune imprinting

To address the limitations of whole-spike COVID vaccines, we explored mRNA vaccines encoding membrane-anchored receptor-binding domain (RBD-TMs), each a fusion of a variant RBD, the transmembrane (TM) and cytoplasmic tail (CT) fragments of the SARS-CoV-2 spike protein. In naive mice, RBD-TM mRNA vaccines against ancestral SARS-CoV-2, Beta, Delta, Delta-plus, Kappa, Omicron BA.1 or BA.5, all induced strong humoral responses against the target RBD. Multiplex surrogate viral neutralization (sVNT) assays indicated broad neutralizing activity against a range of variant RBDs. In the setting of a heterologous boost, against the background of exposure to ancestral whole spike vaccines, sVNT studies suggested that RBD-TM vaccines were able to overcome the detrimental effects of immune imprinting. Omicron BA.1 and BA.5 RBD-TM booster vaccines induced serum antibodies with 12 and 22-fold higher neutralizing activity against the target RBD than their equivalent whole spike variants. Boosting with BA.1 or BA.5 RBD-TM provided good protection against more recent variants including XBB and XBB.1.5. Each RBD-TM mRNA is 28% of the length of its whole-spike equivalent. This advantage will enable tetravalent mRNA vaccines to be developed at well-tolerated doses of formulated mRNA. One Sentence SummarymRNA vaccines encoding membrane-anchored RBDs of SARS-CoV-2 mutants are effective vaccines that can overcome immune imprinting in mice

immunology↗

standR: a Bioconductor package for analysing transcriptomic Nanostring GeoMx DSP data

To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cell in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here we present standR, a R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how application of standR enables scientists to develop in-depth insights into the biology of interest.

bioinformatics↗

Library size confounds biology in spatial transcriptomics data

Spatial molecular technologies have revolutionised the study of disease microenvironments by providing spatial context to tissue heterogeneity. Recent spatial technologies are increasing the throughput and spatial resolution of measurements, resulting in larger datasets. The added spatial dimension and volume of measurements poses an analytics challenge that has, in the short-term, been addressed by adopting methods designed for the analysis of single-cell RNA-seq data. Though these methods work well in some cases, not all necessarily translate appropriately to spatial technologies. A common assumption is that total sequencing depth, also known as library size, represents technical variation in single-cell RNA-seq technologies, and this is often normalised out during analysis. Through analysis of several different spatial datasets, we noted that this assumption does not necessarily hold in spatial molecular data. To formally assess this, we explore the relationship between library size and independently annotated spatial regions, across 23 samples from 4 different spatial technologies with varying throughput and spatial resolution. We found that library size confounded biology across all technologies, regardless of the tissue being investigated. Statistical modelling of binned total transcripts shows that tissue region is strongly associated with library size across all technologies, even after accounting for cell density of the bins. Through a benchmarking experiment, we show that normalising out library size leads to sub-optimal spatial domain identification using common graph-based clustering algorithms. On average, better clustering was achieved when library size effects were not normalised out explicitly, especially with data from the newer sub-cellular localised technologies. Taking these results into consideration, we recommend that spatial data should not be specifically corrected for library size prior to analysis unless strongly motivated. We also emphasise that spatial data are different to single-cell RNA-seq and care should be taken when adopting algorithms designed for single cell data.

bioinformatics↗

Single shot dendritic cell targeting SARS-CoV-2 vaccine candidate induces broad and durable systemic and mucosal immune responses

Current COVID-19 vaccines face certain limitations, which include waning immunity, immune escape by SARS-CoV-2 variants, limited CD8+ cellular response, and poor induction of mucosal immunity. Here, we engineered a Clec9A-RBD antibody construct that delivers the Receptor Binding Domain (RBD) from SARS-CoV-2 spike protein to conventional type 1 dendritic cells (cDC1). We showed that single dose immunization with Clec9A-RBD induced high RBD-specific antibody titers with a strong T-helper 1 (TH1) isotype profile and exceptional durability, whereby antibody titers were sustained for at least 21 months post-vaccination. Uniquely, affinity maturation of the antibody response was observed over time, as evidenced by enhanced neutralization potency and breadth across the sarbecovirus family. Consistently and remarkably, RBD-specific T-follicular helper cells and germinal center B cells were still detected at 12 months post-immunization. Increased antibody-dependent cell-mediated cytotoxicity (ADCC) activity of the immune sera was also measured over time with comparable efficacy against ancestral SARS-CoV-2 and variants, including Omicron. Furthermore, Clec9A-RBD immunization induced a durable poly-functional TH1-biased cellular response that was strongly cross-reactive against SARS-CoV-2 variants, including Omicron, and with robust CD8+ T cell signature. Lastly, Clec9A-RBD single dose systemic immunization primed effectively RBD-specific cellular and humoral mucosal immunity in lung. Taken together, Clec9A-RBD immunization has the potential to trigger robust and sustained, systemic and mucosal immune responses against rapidly evolving SARS-CoV2 variants.

immunology↗

Mucosal vaccination for SARS-CoV-2 elicits superior systemic T central memory function and cross-neutralizing antibodies against variants of concern

COVID-19 vaccines used in humans are highly effective in limiting disease and death caused by the SARS-CoV-2 virus, yet improved vaccines that provide greater protection at mucosal surfaces, which could reduce break-through infections and subsequent transmission, are still needed. Here we show that intranasal (I.N.) vaccination with the receptor binding domain of Spike antigen of SARS-CoV-2 (S-RBD) in combination with the mucosal adjuvant mastoparan-7 improved systemic T cell responses compared to an equivalent dose of antigen delivered by the sub-cutaneous (S.C.) route, adjuvanted by either M7 or the gold-standard adjuvant, alum. T cell phenotypes induced by I.N. vaccine administration included enhanced polyfunctionality (combined IFN-{gamma} and TNF expression) and greater numbers of T central memory (TCM) cells. These phenotypes were T cell-intrinsic and could be recalled in the lungs and/or brachial LNs upon antigen challenge after adoptive T cell transfer to naive recipients. Furthermore, mucosal vaccination induced antibody responses that were similarly effective in neutralizing the binding of the parental strain of S-RBD to its ACE2 receptor, but showed greater cross-neutralizing capacity against multiple variants of concern (VOC), compared to S.C. vaccination. These results highlight the role of nasal vaccine administration in imprinting an immune profile associated with long-term T cell retention and diversified neutralizing antibody responses, which could be applied to improve vaccines for COVID-19 and other infectious diseases.

immunology↗

vissE: A versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and often leads to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis method that summarises redundancies into biological themes and provides various analytical modules to characterise and visualise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissEs versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, removing investigator bias from molecular discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/483195v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1c384b0org.highwire.dtl.DTLVardef@13c6bd3org.highwire.dtl.DTLVardef@1cb1d91org.highwire.dtl.DTLVardef@24cbf_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗