Daniel Osorio

The paper analyzes gene regulatory networks across 9,000 tumors to identify recurrent PD-1 pathway dysregulation linked to patient outcomes.

The paper identifies a novel plasmacytoid dendritic cell lineage in mice and humans derived from common lymphoid progenitors, characterized by unique gene expression and functional properties.

This paper introduces SCORPION, a tool for reconstructing gene regulatory networks from single-cell RNA-seq data, showing it outperforms existing methods and detects consistent regulatory differences in colorectal cancer.

This study shows that targeting endothelial FOXO1 reduces IGF2 levels, improves β-cell function, and enhances wound healing in diabetes.

This study compares single-cell transcriptomic profiles of human inner ear organoids with fetal and adult inner ear tissues to assess their ability to model inner ear disorders.

This study uses single-cell RNA-seq based transfer learning to prioritize synergistic drug combinations for cancer treatment by aligning patient tumor cells with similar cell lines.

The paper develops a computational tool to predict drug combinations for TNBC by integrating transcriptional signatures with drug response data.

The paper identifies PD1 signaling regulation as a prognostic marker in glioblastoma using gene regulatory network analysis across two cohorts.

The paper introduces rPanglaoDB, an R package for downloading and merging labeled single-cell RNA-seq data from PanglaoDB, and demonstrates its use in characterizing fibrocytes through data integration.

scTenifoldKnk predicts gene function via virtual knockout using scRNA-seq data from wild-type samples, recapitulating real-animal KO results.

scTenifoldNet constructs and compares single-cell gene regulatory networks using scRNA-seq data to identify gene expression changes in response to various biological conditions.

The paper evaluates mitochondrial proportion thresholds in scRNA-seq data across human and mouse tissues, proposing tissue-specific thresholds to improve QC accuracy.

The paper identifies genes and gene sets with increased expression variability in schizophrenia, linking this variability to genetic factors and brain development processes.

The paper analyzes single-cell expression variability in homogeneous human cell populations, finding it widespread, heritable, and functionally relevant.

The paper presents single-cell RNA sequencing data from two lymphoblastoid cell lines and their mixture, providing insights into population differentiation in gene expression and aiding normalization methods.