Steven M. Foltz

The paper introduces a data portal and open-source tools for single-cell transcriptomics of pediatric tumors, providing uniformly processed RNA-seq data and metadata from over 700 samples across 55 cancer types.

This study uses longitudinal multi-omic profiling to identify immune escape mechanisms and predictors of response in multiple myeloma.

This study uses linked-read sequencing to phase somatic mutations in multiple myeloma, extending haplotype blocks and revealing phasing patterns in key mutated genes.

OpenPBTA provides a collaborative platform to genomically characterize 1,074 pediatric brain tumors and 22 cell lines, offering analysis modules and somatic datasets for research and clinical applications.

This study evaluates normalization methods for combining microarray and RNA-seq data, finding that quantile and Training Distribution Matching normalization work best for machine learning tasks.