[English workshop]: Dimension Reduction for Beginners: Hitchhiker’s Guide to Matrix Factorization and PCA

Abstract

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.

Language used in the workshop

English

Convenient day for your Long workshop.

@aedin Please let us know your preference

Contact Details

aedin@ds.dfci.harvard.edu

Comment field

Submitted on behalf of Aedin Culhane (@aedin). Workshop will be a re-run of a workshop presented at BioC 2021 (https://aedin.github.io/PCAworkshop/)

Checklist

[X] This workshop has a Bioconductor focus or interest to Bioconductor users/developers.
[X] I checked and confirmed the BioC Asia 2021 Code of Conduct.
[X] I understood that it would be desirable for the workshop to be based on this template project.

Bioconductor / BiocAsia2021