401-6282-00L  Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)

SemesterAutumn Semester 2019
LecturersH. Rehrauer, M. Robinson
Periodicityyearly recurring course
Language of instructionEnglish
CommentNo enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: STA426

Mind the enrolment deadlines at UZH:


401-6282-00 GStatistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)
**Course at University of Zurich**
3 hrs
Mon09:00-12:00UNI ZH .
H. Rehrauer, M. Robinson

Catalogue data

AbstractA range of topics will be covered, including basic molecular biology, genomics technologies and in particular, a wide range of statistical and computational methods that have been used in the analysis of DNA microarray and high throughput sequencing experiments.
Objective-Understand the fundamental "scientific process" in the field of Statistical Bioinformatics
-Be equipped with the skills/tools to preprocess genomic data (Unix, Bioconductor, mapping, etc.) and ensure reproducible research (Sweave)
-Have a general knowledge of the types of data and biological applications encountered with microarray and sequencing data
-Have the general knowledge of the range of statistical methods that get used with microarray and sequencing data
-Gain the ability to apply statistical methods/knowledge/software to a collaborative biological project
-Gain the ability to critical assess the statistical bioinformatics literature
-Write a coherent summary of a bioinformatics problem and its solution in statistical terms
ContentLectures will include: microarray preprocessing; normalization; exploratory data analysis techniques such as clustering, PCA and multidimensional scaling; Controlling error rates of statistical tests (FPR versus FDR versus FWER); limma (linear models for microarray analysis); mapping algorithms (for RNA/ChIP-seq); RNA-seq quantification; statistical analyses for differential count data; isoform switching; epigenomics data including DNA methylation; gene set analyses; classification
Lecture notesLecture notes, published manuscripts
Prerequisites / NoticePrerequisites: Basic knowlegde of the programming language R, sufficient knowledge in statistics

Former course title: Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits5 credits
ExaminersH. Rehrauer, M. Robinson
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationRegistration modalities, date and venue of this performance assessment are specified solely by the UZH.

Learning materials

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Offered in

Computational Biology and Bioinformatics MasterCore CoursesWInformation
Computational Biology and Bioinformatics MasterData ScienceWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation