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<h4>Summary</h4>Several approaches have been proposed to reconstruct interactions between groups of cells or individual cells from single-cell transcriptomics data, leveraging prior information about known ligand-receptor interactions. To enhance downstream analyses, we present an end-to-end dimensionality …
Read PDF<h4>Motivation</h4>Long non-coding RNAs (lncRNAs) have emerged as crucial players in diverse physiological and pathological processes, yet the biological mechanisms of the vast majority of lncRNAs remain elusive. To fill this gap, it is necessary to …
Read PDFN 6-methyladenosine (m6A), the most prevalent internal RNA modification, is an emerging key regulator of gene expression in the central nervous system, and its dysregulation is connected to psychiatric disorders. However, disentangling the causal links …
Read PDF[This corrects the article DOI: 10.3389/fcell.2026.1859833.].
Read PDF<h4>Motivation</h4>Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide profiling at single-cell resolution, revealing heterogeneous regulatory programs and making gene regulatory network (GRN) inference both central and challenging. Recent graph neural network (GNN)-based approaches for GRN inference typically …
Read PDF<h4>Summary</h4>Splitpea takes skipped exon event data at the sample or differential expression level from SUPPA2 and rMATS and maps potential changes to protein-protein interaction (PPI) network rewiring events. It handles a variety of input formats …
Read PDFMyocardial fibrosis is a common pathological feature of multiple cardiovascular diseases, including heart failure, hypertension, and myocardial infarction, and is associated with poor prognosis. Despite extensive research, clinically validated molecular biomarkers for early diagnosis and …
Read PDFMulti-omics integration combines data from transcriptomics, proteomics, and metabolomics to provide insights into biological systems. Here, we present a protocol for integrating and interpreting multi-omics data using unsupervised multi-omics factor analysis (MOFA), supervised projection-based integration …
Read PDF<h4>Motivation</h4>Network analysis has become a central strategy for dissecting complex biological and environmental systems, particularly as modern omics technologies generate increasingly large and heterogeneous datasets. However, current tools often lack the scalability, flexibility, and native …
Read PDF<h4>Motivation</h4>Single-cell analysis of monoallelic expression and genomic imprinting requires accurate genotype determination at the cellular level. However, genotype inference from single-cell RNA sequencing data is challenging due to technical noise, allelic dropout, and sparse gene …
Read PDF<h4>Motivation</h4>Batch effects are a common challenge in the analysis of biological datasets, particularly RNA-seq data. Although numerous methods exist to correct batch effects, their comparative evaluation has received limited attention. Several metrics have been proposed …
Read PDF<h4>Summary</h4>The utility of single-cell RNA sequencing (scRNA-seq) is premised on the notion that transcriptional state can faithfully reflect cell phenotype. However, scRNA-seq measurements are noisy and sparse, with individual transcript counts showing limited correlation with …
Read PDF<h4>Motivation</h4>Spatial transcriptomics techniques capture gene expression data and spatial coordinates, while simultaneously correlating them with tissue section images. This advantage makes Spatial transcriptomics data highly valuable for research, such as investigating disease mechanisms and cancer …
Read PDF<h4>Motivation</h4>Splicing, a critical co-transcriptional process in eukaryotes, enhances transcriptome diversity by generating isoforms specific to cell types, tissues, or developmental stages. Recent advancements in splicing modulators have opened new avenues for targeting previously undruggable genes …
Read PDF<h4>Motivation</h4>Metabolism occurs in a cell type-specific manner, but which cells regulate metabolite levels remains unclear.<h4>Results</h4>Here, we integrate some of the largest metabolite quantitative trait loci datasets, TOPMed and UK Biobank, with one of the most …
Read PDF<h4>Motivation</h4>High-throughput RNA sequencing has significantly advanced transcriptomic profiling in oncology. Millions of RNA-seq datasets have accumulated in public databases such as the Sequence Read Archive (SRA). However, fragmented, ambiguous, or missing metadata can severely limit …
Read PDF<h4>Motivation</h4>Deciphering gene regulatory networks (GRNs) from single-cell transcriptomics data remains a fundamental challenge in computational biology. It is hindered by data sparsity, high dimensionality, and the lack of scalable, generalizable inference models. To address this, …
Read PDF<h4>Summary</h4>We introduce REACTOR, a computational tool designed to detect differential activity of transcriptional regulators and their target genes (regulons) in single-cell RNA-sequencing data. It expands the currently available framework for regulon analysis by introducing a …
Read PDFMetabolic reprogramming is central to cancer biology, enabling tumor cells to sustain rapid proliferation, resist stress, and adapt to therapy. However, these alterations are highly heterogeneous across cancer types, and current treatments rarely exploit subtype-specific …
Read PDFThis letter comments on the recently published AICellType platform for large language model (LLM)-based cell type annotation in single-cell and spatial transcriptomics. While appreciating the authors' systematic benchmarking and practical contribution, concerns are raised regarding …
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