Christian H. Holland

decoupleR evaluates methods for inferring biological activities from omics data, finding simple linear models and consensus scores perform best in perturbation experiments.

The paper benchmarks bulk functional genomics tools on scRNA-seq data, showing comparable performance to bulk data and suggesting their applicability over dedicated single-cell tools.

The paper tests if human pathway and TF footprints can be applied to mouse data, showing they recover mouse perturbations and disease associations.

The paper links perturbation transcriptomics data with cell viability phenotypes to identify cell death and proliferation signatures, improving drug sensitivity prediction.

The paper benchmarks and integrates TF regulon resources to assess their impact on TF activity estimation in human gene expression data.