Michael I. Love

apeglm replaces DESeq2's Normal prior on log fold changes with an adaptive Cauchy prior, reducing variance for low-count genes while preserving true large effect sizes.

SAFE-clustering integrates multiple clustering methods to improve accuracy and robustness in scRNA-seq data analysis, reducing cluster deviation and improving assignment compared to individual methods.

MOVIE evaluates overfitting and stability of unsupervised multi-omics methods using contribution plots from real and permuted null datasets.

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.

This study compares the epigenomes of neutrophils and monocytes to identify regulatory elements and chromatin states, revealing differences in enhancer activity and DNA methylation.

The paper identifies multi-loop activation hubs during macrophage development using Hi-C data, showing they are enriched for AP-1 binding and driven by cell-type specific transcription factors.

zingeR improves differential expression analysis in scRNA-seq by using a zero-inflated negative binomial model to address zero-inflation, matching performance of dedicated scRNA-seq tools.

Salmon provides accurate and fast transcript expression estimates by modeling and correcting GC content bias using a dual-phase inference algorithm.

DESeq2 uses empirical Bayes priors to shrink negative binomial dispersions and log fold changes, giving reproducible effect sizes and error-controlled differential expression calls for RNA-seq.