Davide Risso

This paper identifies and categorizes technical biases in imaging spatial transcriptomics and proposes a unified framework to model these biases.

The paper introduces a method to enhance RNA-seq tools for differential usage, aberrant splicing, and expression retrieval by modifying normalization offsets and presents a faster tool for outlier detection.

SpaceTrooper provides a data-driven quality control framework for imaging-based spatial omics data, improving clustering and cell-type coherence by identifying low-quality cells without fixed thresholds.

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

This paper benchmarks five scRNA-seq analysis frameworks on scalability, efficiency, and accuracy using large and small datasets, finding GPU acceleration and optimized algorithms improve performance.

The paper develops a workflow for differential detection analysis in single-cell RNA-seq data and shows it provides complementary information to differential expression analysis.

omicsGMF integrates dimensionality reduction, batch correction, and imputation for bulk and single-cell proteomics data, improving statistical power for differential abundance detection.

Dune improves replicability of cell type discovery in scRNA-Seq by optimizing the trade-off between cluster resolution and cross-dataset consistency.

This study presents a global transcriptional atlas of sleep deprivation effects in mouse frontal cortex using bulk and single-nuclear RNA sequencing, showing widespread transcriptional repression and isoform switching in glutamatergic neurons.

The paper provides a Bioconductor package with curated landmark datasets for single-cell multimodal data, enabling easy access and analysis of seven different technologies.

signifinder is an R package that collects, implements, and compares cancer-related gene expression signatures across bulk, single-cell, and spatial transcriptomic data to improve reproducibility and usability.

The paper benchmarks ATAC-seq normalization methods and shows that GC-content effects must be accounted for to improve downstream analysis accuracy.

SpatialExperiment provides a data infrastructure for spatially resolved transcriptomics data in R/Bioconductor, with examples from Visium and seqFISH platforms.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

The paper identifies a species-specific retrotransposon promoter that generates a conserved Cdk2ap1 isoform essential for preimplantation development across mammals.

PsiNorm is a scalable normalization method for scRNA-seq data that performs well in accuracy and computational efficiency compared to other methods.

The paper introduces AWST, a data transformation method that improves clustering accuracy in RNA-seq data by reducing the need for gene selection.

The paper introduces mbkmeans, a fast clustering tool for large single-cell datasets using mini-batch k-means, showing improved performance over standard k-means on datasets up to 1.3 million cells.

The paper identifies brain-region specific splicing patterns at single-cell and spatial resolution in postnatal mice, revealing cell-type specific and region-specific isoform expression.

The paper benchmarks statistical methods for microbiome data against RNA-seq methods, finding that univariate RNA-seq methods perform as well as microbiome-specific methods.

The paper identifies non-neuronal cell types in the olfactory system expressing SARS-CoV-2 entry genes, suggesting indirect mechanisms for COVID-19-associated anosmia.

This paper provides an overview of single-cell RNA sequencing analysis using Bioconductor, highlighting its tools and methods for handling high-dimensional data.

This study shows that Shank3 modulates sleep and circadian transcription factor expression in mice, with sleep deprivation exacerbating gene expression differences between Shank3 mutants and wild types.

This paper evaluates normalization methods for scRNA-seq using a framework called scone, showing that top methods improve agreement with validation data.

The paper identifies distinct somatostatin interneuron circuits that specifically control activity in different cortical layers through complementary inhibitory mechanisms.

The paper introduces clusterExperiment, a Bioconductor package for clustering large gene expression datasets using Resampling-based Sequential Ensemble Clustering (RSEC) to identify biologically meaningful subtypes.

Slingshot builds a cluster-level minimum spanning tree to find branching lineages without fixing their number, then fits simultaneous principal curves for stable per-lineage pseudotimes.

The paper introduces a weighting strategy using a zero-inflated negative binomial model to improve bulk RNA-seq tools for single-cell RNA-seq data with zero inflation.

The paper introduces ZINB-WaVE, a method for improving low-dimensional representations of scRNA-seq data by accounting for dropouts and over-dispersion, outperforming PCA and ZIFA.