Dimension Reduction for Beginners: Hitchhiker's Guide to Matrix Factorization and PCA
Channel: Bioconductor
Date: 2021-12-02

Dimension Reduction for Beginners: Hitchhiker’s Guide to Matrix Factorization and PCA

Aedin Culhane (University of Limerick, Ireland)

4:00 PM - 5:00 PM JST (Japan Standard Time) on Tuesday, Nov. 2nd, 2021
ENGLISH WORKSHOP

This workshop will provide a beginner’s guide to matrix factorization, principal component analysis (PCA), the difference between singular value decomposition, different forms of PCA and fast PCA for single-cell data as well as correspondence analysis and decomposition of the Pearson Residuals. We will describe how to detect artifacts and select the optimal number of components. It will focus on SVD, PCA, COA applied toy datasets and single-cell data.
Principal component analysis (PCA) is a key step in many bioinformatics pipelines. In this interactive session we will take a deep dive into the various implementations of singular value decomposition (SVD) and principal component analysis (PCA) to clarify the relationship between these methods, and to demonstrate the equivalencies and contrasts between these methods. We will describe correspondence analysis (COA) and demonstrate how it differs from PCA. We will also discuss interpretation of outputs, as well as some common pitfalls and sources of confusion in utilizing these methods.
Moderator: Peter Hickey (Walter and Eliza Hall Institute of Medical Research)