Motivation Single-cell RNA sequencing (scRNA-seq) has become an attractive tool for studying complex diseases, in which transient cell states affecting diverse cell populations characterise disease development and progression. However, due to data sparsity and disease heterogeneity analysis is often challenging. With recent advances in machine learning, two widely used approaches have emerged for learning cellular representations: large-scale foundation models and biological knowledge-guided methods. Despite their complementary strengths, there is currently no unified workflow for systematically comparing and integrating these approaches. Results Here, we present scRepresenter, an open-source workflow for computing, integrating, and validating cellular embeddings derived from foundation models and biological knowledge-guided methods in the context of complex diseases. It consists of two components: a command-line workflow that computes cellular embeddings and performs downstream analyses, and an interactive Shiny application for visualizing and comparing the computed embeddings. scRepresenter supports four categories of cellular representations: (1) expression-based, (2) knowledge-guided, (3) foundation model-derived, and (4) hybrid embeddings that combine foundation model-derived representations with knowledge-guided representations. This approach takes a cell-by-gene count matrix as input and outputs an integrated object containing the computed embeddings. Then, this object can be uploaded into our interactive Shiny application to compare different embeddings. Availability The workflow is available at https://github.com/GuilhermePocas/scRepresenter Contact AL291@cam.ac.uk; MA2129@cam.ac.uk
Abstract Motivation Inferring gene networks from single-cell RNA sequencing data is challenging due to high sparsity, dimensionality, and technical noise. Current pipelines lack the multi-dataset integration and comprehensive post-processing analysis. Results scGraphVerse is an R package that integrates multiple algorithms (GENIE3, GRNBoost2, ZILGM, PCzinb, and JRF) with extensive evaluation and visualization tools. Its modular workflow supports early, late, and joint integration strategies for multi-dataset analysis, providing standardized input/output interfaces and biological interpretation tools, including community detection, pathway enrichment, and literature mining. Benchmarking on simulated data showed model-based methods (PCzinb and ZILGM) perform well with limited sample sizes, while JRF performs best as the network size and dataset numbers increase. A PBMC case study demonstrates JRF’s ability to identify literature-supported regulatory communities across donors. Availability and implementation The package is available in Bioconductor 3.22 at https://bioconductor.org/packages/release/bioc/html/scGraphVerse.html. Code and examples: https://github.com/ngsFC/scGV_analysis.
Francesco Cecere, D. De Canditiis, Annamaria Carissimo et al.· Bioinformatics Advances· 0 citations
A Framework for Learning Over REgulatory-Embedding Networks (FloREN), a supervised and interpretable sample representation method that enables improved sample stratification and biomarker discovery and supports downstream analyses that found specific immune network mechanisms in immune-mediated inflammatory diseases (IMIDs).
Iñigo Clemente‐Larramendi, S. Hillion, D. Cornec et al.· bioRxiv· 0 citations
Conventional annotation of single-cell RNA-sequencing (scRNA-seq) data relies heavily on manual, marker-based thresholding, an approach that can obscure subtle transcriptomic gradients and collapse functionally distinct cell states into broad, heterogeneous populations. Here we apply the Gaussian multi-Graphical Model (GmGM) framework, which jointly infers cell-cell and gene-gene dependency structure from a single scRNA-seq data matrix, to a 10x Genomics PBMC dataset. Ten independent GMGM-Leiden clustering runs were integrated into a robust consensus partition using a soft cluster ensemble approach and benchmarked against reference cell-type annotations. This strategy yielded stable cluster partitions that resolve biologically meaningful sub-populations not distinguished by the reference annotation. In parallel, for each cluster, gene co-expression modules were extracted from the fitted model via consensus Leiden clustering across resolutions, evaluated using standard network metrics, and validated functionally with the Network Enrichment Analysis Test (NEAT), which confirmed non-random enrichment signal. A module-scoring procedure linked network topology to per-cell, per-cluster expression signatures, and a novel extension of GmGM, recovering a shared cell-cell network together with population-specific gene networks in a single model run, was demonstrated in a case study on the CD4+ T-cell population. These results indicate that GmGM provides a unified, reproducible framework for joint cell clustering and gene-network inference, capable of revealing cellular structure beyond that captured by conventional pipelines.
O. Lanzetta, L. Cutillo, Bailey Andrew et al.· 0 citations
Single-cell RNA sequencing now routinely produces detailed maps of cell types and states, but interpreting a finished project remains harder than it should be. Once the analysis is done, the results are usually handed over as static reports, figure panels and supplementary tables. A biologist who later wants to revisit an annotation, recompute a cell-type proportion or check whether a pathway is specific to one group typically has to return to a bioinformatician rather than explore the data directly. We developed scSpark to close this gap. The platform takes the completed outputs of a single-cell project: cell annotations, embeddings, differential-expression tables, trajectories, cell-cell communication networks and enrichment results—and serves them through a web browser as an interactive workspace. Heavy computation stays upstream: scSpark indexes the precomputed objects under a single project structure and exposes them through six modules for cell annotation, differential analysis, trajectory exploration, cell-cell communication, functional interpretation and AI-assisted result interrogation. Every action in these modules, from a query to a label change, an export or an AI-generated summary, is linked to a specific project version, data object, parameter set and output file, so that any conclusion can be traced back to the evidence behind it. We illustrate the platform by reworking a published periodontitis dataset through this interface. scSpark does not replace upstream pipelines or expert judgement; it is a layer that makes their results easier to inspect, revise and reuse, and that turns a single-cell project from a one-off report into an interpretation others can follow and check. Significance Statement Single-cell studies produce increasingly intricate maps of tissues, but those maps are hard to interrogate once they have been written up as static reports. scSpark tackles this post-analysis bottleneck by holding a project’s annotations, marker evidence, differential results, pathways, communication networks, AI-generated summaries and publication-ready figures together in one workspace, where each item is linked to the data and settings that produced it. The platform is built to support expert decisions rather than to make them: its aim is to let researchers check, revise and reuse a result, and to see exactly how it was reached.
Jin Zhang, Zhengxi Liu, Zhongqi Pu et al.· bioRxiv· 0 citations
Bulk and single-cell RNA sequencing (scRNA-seq) have become essential for investigating disease mechanisms and identifying diagnostic biomarkers. However, the growing volume of transcriptomic data remains difficult to reuse efficiently for many researchers. Downstream analysis often requires multiple statistical, visualization, and reporting tools, creating fragmented workflows that reduce transparency and reproducibility, particularly when analyzing scRNA-seq data. To address this gap, we developed CoTRA (Comprehensive Toolbox for RNA-seq Analysis), an open-source R/Shiny package for bulk and scRNA analysis. CoTRA integrates established methods into modular workflows, exposes parameters, and offers alternatives at selected stages. It supports bulk RNA-seq quality assessment, differential expression, annotation, enrichment, and reporting, as well as scRNA quality control, dimensionality reduction, clustering, marker identification, cell-type annotation, differential abundance, trajectory inference, pathway activity, and cell-cell communication. CoTRA runs on workstations or HPC environments without mandatory external data submission and was tested on Linux, Windows, and macOS. Compared with 14 other platforms for bulk RNA-seq/scRNA-seq, CoTRA supported 46 of 49 predefined functionality criteria. Tool validation using published rd10 retinal bulk RNA-seq identified 1,947 shared differentially expressed genes with concordant direction and strong log2 fold-change agreement. A retinal scRNA-seq case study demonstrated appropriate clustering, cell-type resolved analysis, and pathway activity scoring. CoTRA provides a graphical environment for bulk and single-cell RNA-seq analysis while retaining parameter transparency, methodological flexibility, and reproducible outputs. Strong concordance with the published bulk RNA-seq analysis supports the workflow consistency, while the single-cell case study demonstrates its applicability to advanced scRNA-seq analysis. The source code is freely available at https://github.com/UmairSeemab/CoTRA.