About

Yun S. Song is a Professor of Electrical Engineering and Computer Sciences, Statistics, and the Director of the Center for Computational Biology at UC Berkeley. His research interests encompass computational biology, population genomics, applied probability, and statistics. He is specifically interested in analyzing mathematical models and developing statistical inference tools to investigate the mechanistic details of gene expression dynamics at both transcription and translation levels. His work involves probing the underlying biological processes through computational and statistical methods, contributing to a deeper understanding of gene regulation, evolution, and genomics.

Research topics

• Cell biology
• Computational biology
• Biology
• Genetics

Selected publications

• XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment

  Science Advances · 2021 · 116 citations

  • Computational biology
  • Biology
  • Cell biology

  Single-cell RNA sequencing (scRNA-seq) of tissues has revealed remarkable heterogeneity of cell types and states but does not provide information on the spatial organization of cells. To better understand how individual cells function within an anatomical space, we developed XYZeq, a workflow that encodes spatial metadata into scRNA-seq libraries. We used XYZeq to profile mouse tumor models to capture spatially barcoded transcriptomes from tens of thousands of cells. Analyses of these data revealed the spatial distribution of distinct cell types and a cell migration-associated transcriptomic program in tumor-associated mesenchymal stem cells (MSCs). Furthermore, we identify localized expression of tumor suppressor genes by MSCs that vary with proximity to the tumor core. We demonstrate that XYZeq can be used to map the transcriptome and spatial localization of individual cells in situ to reveal how cell composition and cell states can be affected by location within complex pathological tissue.

  PublisherDOI

Recent grants

• Mathematical Models and Statistical Methods for Large-Scale Population Genomics

  NIH · $2.2M · 2010–2020

• Population Genetic Consequences of Recent Explosive Population Growth in Humans

  NIH · $2.2M · 2014–2019

• CAREER: Computational Methods for High-Throughput Sequencing and Population Genomics Analysis

  NSF · $664k · 2009–2015

• NIH Grant K99GM080099

  NIH · $85k · 2007

• Robust and efficient statistical inference methods for genomics

  NIH · $2.0M · 2019–2025

Frequent coauthors

• Jeffrey P. Spence

  Stanford University

  98 shared

• Sanjit Singh Batra

  University of California, Berkeley

  77 shared

• Isaac B. Hilton

  Rice University

  62 shared

• Alan Cabrera

  Rice University

  62 shared

• Chun Ye

  Gladstone Institutes

  41 shared

• Jillian F. Banfield

  University of California, Berkeley

  36 shared

• Jack Kamm

  35 shared

• Matthias Steinrücken

  University of Chicago

  35 shared

Labs

• Center for Computational BiologyPI

Similar researchers at University of California, Berkeley

• Peidong Yangh-262
• Jennifer A. Doudnah-230
• Jeffrey R. Longh-189
• John F. Hartwigh-180
• Rasmus Nielsenh-180