Thomas D. Sherman

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS

The paper introduces computational tools for NMF analysis of single-cell RNA-seq data, demonstrating their use in quantifying cell state transitions in pancreatic cancer.

CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

The paper introduces CoGAPS 3, an upgraded Bayesian non-negative matrix factorization tool for single-cell analysis, improving computational efficiency and enabling analysis of larger datasets.

Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species.

The paper introduces scCoGAPS and projectR to decompose cell identity into shared and species-specific factors, enabling transfer learning across datasets and species.

CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer

CancerInSilico simulates bulk and single-cell gene expression data from mathematical models to enable benchmarking of time course analysis methods with known ground truth.

Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development.

This study uses scRNA-Seq to analyze human and mouse retinal development, identifying conserved and species-specific gene expression patterns and a novel role for ATOH7 in photoreceptor specification.

Comprehensive analysis of retinal development at single cell resolution identifies NFI factors as essential for mitotic exit and specification of late-born cells

The paper uses single-cell RNA sequencing to analyze retinal development stages and identifies NFI factors as regulators of late progenitor cell specification and mitotic exit.

PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF

The paper introduces PatternMarkers and GWCoGAPS to identify data-driven biomarkers from whole-genome data using NMF, demonstrating their application on GTex data.