Andrew J. Gentles

EcoNet improves immunotherapy response prediction by modeling tumor microenvironment signaling networks, outperforming existing methods in AUC and stratifying ccRCC patients by clinical outcome.

The paper integrates mouse lymph node tumor data with human melanoma tumors to identify transcriptional programs and ecotypes associated with metastasis.

PhenoMapR maps bulk expression phenotypes to single-cell and spatial transcriptomics data to identify biologically relevant cellular populations and spatial niches.

The paper identifies spatially distinct epithelial states in PDAC that correlate with immunosuppressive myeloid niches and poor prognosis.

The paper identifies and isolates human AML leukemia stem cells using single-cell multi-omics and establishes a prognostic gene signature for clinical relevance.

DeSpotX improves spatial transcriptomics by decontaminating transcript data using a deep generative model that identifies contamination and enhances biological coherence.

The paper updates the PRECOG resource by adding pediatric and immunotherapy datasets, enabling analysis of gene expression associations with clinical outcomes using statistical models and deconvolution.

This study identifies new functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis using multiomic single cell analyses and functional assays.

MONTAGE identifies spatially resolved functional enrichment gradients in tumor microenvironments by analyzing spatial communities and their clinical relevance.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

The paper analyzes the translatome of astrocytes and microglia after stroke to identify their distinct roles in inflammation during hyperacute and acute phases.

CytoSPACE aligns single-cell and spatial transcriptomes using convex linear optimization, outperforming existing methods in noise-tolerance, accuracy, and efficiency.

This study uses single-cell transcriptomics to identify gene regulatory networks and lineage trajectories in human fetal thymic epithelial cells, revealing interferon response pathways that distinguish thymic epithelium from other anterior foregut-derived organs.

CELESTA identifies cell types in multiplexed in situ images by integrating protein expression and spatial information, revealing novel spatial biology associated with lymph node metastasis.

The paper identifies TCF4 overexpression driven by 18q21.2 gains as a mechanism promoting IgM expression in ABC-like DLBCL and shows BET inhibitors can suppress it.

This paper introduces AMARETTO, a multi-omics integration algorithm that identifies pancancer driver genes related to smoking and antiviral responses by analyzing eleven cancer types.