Mark Robinson: Catalogue data in Autumn Semester 2021
| Prof. Dr. Mark Robinson
(Professor Universität Zürich (UZH))
Inst. Molecular Life Sciences
|044 635 48 48
|ZüKoSt: Seminar on Applied Statistics
|M. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, M. Robinson, C. Strobl, S. van de Geer
|About 5 talks on applied statistics.
|See how statistical methods are applied in practice.
|There will be about 5 talks on how statistical methods are applied in practice.
|Prerequisites / Notice
|This is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web:
Course language is English or German and may depend on the speaker.
|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 as an incoming student.
UZH Module Code: STA426
Mind the enrolment deadlines at UZH:
|H. Rehrauer, M. Robinson
|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.
|-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
|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, 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