Alexander Dietrich

Rectangle provides a scalable deconvolution framework for bulk RNA-seq using single-cell data, achieving high accuracy and robustness across diverse benchmarks.

This paper benchmarks second-generation single-cell-informed deconvolution tools for bulk RNA-seq, revealing performance differences based on data characteristics and providing an ecosystem called omnideconv.

The paper introduces deconvMe, an R package for DNA methylation-based cell-type deconvolution, and compares its results to gene expression and experimental data.

The paper uses bulk RNA-seq to analyze immune responses to SARS-CoV-2 variants, showing that 10 million reads per sample can estimate immune cell composition and BCR/TCR sequences.

SimBu simulates bulk RNA-seq data with variable cell type composition to create in silico gold standards for evaluating deconvolution methods.

Namco provides a user-friendly R Shiny application for comprehensive microbiome analysis, covering data processing to complex tasks like machine learning and network analysis.

DICAST benchmarks alternative splicing analysis tools, showing STAR and HISAT2 perform best, and proposes a reporting standard for event detection.