Distinguished Speakers Seminar Series
Learning from Data in Single-Cell Transcriptomics
Seminar Series Details:
About Sandrine Dudoit (UC Berkeley)
Sandrine Dudoit is Executive Associate Dean in the College of Computing, Data Science, and Society, Professor in the Department of Statistics, and Professor in the Division of Biostatistics, School of Public Health, at the University of California, Berkeley. She was Chair of the Department of Statistics at UC Berkeley from July 2019 to June 2022.
She is a founding core developer of the Bioconductor Project (http://www.bioconductor.org), an open-source and open-development software project for the analysis of biomedical and genomic data
Professor Dudoit is a co-author of the book Multiple Testing Procedures with Applications to Genomics and a co-editor of the book Bioinformatics and Computational Biology Solutions Using R and Bioconductor. She is Associate Editor of The Annals of Applied Statistics, RSS: Data Science and Artificial Intelligence, and IEEE/ACM Transactions on Computational Biology and Bioinformatics. Professor Dudoit was named Fellow of the American Statistical Association (2010), Elected Member of the International Statistical Institute (2014), and Fellow of the Institute of Mathematical Statistics (2021). She received a Medallion Award from the Institute of Mathematical Statistics (2025).
About this Class
The ability to measure gene expression levels for individual cells (vs. pools of cells) and with spatial resolution is crucial to address many important biological and medical questions, such as the study of stem cell differentiation, the discovery of cellular subtypes in the brain, and cancer diagnosis and treatment. Single-cell transcriptome sequencing (RNA-Seq) allows the high-throughput measurement of gene expression levels for entire genomes at the resolution of single cells. Spatially-resolved transcriptomics further allows the measurement of gene expression levels along with the location of the RNA molecules within a tissue. Transcriptomics exemplifies the range of issues one encounters in a data science workflow, where the data are complex in a variety of ways, questions are not always clearly formulated, there are multiple analysis steps, and drawing on rigorous statistical principles and methods is essential to derive meaningful and reliable biological results.
In this talk, Dr. Dudoit will provide a survey of statistical questions related to the analysis of single-cell transcriptome sequencing data to investigate the differentiation of stem cells in the brain, including, exploratory data analysis, expression quantitation, cluster analysis, and the inference of cellular lineages. She will also address differential expression analysis in spatial transcriptomics.