David L. Gibbs

The paper identifies a cold fibrotic network in LUSC TME driven by CAFs and shows its conservation in other CIACs, suggesting therapeutic targets.

The paper identifies a fibroblast state called STIF that drives YAP-dependent regenerative programs and (pre)malignancy through mechanotransduction in response to tissue injury.

This study identifies OPG-secreting CAFs as immunosuppressive stromal cells and shows that blocking OPG promotes immune infiltration and tumor regression in mouse models.

The paper describes how the Human Tumor Atlas Network shares data through standards, infrastructure, and community engagement, making datasets accessible via multiple platforms.

The paper introduces a method to compute gene set scores for single-cell data by smoothing expression values using a nearest neighbor graph, improving accuracy over bulk methods.

The paper develops a method to generate patient-specific cell communication networks from gene expression data and shows that specific communication edges are associated with disease progression in cancer.

The paper develops a Functional Module States framework to predict drug sensitivity by analyzing transcriptional states from gene expression data in breast cancer cell lines.

The paper introduces an ensemble classifier using XGBoost to robustly classify tumor samples into six immune subtypes based on gene-level features.

ADAPTS improves cell type deconvolution in single-cell RNAseq data by customizing signature matrices and using hierarchical clustering to refine cell type identification.

The paper introduces an empirical adaptation of Brown’s method to combine dependent P-values, showing it outperforms Fisher’s method on simulated and TCGA data.