Aaron T. L. Lun

Epiregulon: Inference of single-cell transcription factor activity to predict drug response and drivers of cell state

Epiregulon infers single-cell TF activity using ATAC-seq and RNA-seq to predict drug response and identify drivers of cell state.

edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets

edgeR v4 adds a continuous negative binomial for fractional counts, bias-corrected quasi-likelihood for small counts, C-based fitting, and new transcript, exon, methylation and fold-change-threshold analyses.

Systematic perturbation screens decode regulators of inflammatory macrophage states and identify a role for TNF mRNA m6A modification

The paper identifies regulators of inflammatory macrophage states and shows that TNF mRNA m6A modification controls its stability and contributes to disease.

Powering single-cell analyses in the browser with WebAssembly

kana enables interactive single-cell omics analysis in the browser using WebAssembly, completing analysis of over 100,000 cells in 5 minutes on a laptop.

The crisprVerse: a comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies

The paper introduces crisprVerse, a Bioconductor ecosystem for designing and annotating CRISPR gRNAs across multiple technologies and nucleases.

SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor

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

Transposable element expression at unique loci in single cells with CELLO-seq

The paper introduces CELLO-seq, a long-read RNA sequencing method for single cells, to study TE expression in mouse and human cells, finding locus-specific expression and regulatory heterogeneity.

Orchestrating Single-Cell Analysis with Bioconductor

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

Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data

The paper introduces a statistical method to distinguish real cells from empty droplets in single-cell RNA sequencing data, improving detection of low-UMI cell libraries while controlling false discovery rates.

Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data

Log-transforming normalized scRNA-seq counts with a pseudo-count of 1 creates spurious expression differences and artificial clusters or trajectories when size factors vary; a derived larger pseudo-count bounds this error.

Detection and removal of barcode swapping in single-cell RNA-seq data

The paper quantifies barcode swapping in single-cell RNA-seq data and develops an algorithm to remove swapped reads, reducing artifacts in droplet-based experiments.

No kissing in the nucleus: Unbiased analysis reveals no evidence of trans chromosomal regulation of mammalian immune development

The paper uses high-resolution genome-wide chromosome conformation capture to analyze transchromosomal interactions in immune cells and finds no evidence of gene regulatory transchromosomal interactions.

Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis

This study compares the off-target effects of RNAi, antisense oligonucleotides, and CRISPRi in transcriptomic analyses, showing that all methods have non-negligible off-target effects and recommend using multiple LOF methods for functional validation.

beachmat: a Bioconductor C++ API for accessing single-cell genomics data from a variety of R matrix types

The paper introduces beachmat, a C++ API for efficient access to single-cell genomics data in various R matrix formats, demonstrating its performance and integration capabilities.

Correcting batch effects in single-cell RNA sequencing data by matching mutual nearest neighbours.

This paper introduces a batch correction method for scRNA-seq data that matches mutual nearest neighbours, showing improved performance over existing methods on simulated and real data.

Assessing The Reliability Of Spike-In Normalization For Analyses Of Single-Cell RNA Sequencing Data

This study evaluates spike-in normalization in single-cell RNA sequencing, finding it reliable despite technical noise and minor variance in spike-in RNA addition and behavior.