Sandrine Dudoit, PhD
Professor, Division of Biostatistics, School
of Public Health, and Department of Statistics
Chair and Head Graduate Advisor, Graduate Group in Biostatistics
University of California,
101 Haviland Hall, #7358
Berkeley, CA 94720-7358
Tel: (510) 643-1108
Fax: (510) 643-5163
PB HLTH 295 -- Statistics and Genomics Seminar
Spring 2017 schedule [HTML]
PB HLTH C240F/STAT C245F -- Statistical Genomics II
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]
Remove Unwanted Variation from RNA-Seq Data.
D. Risso and S. Dudoit.
Bioconductor R package: [Current release version]
Single Cell Overview of Normalized Expression data.
M. Cole and D. Rissso.
R package version 0.99.0, works with R version 3.3 and
Bioconductor version 3.4: [GitHub]
D. Risso, J. Ngai, T. P. Speed, and S. Dudoit.
The role of spike-in standards in the normalization of RNA-seq.
In S. Datta and D. Nettleton, editors, Statistical Analysis of Next Generation Sequencing Data, Frontiers in Probability and the Statistical Sciences, Chapter 9, pages 169-190. Springer International Publishing, 2014.
D. Risso, J. Ngai, T. P. Speed, and S. Dudoit. Normalization of RNA-seq data using factor analysis of control genes or samples. Nature Biotechnology, 32(9): 896-902, 2014.
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., RNA-Seq for transcriptome
analysis and genome annotation and ChIP-Seq for DNA-protein
interaction profiling (e.g., transcription factor binding). My
contributions include: exploratory data analysis, normalization
and expression quantitation, differential expression analysis,
class discovery, prediction, 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]
Berkeley Institute for Data Science, Senior Fellow
Berkeley Stem Cell
California Institute for
Quantitative Biosciences (QB3)
Center for Computational
Biology (CCB), UC Berkeley
Graduate Group in
Biostatistics, UC Berkeley
Group in Computational and Data Science and Engineering (CDSE), UC
Groupin Computational and Genomic Biology, UC Berkeley
Bioinformatics and Molecular Biostatistics (CBMB), UC San Francisco