README.md

In gabrielodom/Bioc2019pathwayPCA: A workshop on the pathwayPCA package

Integrative Pathway Analysis with pathwayPCA

15 March 2019

Gabriel Odom ([e-mail])

James Ban ([e-mail])

Lily Wang ([e-mail])

Xi Chen ([e-mail])

Workshop Description

With the advance in high-throughput technology for molecular assays, multi-omics datasets have become increasingly available. In this workshop, we will demonstrate using the pathwayPCA package to perform integrative pathway-based analyses of multi-omics datasets. In particular, we will demonstrate through three case studies the capabilities of pathwayPCA to

• perform pathway analysis with gene selection,
• integrate multi-omics datasets to identify driver genes,
• estimate and visualize sample-specific pathway activities in ovarian cancer, and
• identify pathways with sex-specific effects in kidney cancer.

Pre-requisites

• Basic knowledge of R syntax
• Familiarity with RStudio
• Familiarity with pathway analysis
• Knowledge of Principal Components Analysis (PCA) is helpful but not required

Workshop Participation

This will be a hands-on workshop, students will live-code with us. It would be helpful for participants to bring a laptop with RStudio and pathwayPCA package installed (https://gabrielodom.github.io/pathwayPCA/index.html#installing-the-package).

R / Bioconductor packages used

• pathwayPCA will be covered in depth
• rWikiPathways
• tidyverse package suite (We will make use of some data constructs and ideas from these packages, but we do not expect users to be intimately familiar with Tidyverse)

Time outline

| Activity | Time | |----------------------------------------------------------------------|------| | Introduction to pathwayPCA | 10m | | Case Study 1: Estimating sample-specific pathway activities | 10m | | Case Study 2: Pathway analysis for multi-omics datasets | 10m | | Case Study 3: Analyzing experiments with covariates and interactions | 10m | | Summary and Conclusion | 5m | | Questions and Comments | 5m |

Workshop goals and objectives

Learning goals

• Describe PCA-based pathway analysis
• Apply the pathwayPCA workflow in typical gene expression and copy number variations experiments
• Perform integrative pathway-based analysis for multiple types of omics data jointly
• Apply pathwayPCA to experiments with complex designs, such as those with covariate and/or interaction effects

Learning objectives

• Identify pathways significantly associated with survival, binary, or continuous outcome using the pathwayPCA approach
• Prioritize genes most likely to be driver (instead of passenger) genes using integrative analysis
• Estimate and visualize individual gene effects within a significant pathway
• Estimate and visualize sample-specific pathway activities
• Identify pathways with significant sex-specific effects

gabrielodom/Bioc2019pathwayPCA documentation built on June 19, 2019, 7:40 p.m.