TABLE OF CONTENTS

• Introduction
• 1 Algorithm settings:
  • 1.1 Creating a plot
  • 1.2 Selecting data
  • 1.3 Setting paramaters
    • 1.3.1 Gene set
    • 1.3.2Parameters
• 2 Performing GSVA
• 3 Using engineered data matrix as input
  • 3.1 Dimension reduction 
  • 3.2 Marker set analysis
• 4 Video tutorial
• 5 Useful links

Introduction

Feature engineering is the process of crafting new features from the existing features. These new features can be created through domain knowledge, purely algorithmically, or with a mix of both. For example, principal component analysis (PCA) creates a reduction by computing components, which can be seen as new features of the data. PCA creates these components by trying to explain the most amount of variation in the data, hence these components are created purely algorithmically. In biological data science, it would be useful to integrate the algorithmic approach with domain knowledge, to get even more insight from the data. For example, pathways are a series of interactions among molecules in a cell that leads to a certain product or a change in that cell. By using domain knowledge, we know that certain features (enzymes) belong to a certain pathway. By creating these pathway sets, we could extract the overall activity of the pathway from the original features themselves, which means converting your features x observations data matrix (e.g. genes x cells) in a sets x observations data matrix (e.g. pathways x cells). Then we could use these newly created engineered data to perform the analyses present in the UniApp.

The UniApp enables you to perform the set variation analysis (GSVA), which converts domain knowledge (the sets you define, like pathways) to an analysis-ready data matrix by assessing the relative enrichment of sets across observations using a non-parametric approach. You can also upload any custom engineered data generated with any other method, as long as the observation names of the custom engineered data correspond to the ones of the original data.

1 Algorithm settings:

1.1 Creating a plot

As a first step of the analysis, a plot must be created by clicking on the create plot icon. This will lead you to a section where the analysis of interest (in this case gene set variation analysis) can be selected.

The analysis can be effectively organised by assigning a name and description in the respective fields. Subsequently under the "choose algorithm to run your analysis", gene set variation must be selected.

1.2 Selecting data

Next the input analysis can be selected into the track element under "choose track element". In the cell selection tab you can choose the observations to use as input. For more information see the section on Cell/sample selection. Note that subsetting at the pretreatment step is a "hard" subset meaning that excluded cells/samples at this step will not be present in the downstream steps.

1.3 Setting paramaters

In the set parameters field you will be able to define how to perform the gene set variation analysis.