Gallantries Grant - Intellectual Output 2 - Large-scale data analysis, and introduction to visualisation and data modelling

Comment: What is a Learning Pathway?

We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.

This Learning Pathway collects the results of Intellectual Output 2 in the Gallantries Project

Success Criteria:

• SC2.1) Large-scale data analyses and -handling. In this module, learners will gain competency in managing, organizing, and analysing large collections of datasets.
• SC2.2) Analysis of high-dimensional datasets. Real-world scientific studies often involve more complex datasets. For example, combining data from different experiments or timepoints. This more complex experimental setup translates to increased complexity in data analysis.
• SC2.3) Data visualisation. This module will cover the basics of data visualisation to aid with exploration, interpretation of complex datasets.
• SC2.4) Data modelling. This module will introduce learners to the basics data modelling techniques. This is often required for the identification of patterns in data required for e.g. classification.
• SC2.5) Machine learning. This module will also cover more advanced data modelling techniques such as machine learning.
• SC2.6) Reasoning about impact of computation on results. Many choices must be made during data analysis. This includes experimental design, choice of data analysis tools and their parameter settings, and external reference databases. Each of these choices will impact the results. Accurate interpretation of results is only possible with an understanding and awareness of the impact of these factors.

Year 1: Introduction to large-scale analyses in Galaxy

Galaxy offers support for the analysis of large collections of data. This submodule will cover the upload, organisation, and analysis of such large sets of data and files. [SC2.1; SC1.3,5]

Year 1: Introduction to the human microbiome analyses

The human microbiome consists of a community of thousands of species of microorganisms. Sequencing of this community is often performed to identify which species of microorganism are present. This aids in diagnostics and treatment of patients. [SC2.1-3,6; SC1.4,5]

Year 1: Advanced microbiome analysis

By using more complex sequencing techniques, it is possible to not only obtain information about which organisms are present in the microbiome, but also their activity. This can e.g. aid in identification of antibiotic resistance. This more complex sequencing requires more complex data analysis [SC2.1-4,6; SC1.4,5]

Year 2: Cancer Analysis

The previous submodules focused on scaling up in terms of number of samples. This submodule will focus on scaling up in terms of complexity. Cancer is a disease of the genome, it is a multifaceted and heterogeneous disease. This leads to complex datasets and analysis pipelines [SC2.3,4; SC1.5]

Year 2: Intro to machine learning

Going beyond conventional statistics, many scientific data analyses benefit from machine learning techniques for modelling of datasets. This is widely used in biomedical domain. [SC2.4,5; SC1.4]

Year 2: Introduction to the Galaxy visualisation framework

(This module was cancelled due to insufficiencies in the Galaxy Visualisation Framework.) Galaxy has many options for visualisation of scientific data. This module will cover how to use this framework to create and share visualisation. [SC2.2-3; SC1.1,3,6]

Year 3: Visualisation of complex multidimensional data

For advanced visualisation, tools such as Circos may be utilized where Galaxy’s basic visualisation framework does not suffice. [SC2.2-3; SC1.5]

Year 3: Introduction to Visualisation with R and Python

When the available visualisation options do not suffice, custom plots and visualisations can be created using one of several extensive visualisation libraries available in R and Python. This module will cover the basics of using R and Python to create custom plots and visualisations. [SC2.3; SC1.1]