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Statistical Data Analysis for Genome Scale Biology

Statistical Data Analysis for Genome Scale Biology

Last Revised:
18. July 2019
Code:
2019-CSAMA
Location:
Bressanone-Brixen, Italy
Group:
Not Specified
Level:
Not Specified

Description

Contents:
The one-week intensive course Statistics and Computing in Genome Data Science teaches statistical and computational analysis of multi-omics studies in biology and biomedicine. It covers the underlying theory and state of the art (the morning lectures), and practical hands-on exercises based on the R / Bioconductor environment (the afternoon labs). The course covers the primary analysis of high-throughput sequencing based assays in functional genomics and integrative methods including efficiently operating with genomic intervals, statistical testing, linear models, machine learning, bioinformatic annotation and visualization.

Topics:
•    Introduction to Bioconductor
•    Elements of statistics: hypothesis testing, multiple testing, regression, regularization, clustering and classification (machine learning), visualization
•    Computing with sequences and genomic intervals
•    Integrating multiple layers of ‘omic data
•    Working with annotation – genes, genomic features and variants
•    RNA-Seq data analysis and differential expression
•    Single-cell RNA-Seq
•    Proteomics primer
•    Interactive data visualization using Shiny


Course Structure:
The course consists of
Morning lectures: 20 x 45 minutes: Monday to Friday 8:30 – 12:00
Practical computer tutorials in the afternoons (14:00 – 17:00) on Monday, Tuesday, Thursday and Friday

Learning goals:
At the end of the course, participants should be able to run analysis workflows on their own (multi-)omic data, adapt and combine different tools, and make informed and scientifically sound choices about analysis strategies.

Prerequisites:
The course is intended for researchers who have basic familiarity with the experimental technologies and their applications in biology, and who are interested in making the step from a user of bioinformatics software towards adapting or developing their own analysis workflows. The four practical sessions of the course will require you to follow and modify scripts in the computer language R.


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