Biology subjects

Bhuva, D. D.

Publications and source records attributed to Bhuva, D. D..

11 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2025

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↗

bioRxiv2024

SpaNorm: spatially-aware normalisation for spatial transcriptomics data

Library size normalisation is necessary to enable comparisons between observations in transcriptomic datasets. Numerous methods have been developed to normalise these effects with sample and gene specific adjustments. However, in spatial transcriptomics data, normalisation is complicated by the fact that spatial region-specific library size confounds biology. The most popular approach of adapting methods developed for single-cell RNA-seq data has been shown to excessively remove biological signals associated with spatial domains and thus results in poorer downstream domain identification. To this end, we propose the first spatially-aware normalisation method, SpaNorm. SpaNorm concurrently models spatial library size effects and the underlying smooth biology, to tease apart these effects, and thereby remove library size effects without removing biology. This is achieved through optimal decomposition of spatially smooth variation into those related and unrelated to library size and the use of location-specific scaling factors. Using 27 tissue samples from 6 datasets spanning 4 spatial platforms, we show that SpaNorm outperforms current state of the art methods at retaining biological information in the form of spatial domains and spatially variable genes (SVGs) better than 4 commonly used single-cell normalisation approaches. SpaNorm is versatile and it can be used for both spot-based and subcellular spatial transcriptomics data. Notably, the benefit of using SpaNorm is more pronounced for the latter data such as those from Xenium, STOmics and CosMx platforms for which the proportion of genes exhibiting region-specific library size effect is higher. SpaNorm works equally well with segmented cell-level data and spot-based data where each spot contains multiple cells.

bioinformatics↗

bioRxiv2024

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↗

bioRxiv2024

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↗

bioRxiv2023

Identification of cell types, states and programs by learning gene set representations

As single cell molecular data expand, there is an increasing need for algorithms that efficiently query and prioritize gene programs, cell types and states in single-cell sequencing data, particularly in cell atlases. Here we present scDECAF, a statistical learning algorithm to identify cell types, states and programs in single-cell gene expression data using vector representation of gene sets, which improves biological interpretation by selecting a subset of most biologically relevant programs. We applied scDECAF to scRNAseq data from PBMC, Lung, Pancreas, Brain and slide-tags snRNA of human prefrontal cortex for automatic cell type annotation. We demonstrate that scDECAF can recover perturbed gene programs in Lupus PBMC cells stimulated with IFNbeta and TGFBeta-induced cells undergoing epithelial-to-mesenchymal transition. scDECAF delineates patient-specific heterogeneity in cellular programs in Ovarian Cancer data. Using a healthy PBMC reference, we apply scDECAF to a mapped query PBMC COVID-19 case-control dataset and identify multicellular programs associated with severe COVID-19. scDECAF can improve biological interpretation and complement reference mapping analysis, and provides a method for gene set and pathway analysis in single cell gene expression data.

bioinformatics↗

bioRxiv2023

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↗

bioRxiv2023

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↗

bioRxiv2023

Spatial transcriptomic analysis of Sonic Hedgehog Medulloblastoma identifies that the loss of heterogeneity and promotion of differentiation underlies the response to CDK4/6 inhibition

BackgroundMedulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups based on gene expression and genomic features. Single cell transcriptome studies have defined the cellular states underlying each MB subgroup, however the spatial organisation of these diverse cell states and how this impacts response to therapy remains to be determined. MethodsHere, we used spatially resolved transcriptomics to define the cellular diversity within a sonic hedgehog (SHH) patient-derived model of MB and identify how cells specific to a transcriptional state or spatial location are pivotal in responses to treatment with the CDK4/6 inhibitor, Palbociclib. We integrated spatial gene expression with histological annotation and single cell gene expression data from MB, developing a analysis strategy to spatially map cell type responses within the hybrid system of human and mouse cells and their interface within an intact brain tumour section. ResultsWe distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolve at a cellular resolution a distinct tumour interface where the tumour contacts neighbouring mouse brain tissue consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. ConclusionsOur data highlight the power of using spatial transcriptomics to characterise the response of a tumour to a targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB.

genomics↗

bioRxiv2022

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↗

bioRxiv2020

Stable gene expression for normalisation and single-sample scoring

BackgroundTranscriptomic signatures are useful in defining the molecular phenotypes of cells, tissues, and patient samples. Their most successful and widespread clinical application is the stratification of breast cancer patients into molecular (PAM50) subtypes. In most cases, gene expression signatures are developed using transcriptome-wide measurements, thus methods that match signatures to samples typically require a similar degree of measurements. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical applications, and accordingly thousands of existing gene signatures are unexplored in a clinical context. ResultsGenes in a molecular signature can provide information about molecular phenotypes and their underlying transcriptional programs from tissue samples, however determining the transcriptional state of these genes typically requires the measurement of all genes across multiple samples to allow for comparison. An efficient assay and scoring method should quantify the relative abundance of signature genes with a minimal number of additional measurements. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across large numbers (thousands) of samples, allowing signature scoring, and supporting general data normalisation for transcriptomic data. Based on singscore, we have developed a new method, stingscore, which quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these "stably-expressed genes". ConclusionWe show that our proposed list of stable genes has better stability across cancer and normal tissue data than previously proposed stable or housekeeping genes. Additionally, we show that signature scores computed from whole-transcriptome data are comparable to those calculated using only values for signature genes and our panel of stable genes. This new approach to gene expression signature analysis may facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

bioinformatics↗