Finnish IT Center for Science (CSC) provides cloud computing services
Justus Reunanen, docent, University of Oulu
Anna Kaisanlahti, doctoral researcher, University of Oulu
Leo Lahti, associate prof. in data science
Pande Putu Erawijantari, postdoc
Tuomas Borman, doctoral researcher
Giulio Benedetti, scientific programmer
Department of Computing, Uni. Turku, Finland datascience.utu.fi
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Focus on multi-omic data integration, with emphasis on microbiome research
Advanced MSc, PhD & postdoctoral researchers who wish to learn new skills in scientific programming and multi-omic data analysis
Earlier experience with R is expected
Introduction to multi-omic data integration and analysis with R/Bioconductor, a popular open-source environment for life science informatics.
After the course, you will know how to:
organize multiple data sources into a coherent data science framework
implement open & reproducible data science workflows
approach common data analysis tasks by utilizing available documentation and R tools
Primary focus: microbiome research; methods are generally applicable to transcriptomics, metabolomics, single cell sequencing and other omics integration.
Day 1: open data science framework
Day 2: tabular data analysis (single omics)
Day 3: multi-table data analysis (multi-omics)
Lectures, demonstrations don’t hesitate to ask questions!
Practicals Solve tasks by taking advantage of the online examples and resources that are pointed out in the material. There is often more than one way to solve a given task.
Additional exercises & example data sets, supporting online material, many ways to solve a given task
Presentations, present your solutions, highlight questions and challenges, engage the audience
Teaching follows the open online book (beta version) created by the course teachers, Orchestrating Microbiome Analysis.
The openly licensed teaching material, exercises and slides will be available online during and after the course.
If you need a small break, take it!
Development work has received support from several sources.
Broader context and practical skills on:
Open data science workflow: setting up reproducible data science workflows with Quarto
Data containers: understanding SummarizedExperiment
Basic data wrangling (e.g. subsetting, aggregation)