Peter V. Kharchenko

This paper discusses challenges and future directions in single-cell analysis, highlighting advances in technologies and computational methods for studying cellular heterogeneity.

UBiT2 is a client-side web application for analyzing RNA-seq and qPCR data, offering offline analysis and visualization with interactive tools and exportable figures.

This study compares PCA and t-SNE with distance metric modifications for visualizing and classifying single-cell RNA-seq data, finding that variable gene selection is critical for reliable classification.

This study introduces PAGODA, a method to detect transcriptional heterogeneity by analyzing pathway and gene set overdispersion, and applies it to characterize neuronal progenitors in the developing mouse cortex.