Hubert Rehrauer: Catalogue data in Autumn Semester 2016 |
| Name | Dr. Hubert Rehrauer |
| Address | Functional Genomics Center Zürich ETH Zürich, Y32 H 66 Winterthurerstrasse 190 8057 Zürich SWITZERLAND |
| Telephone | 044 635 39 24 |
| hubert.rehrauer@fgcz.ethz.ch | |
| Department | Mathematics |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 401-6282-00L | Statistical 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 credits | 3G | H. Rehrauer, M. Robinson | |
| Abstract | A 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. | ||||
| Learning 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 | ||||
| Content | Lectures 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 notes | Lecture notes, published manuscripts | ||||
| Prerequisites / Notice | Prerequisites: 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 | ||||