Daniel Schnell

Quantifying tumor specificity using Bayesian probabilistic modeling for drug target discovery and prioritization

This paper introduces BayesTS, a Bayesian probabilistic score for tumor specificity to prioritize drug targets, demonstrating its effectiveness across multiple tissues and data types.

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

The paper introduces MAMS, a metadata standard for single-cell data matrices and analysis workflows, to improve reproducibility and interoperability across datasets.

LungMAP Portal Ecosystem: Systems-Level Exploration of the Lung

The paper describes the LungMAP Portal Ecosystem, a centralized resource integrating multi-omics and imaging data across species to enable standardized analysis of lung biology and disease.

Fetal maturation revealed by amniotic fluid cell-free transcriptome in rhesus macaques

The paper uses transcriptomic data from amniotic fluid and fetal tissues to identify biomarkers of fetal maturity in rhesus macaques, showing that cell-free RNA can predict lung compliance and gestational age.

CellDrift: Inferring Perturbation Responses in Temporally-Sampled Single Cell Data

CellDrift identifies temporally varied perturbation patterns in single-cell data by analyzing covarying gene expression across time, outperforming existing methods in imputation and pattern detection.

Identification and Removal of Doublets with DoubletDecon

DoubletDecon identifies and removes doublets from scRNA-seq data using deconvolution and gene expression analysis.

Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF

The paper introduces an improved clustering algorithm for scRNA-seq data that outperforms existing methods on benchmark datasets and scales to large-scale data.

DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets

DoubletDecon identifies and removes doublets in scRNA-Seq data using deconvolution and cell-state gene expression analysis, improving cell state delineation.

cellHarmony: Cell-level matching and holistic comparison of single-cell transcriptomes

cellHarmony matches and compares single-cell transcriptomes to identify disease-specific gene regulatory programs and cell-state changes.