Aedín C. Culhane

The paper introduces demux_bench, a reproducible and portable pipeline for benchmarking genetic demultiplexing methods in scRNA-seq using Nextflow and Apptainer.

demuxSNP improves demultiplexing in scRNA-seq by combining cell hashing with SNP data, outperforming existing methods on low-quality data.

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

This paper introduces correspondence analysis (CA) as an alternative to PCA for scRNA-seq data, showing it outperforms existing methods in 8 out of 9 datasets.

This paper shows that molecular subtypes and gene signatures in ovarian cancer are influenced by stromal cell content, suggesting that bulk tissue analysis may be unreliable without accounting for cell admixture.

The paper develops a multi-gene signature predicting PARP inhibitor response across cancer types using machine learning and validates it in vitro and in clinical datasets.

MOGSA integrates multiple omics data to improve gene set analysis by combining features across data types and reducing noise, revealing molecular subtypes in bladder cancer.

This paper reviews matrix factorization applications for systems-level omics analysis, highlighting their ability to uncover biological knowledge from high-dimensional data.

The paper introduces MultiAssayExperiment, a Bioconductor package for integrating multi-omics data, demonstrating its use with TCGA datasets to simplify analysis and visualization.