Network Analysis of High Dimensional Biological Data - 15th July 2019

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Date:  15th July 2019

Time: 9am - 5pm

Price: £45

Description: This one day practical course is designed to introduce participants to the joys of networks, focusing on their application to biological research and in particular, their use in the analysis of complex data.
The course will start by introducing the principles of networks and their use as a generic medium to understand the relationships between entities. It will then introduce participants to a new analysis platform, Graphia, going through the basics of its functionality for network visualisation and navigation, and demonstrating the tool‘s use in the exploration of large networks of protein interaction data and phylogenetic trees.
The course will then outline the principles of correlation networks, a powerful approach to the analysis of any data matrix, such as those generated by RNA-seq, single cell, proteomics, metaboloics analyses etc. You will be shown how this approach allows you to explore data structure, discovering patterns and associations within data, and how you can use the software to rapidly extract meaning from large and complex datasets. The final session of the day will allow you explore your own data under the guidance of the tutors.

Instructor: Prof Tom Freeman

Location: Minilab 1, Chancellor's Building, Little France (BioQuarter) Campus, Edinburgh, EH16 4SB

Who should attend: Graduates, postgraduates, and PIs, who work with large data matrices such as those generated by RNA-seq, single cell, proteomics, metaboloics analyses etc.

Course structure: This one day course will consist of hands-on demonstrations, talks, and exercises. 

Session 1.  
       (Talk 1) Introduction to the course: overview of aims and objectives, course content. 

Concepts and terminolgy of network analysis.
       (Practical 1) Introduction to Graphia: data input types, navigation, overview of tool‘s main and functionality.

Session 2.
       (Talk 2) Introduction to correlation network analysis – principles and considerations.  
       (Practical 2) Graphia practical: Preparation of input files, use of correlation values, graph structure, clustering and data handling.

Session 3.
    • (Practical 3) A worked example: go through the analysis of an RNA-seq dataset analysis from data input through clustering, enrichment analysis export of images, profiles and lists.
    • (Talk 3) Application of network correlation analysis to other data types: single cell, microbiome, genome diveristy, etc. 

Session 4.
    • (Practical 4) Data surgery: Opportunity of participants to examine data from their own lab or data from another lab of interest to them.