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401-6282-00L 5 Credits DR , MSC D-INFK , D-MATH , D-BSSE

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:
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Last Updated: 2026-06-03 00:07:35

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.

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; analysis of single-cell cytometry and gene expression datasets

Resources

Lecture Notes

Lecture notes, published manuscripts

General Information

Language
English
Levels
DR , MSC
Frequency
Yearly recurring

Examination

Type
graded semester performance
Registration modalities, date and venue of this performance assessment are specified solely by the UZH.

Course Components

Type Title Time & Place Hours
lecture with exercise Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)
**Course at University of Zurich**
No time listed 3 h weekly

Offered In

  • Computational Biology and Bioinformatics Master (More information at: )
    • Core Courses (The list of core courses is a closed list - no other courses can be added in this category. The assignment of the courses to the respective subcategory cannot be changed. Students must pass at least one course in each subcategory. A total of 40 ECTS must be acquired in the core course category, including the mandatory CBB seminar.)
  • Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
  • Doctorate Mathematics (More Information at: )
    • Subject Specialisation (The list of courses eligible for doctoral students is published each semester in the newsletter of the ZGSM.)