TABLE OF CONTENTS

• Introduction
• 1. Algorithm settings
  • 1.1 Creating a plot
  • 1.2 Selecting data
  • 1.3 Setting parameters
    • 1.3.1 Gene set
    • 1.3.2 Parameters
    • 1.3.3 Columns to include
  • 1.4 Running the gene enrichment analysis
• 2. Interactive plot page of the gene set enrichment analysis 
  • 2.1 Ranks
  • 2.2 Statistics
  • 2.3 Bar plot
  • 2.4 Waterfall plot
• 3 Useful links
• Gene set enrichment analysis (GSEA)

Introduction

Analyzing large datasets on a feature-by-feature basis makes it difficult to discern global patterns. For example, metabolism can be divided in well-characterized pathways: a powerful approach to study those pathways is to determine whether transcripts, proteins or metabolites in a metabolic pathway are deregulated as a group. Such insight can be derived using the enrichment analysis. The analysis is not restricted to pathways: it can be applied to any possible defined set.

The UniApp uses GSEA (Gene Set Enrichment Analysis) to perform the competitive set enrichment analysis. GSEA is a computational method that determines whether an a priori defined set of features shows statistically significant differences between two biological states (e.g. phenotypes). This type of enrichment analysis is called competitive because when each set is analysed, all the features in the data are considered, not only the features present in that particular set.

The gene set enrichment analysis can be performed using any sort of ranked gene list as input. In the UniApp ranked list of genes can be created by using the differential gene expression analysis, marker genes, gene expression entropy, network analysis and rank-based meta-analysis. 

Additionaly, when using inputs that have multiple ranked-gene lists in arranged in a table like from the cluster marker genes or network analysis you can select which column to include in the Select columns to include tab. 

1.3.1 Gene set

In the gene set tab you can select which gene sets to usein your set enrichment analysis. Currently the UniApp supports gene sets hosted on MsigDB and KEGG that  are annotated by domain experts. Additionally you can also provide a your own, custom gene set. 

• Gene set library: select gene set or sets to use in the set enrichment analysis. Currently you can choose between sets from MsigDB, KEGG or ask a Unicle member for the possibility to upload a custom set. 
• Gene set: select a subset of from the previously selected set. 

1.3.2 Parameters

In the parameters tab you can specify parameters for your set enrichment analysis. 

• Minimum set size:  it defines the minimal set size. The sets which size is below this threshold will be ignored. This is important since the normalized score computed by the algorithm is not very accurate for extremely small sets.
• Maximum set size:  it defines the maximal set size.
• Permutations: the number of permutations to perform to assess the statistical significance of the enrichment score. The higher it is, the more accurate the p-values will be. 
• Random seed: since the statistical significance is assessed by a permutation test, the same random seed must be used to reproduce the same results using the same parameters.

If you have selected a marker genes list as input for the gene set enrichment analysis an additional parameter called "Rank table to use" will be displayed. Here you can can choose which marker gene rank table will be used. See here.

1.3.3 Columns to include

In the columns to include tab you can choose which columns to include in the gene set enrichment analysis in case you have selected marker genes or network analysis as input.

1.4 Running the gene enrichment analysis

When the parameters are all set-up, you can click on the Run icon on the top right to compute the gene set enrichment analysis results.

2. Interactive plot page of the gene set enrichment analysis 

The results of the GSEA can be explored with four different ways  in the interactive plot page. These are ranks, statistics, bar plot and waterfall plot.

2.1 Ranks

When clicking on the Ranks button a table will be displayed showing gene sets ranked by NES from highest to lowest. In case you performed the GSEA on multiple columns there will be a resulting ranked gene set column for each of the input columns.