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<h4>Motivation</h4>Single-cell 3'-tagging sequencing, such as that provided by 10x Genomics, can be used to study alternative polyadenylation (APA). APA can affect RNA function, stability, and subcellular localization, thereby influencing development and disease processes. Computational tools …
Read PDF<h4>Motivation</h4>Flow cytometry (FC) is a widely used technique for analysing cells or particles based on the fluorescence of specific markers. Thresholds for fluorescence are typically set manually, a laborious, subjective process that scales poorly as …
Read PDF<h4>Summary</h4>Cell-cell communication dynamically changes across time while involving diverse cell populations and ligand types such as proteins and metabolites. Single-cell transcriptomics enables its inference, but existing tools typically analyze ligand types separately and overlook their …
Read PDF<h4>Summary</h4>Identifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an …
Read PDF<h4>Motivation</h4>Spatial omics data provides unprecedented insights into disease biology, yet its complexity introduces significant challenges in data analysis. Comprehensive analysis requires frameworks that integrate diverse modalities and enable joint processing of multiple datasets and corresponding …
Read PDF<h4>Introduction/objective</h4>Single-cell RNA sequencing (scRNA-seq) resolves cell types and molecular phenotypes within heterogeneous specimens but typically requires fresh, high-quality single-cell suspensions processed immediately to preserve transcriptional profiles. This constraint complicates samples with long preparation times and …
Read PDFThe identification of cell types is a basic step of pipelines for Single-Cell RNA sequencing (scRNA-seq) data analysis. However, unsupervised clustering of cells from scRNA-seq data has multiple challenges: high dimensionality, sparseness of the expression …
Read PDF<h4>Motivation</h4>Accurate demultiplexing of pooled single-cell RNA-seq (scRNA-seq) data is critical for large-scale studies. However, existing methods like vireo, while effective up to ∼16 donors, often struggle with poor clustering due to local optima as donor …
Read PDF<h4>Summary</h4>Adoptive cellular immunontherapies, such as chimeric antigen receptor (CAR) T cell therapy, have transformed cancer treatment, yet challenges such as resistance, relapse, and high costs limit their efficacy and accessibility. A comprehensive understanding of cellular …
Read PDF<h4>Summary</h4>Gene signature scoring provides a simple yet powerful approach for quantifying biological signals within single-cell omics datasets. UCell and pyUCell offer fast and robust implementations of rank-based signature scoring for R and Python, respectively, integrating …
Read PDF<h4>Summary</h4>Characterizing the distribution of biological marker expression at the single cell level in whole tissues requires diverse image analysis steps, such as segmentation of cells and nuclei, detection of RNA transcripts (or other staining), or …
Read PDF<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 PDF<h4>Motivation</h4>Electrophysiological recordings are essential in experimental and computational neuroscience, providing insights into neuronal excitability and network behaviour. Extracting features such as action potential thresholds, widths, and firing patterns is conceptually straightforward, but in practice it …
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>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>Analysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type-specific regulatory regions and their effects on gene expression. While powerful, such analysis …
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 …
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