Keywords

Learning outcomes

After the course, the student can:
- compare sequencing and microarray technologies used in high-throughput analysis and choose suitable ones for the analysis required.
- explain the principles of measurement technologies covered and how various inherent errors and biases of the measurement techniques affect the analysis.
- take raw data from high-throughput experiments and preprocess and normalize the data for analysis if needed using standard tools.
- apply common methods and algorithms, including state-of-the-art, to extract information from high-throughput measurement data, particularly in the context of RNA-seq and ChIP-seq data.
- discuss the statistical principles underlying the data analysis methods above and identify the benefits and weaknesses of each method.
- select suitable algorithms for the analysis and justify the choice.
- build data analysis pipelines for microarray and sequencing data analysis.

Contents

- High-throughput sequencing and microarray technologies
- Short read alignment, including Burrows-Wheeler and indexing
- Advanced clustering, including k-means, consensus clustering and biclustering
- Statistical testing for high-throughput data
- Gene set enrichment analysis
- Feature detection
- Multiple testing problem, including False discovery rate (FDR) estimation

Evaluation

Belongs to following study modules

2017–2018

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

BIO4450 High-throughput data analysis