A. Introduction
B. Omics-based biomarkers detection
C. Classification
- - C.1. Unsupervised classification
  - C.2. Supervised classification or predictors
D. Visualization of omics data
E. Genomics
F. Functional profiling
G. In silico approaches combining omic studies
H. Single-cell analysis
I. Whole worked examples
- - Gene expression microarrays analysis
J. References and links

A. Introduction

B. Omics-based biomarkers detection

Omics-based biomarkers detection
Tool: Babelomics (http://babelomics.bioinfo.cipf.es/ from Firefox browser)
Exercises
1. Metabolomics, Proteomics, Transcriptomics from arrays,…
2. RNA-Seq, microRNA-Seq,…

C. Classification

C.1. Unsupervised classification

How can we detect groups of patients with similar expression profile? What microRNAs or genes have a common intensity pattern for an experimental group? Could we explore our data before continuing the analysis?

C.2. Supervised classification or predictors

How do we predict or classify patients with different stages of a disease according to their level of expression?

D. Visualization of omics data

Presentation
Tools: BoxPlotR (http://boxplot.tyerslab.com), Heatmapper (http://heatmapper.ca)
Exercises

E. Genomics

General overview of the sessions. Studies of genomic variation. Prioritization of variants and genes. Presentation.
CSVS (CIBERER Spanish Variant Server). This database provides information about the variability of the Spanish population. It is very useful for filtering polymorphisms and local variations when prioritizing candidate disease genes. CSVS exercises.
BiERapp. Interactive web application for assisting in gene prioritization in Whole-Exome Sequencing studies. BiERapp exercises.
TEAM (Targeted Enrichment Analysis and Management). Management of panels of genes for targeted enrichment and massive sequencing for diagnostic applications. How to design panels of genes. TEAM exercises.
Prioritization of genes. How to prioritize from Endeavour?. Exercises

F. Functional profiling

F.1.Over-representation and GSA methods

Presentation
Tools:
- PANTHER: Classification System
- g:Profiler
- Babelomics (http://courses.babelomics.org/ from Firefox browser)
- Functional profiling for microRNAs: omiRas, miRNet, mirPath v.3.
Exercises

F.2. Protein-protein interaction

F.3. Signaling pathways analysis

hiPathia. High-throughput pathway inference analysis. hipathia_jan2019.pdf
PathAct. Actionable pathway workshop. pathact_2018jan.pdf

G. In silico approaches combining omic studies

Fortunately there are many studies in public respositories where is possible to select, download and process its omics data. The combination of information for all selected studies could be an interesting idea to detect a global effect along of similar studies (same topic, experimental design…). These sessions will help us to get familiar with several resources really useful to analyze these data.

GEO (Gene Expression Omnibus)
ImaGEO: Integrative Gene Expression Meta-Analysis from GEO database
NetworkAnalyst
MIO: miRNA target analysis system for Immuno-Oncology. A web tool where the he integration of several machine learning methods enable the selection of prognostic and predictive microRNAs and gene interaction network biomarkers.

H. Single-cell analysis

I. Whole worked examples

Gene expression microarrays analysis

Approach 1: Processing & Differential expression from Babelomics + Functional profiling from PANTHER & STRING.
Approach 2: Processing & Differential expression from GEO2R + Functional profiling from PANTHER & STRING.

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