Sandrine Dudoit, PhD
Professor and Chair, Department of Statistics
Professor, Division of Epidemiology and Biostatistics, School of Public Health
University of California, Berkeley
367 Evans Hall, #3860
Berkeley, CA 94720-3860
Fax: (510) 642-7892
Postdoctoral position. Elizabeth Purdom and I are recruiting a postdoc to work on the development of statistical methods and software for the analysis of high-throughput genomic data, with an emphasis on single-cell platforms. This work is motivated by a collaboration with neurobiologist John Ngai, on exciting questions such as the discovery and characterization of novel cell types and the study of stem cell differentiation in the brain.
If interested, please follow the application process detailed in this document.
Advising and enrollment. Please consult the webpage.
Prospective graduate students. Please take the time to read carefully the information on the program websites (Statistics, Biostatistics) before contacting me. You will find answers to most of your questions regarding application procedures, admission criteria, degree requirements, and financial support on these websites. If you still have questions regarding administrative matters, please contact La Shana Porlaris (Statistics) or Sumaiya Elahi (Biostatistics).
P. Boileau, N. S. Hejazi, and S. Dudoit (2019). Exploring high-dimensional biological data with sparse contrastive principal component analysis, bioRxiv.
Bioconductor R package: scPCA.
K. Van den Berge, H. Roux de B ́ezieux, K. Street, W. Saelens, R. Cannoodt, Y. Saeys, S. Dudoit, and L. Clement (2019). Trajectory-based differential expression analysis for single-cell sequencing data, bioRxiv.
Bioconductor R package: tradeSeq.
Statistical Methods and Software for Investigating Stem Cell Differentiation Using Single-Cell Transcriptome Sequencing, Department of Statistics and Actuarial Science, Simon Fraser University, March 23, 2018.
Keynote Presentation, Intelligent Systems for Molecular Biology (ISMB), Orlando, FL, July 11, 2016.
Using Single-Cell Transcriptome Sequencing to Infer Olfactory Stem Cell Fate Trajectories.
Workshop, BioC 2016: Where Software and Biology Connect, Stanford, CA, June 25, 2016.
Analysis of Single-Cell RNA-Seq Data with R and Bioconductor.
R package bioc2016singlecell: [GitHub]
Research and Teaching Activities
My research and teaching activities concern the development and
application of statistical methods and software for the analysis
of biomedical and genomic data.
Statistical methodology. My methodological research
interests regard high-dimensional inference and include
exploratory data analysis (EDA), visualization, loss-based
estimation with cross-validation (e.g., density estimation,
regression, model selection), and multiple hypothesis testing.
Applications to biomedical and genomic research. Much of my
methodological work is motivated by statistical inference
questions arising in biological research and, in particular, the
design and analysis of high-throughput microarray and sequencing
gene expression experiments, e.g., single-cell transcriptome sequencing (RNA-Seq) for discovering novel cell types and for the study of stem cell differentiation. My contributions include: exploratory data analysis, normalization
and expression quantitation, differential expression analysis,
class discovery, prediction, cell lineage inference, integration of biological annotation
metadata (e.g., Gene Ontology (GO) annotation).
Statistical computing. I am also interested in statistical
computing and, in particular, reproducible research. I am 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.
Curriculum Vitae and Biography
Curriculum Vitae [PDF]