Sandrine Dudoit
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,
Berkeley
101 Haviland Hall, #7358
Berkeley, CA 947207358
Tel: (510) 6431108
Fax: (510) 6435163
Email: sandrine@stat.berkeley.edu
PB HLTH 295  Statistics and Genomics Seminar
Fall 2016 schedule [HTML]
PB HLTH C240C/STAT C245C 
Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine I
Conferences

Keynote Presentation, Intelligent Systems for Molecular Biology (ISMB), Orlando, FL, July 11, 2016.
Using SingleCell Transcriptome Sequencing to Infer Olfactory Stem Cell Fate Trajectories.
Slides: [PDF]

Workshop, BioC 2016: Where Software and Biology Connect, Stanford, CA, June 25, 2016.
Analysis of SingleCell RNASeq Data with R and Bioconductor.
R package bioc2016singlecell: [GitHub]
Software
Publications

D. Risso, J. Ngai, T. P. Speed, and S. Dudoit.
The role of spikein standards in the normalization of RNAseq.
In S. Datta and D. Nettleton, editors, Statistical Analysis of Next Generation Sequencing Data, Frontiers in Probability and the Statistical Sciences, Chapter 9, pages 169190. Springer International Publishing, 2014.
[PDF]
[HTML]

D. Risso, J. Ngai, T. P. Speed, and S. Dudoit. Normalization of RNAseq data using factor analysis of control genes or samples. Nature Biotechnology, 32(9): 896902, 2014.
[HTML].
Employment Opportunities
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 highdimensional inference and include
exploratory data analysis (EDA), visualization, lossbased
estimation with crossvalidation (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 highthroughput microarray and sequencing
gene expression experiments, e.g., RNASeq for transcriptome
analysis and genome annotation and ChIPSeq for DNAprotein
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 opensource and opendevelopment software project for the analysis of biomedical and genomic data.
Curriculum Vitae and Biography
Curriculum Vitae: [PDF]
Biography:
[PDF]
[Word]
Affiliations
Berkeley Institute for Data Science, Senior Fellow
Berkeley Stem Cell
Center
Bioconductor Project
California Institute for
Quantitative Biosciences (QB3)
Center for Computational
Biology (CCB), UC Berkeley
Graduate Group in
Biostatistics, UC Berkeley
Graduate
Group in Computational and Data Science and Engineering (CDSE), UC
Berkeley
Graduate
Groupin Computational and Genomic Biology, UC Berkeley
Center for
Bioinformatics and Molecular Biostatistics (CBMB), UC San Francisco