Natalie R. Davidson

This paper integrates multiple transcriptomics assays to identify cancer biomarkers, finding GBP4 as a predictor of recurrence and immune remodeling.

This study shows HGSC transcriptomic subtypes are driven by tumor cellular composition rather than intrinsic biology, with high predictive accuracy using deconvolution methods.

The paper deconvolves adipocyte proportions from bulk RNA-seq data of HGSOC tumors and finds higher adipocyte content is associated with worse survival.

The paper examines how missing cell types in single-cell references affect deconvolution of bulk data and shows that missing cell-type profiles can be detected using non-negative matrix factorization.

BuDDI predicts cell-type-specific perturbations from bulk data by integrating single-cell and bulk profiles using domain adaptation in a variational autoencoder.

The paper uses deconvolution on bulk transcriptomic data to show that HGSOC subtypes are driven by tumor microenvironment composition, particularly fibroblast and immune cell content.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

pmVAE learns interpretable single-cell representations by incorporating pathway structure, improving detection of perturbation-targeted pathways in single-cell RNA-seq data.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

The paper identifies hundreds of genes deregulated by SVs in cancer, showing increased expression linked to SV breakpoints and enhancer elements.

The paper analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.