Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Hubert Rehrauer: Catalogue data in Autumn Semester 2015

Name Dr. Hubert Rehrauer
Address
Functional Genomics Center Zürich
ETH Zürich, Y32 H 66
Winterthurerstrasse 190
8057 Zürich
SWITZERLAND
Telephone044 635 39 24
E-mailhubert.rehrauer@fgcz.ethz.ch
DepartmentMathematics
RelationshipLecturer

NumberTitleECTSHoursLecturers
401-6282-00LStatistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: STA426

Mind the enrolment deadlines at UZH:
http://www.uzh.ch/studies/application/mobilitaet_en.html
5 credits3GH. Rehrauer, M. Robinson
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