DESCRIPTION

Title: Hitchhikers Guide to PCA: Demystifying dimensionality reduction in R/Bioconductor
Version: 0.1.0
Date: 2020-06-23
Authors@R: 
    c(person(given = "Lauren", family = "Hsu",
             role = c("aut"),
             email = "lrnshoe@gmail.com",
             comment = c(ORCID = "0000-0002-6035-7381")),
     person(given = "Aedin", family = "Culhane",
             role = c("aut", "cre"),
             email = "aedin@jimmy.harvard.edu",
             comment = c(ORCID = "0000-0002-1395-9734")))
Description: 
    This workshop will provide a beginner's guide to principal component analysis (PCA), the 
    difference between singular value decomposition, different forms of PCA and fast PCA for 
    single-cell data. We will describe how to detect artifacts and select the optimal number 
    of components. It will focus on SVD and PCA applied to 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 also discuss interpretation of outputs, as 
    well as some common pitfalls and sources of confusion in utilizing these methods.
Imports: 
    ade4,
    explor,
    factoextra,
    FactoMineR,
    GDAtools,
    ggfortify,
    ggplot2, 
    ggthemes, 
    grDevices,
    gridExtra,
    irlba, 
    MASS,
    mogsa,
    scater,
    scatterD3,
    SingleCellExperiment, 
    SummarizedExperiment
Suggests: 
    BiocStyle,
    devtools,
    knitr
License: GPL-2
Encoding: UTF-8
VignetteBuilder: knitr
URL: https://github.com/aedin/Frontiers_Supplement