Omics Integration and Systems Biology

Description

The aim of this course is to provide an integrated view of data-driven hypothesis generation through machine learning integration methods, biological graph / network analysis and genome-scale metabolic models. A general description of different methods for analyzing different omics data (e.g. transcriptomics and genomics) will be presented with some of the lectures discussing key methods and pitfalls in their integration.

Topics covered

• Data pre-processing and cleaning prior to integration;
  

• Application of key machine learning methods for multi-omics analysis including deep learning;
  

• Multi-omics factor analysis,  dimension reduction and clustering;
  

• Biological network inference, community and topology analysis and visualization;
  

• Condition-specific and personalized modeling through Genome-scale Metabolic models for integration of transcriptomic, proteomic, metabolomic and fluxomic data;
  

• Identification of key biological functions and pathways;
  

• Identification of potential biomarkers and targetable genes through modeling and biological network analysis;
  

• Application of network approaches in meta-analyses;
  

• Similarity network fusion and matrix factorization techniques;
  

• Integrated data visualization techniques
  

Learning outcomes

At the end of the course, students should:

• Identify key methods for analysis and integration of omics data based on a given dataset;
  

• Perform standard feature selection reduction techniques;
  

• Understand the differences and apply dimension reduction techniques;
  

• Understand strengths and pitfalls of key machine learning techniques in multi-omic analysis;
  

• Apply unsupervised and supervised data integration techniques;
  

• Build biological networks based on different omics data including integrated multi-omics networks;
  

• Perform centrality and community analyses in graphs;
  

• Apply network approaches in meta-analyses;
  

• Apply similarity network fusion of patient data;
  

• Compare different cell-types or conditions through the application of different biological network analysis techniques;
  

• Simulate biological functions using constraint-based models and flux balance analysis;
  

• Identify potential confounding factors and sources of bias.
  

Pre-requisites

Practical exercises can be performed using R or Python, so we only accept students with previous experience in one of those programming languages. We will not discuss how to process specific omics, and the students are referred to other NBIS courses for this matter.

Required:

• Basic knowledge in R or Python;
  

• Basic understanding of frequentist statistics;
  

• A computer with web camera and Zoom.
  

Desirable:

• Experience with NGS and omics analysis
  

• Completing "Introduction to bioinformatics using NGS data" and "Introduction to biostatistics and machine learning" NBIS courses
  

Level

beginner