Introduction to Multivariate Analysis of Gene Expression Data using MADE4

Aedin Culhane1

1Department of Data Science, Dana-Farber Cancer Institute, Department of Biostatistics, Harvard TH Chan School of Public Health

Abstract

Vignette introduction function in the made4 package. This package is old and was originally designed for the analysis of microarray data. However most of the functions, such as bga, cia etc can be applied to any ’omics data. Some of these functions are avaialble in omicade4, mogsa and corral, and I am migrating this package into Bioconductor compliant package ;-) If you have found functions in made4 useful, and would like to assist, in updating it, please let me know. I’d be delighted.

Package

BiocStyle 2.30.0

1 Introduction

library(ade4)
library(made4)

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library(scatterplot3d)

The package made4 facilitates multivariate analysis of microarray gene expression data. The package provides a set of functions that utilise and extend multivariate statistical and graphical functions available in ade4, (Thioulouse et al. 1997) .
made4 accepts gene expression data is a variety of input formats, including Bioconductor formats, - SummarizedExperiment - ExpressionSet - data.frame - matrix and older microarray formats; ExpressionSet, marrayRaw

2 Installation

made4 requires the R package ade4. It should be installed automatically when you install made4. To install made4 from bioconductor

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("made4")

3 Further help

The package made4 is described in more detail in the RNews newletter, December 2006.

Culhane AC and Thioulouse J. (2006) A multivariate approach to integrating datasets using made4 and ade4.R News, 6(5) 54-58. pdf

Extensive tutorials, examples and documentation on multivariate statistical methods are available from [the ade4 website] (http://pbil.univ-lyon1.fr/ADE-4) and ade4 user support
is available through the ADE4 mailing list.

This tutorial assumes a basic knowledge of R, but we have found that Emmanuel Paradis’s R for Beginners is a very good guide to those unfamiliar with R. This is [available at] (http://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf).

This documents assumes that data is normalized and preprocessed.

4 Citing

We are delighted if you use this package. Please do email us if you find a bug or have a suggestion. We would be very grateful if you could cite:

Culhane AC, Thioulouse J, Perriere G, Higgins DG.(2005) MADE4: an R package for multivariate analysis of gene expression data. Bioinformatics 21(11): 2789-90.

5 Quickstart

We will very briefly demonstrate some of the functions in made4. To do this we will use a small dataset that is available in made4. This dataset Khan contains gene expression profiles of four types of small round blue cell tumours of childhood (SRBCT) published by Khan et al. (2001). This is a subset of the published dataset. It contains gene expression levels for 306 genes for 64 patient samples. Load the necessary R packages and dataset.

 library(made4)
 library(ade4)
 data(khan)

This experiment studied gene expression in patient with four types of SRBCT. These were neuroblastoma (NB), rhabdomyosarcoma (RMS), Burkitt lymphoma, a subset of non-Hodgkin lymphoma (BL), and the Ewing family of tumours (EWS). Gene expression profiles from both tumour biopsy and cell line samples were obtained and are contained in this dataset. In this study data were divided into a training set of 64 samples, and a blind test dataset. These 2 dataset are called khan$train and khan$test. Have a look at the data. For this example we will just example the training dataset.

names(khan)

## [1] "train"                "test"                 "train.classes"       
## [4] "test.classes"         "annotation"           "gene.labels.imagesID"
## [7] "cellType"

k.data<-khan$train
k.class<-khan$train.classes

5.1 Overview

The made4 function overview() provides a quick way to get an overview or feel for data. overview() will draw a boxplot, histogram and dendrogram of a hierarchical analysis. Hierarchical clustering is produced using average linkage clustering with a Pearson correlation measure of similarity (Eisen et al. 1998) This gives a quick first glance at the data.

overview(k.data)

Often its useful to label the samples using a class vector or covariate of interest, in this case, the tumour type (EWS, BL, NB or RMS).

overview(k.data, labels=k.class)

Figure 1: Overview of Khan data

Figure 1: A) dendrogram showing results of average linkage clustering, B) boxplot and C) histrogram.

Often one will known classes in the data (eg Normal v Treatment, or different tumor types). We can insert a class colourbar under the dendrogram, and colour the boxplot.

overview(k.data, classvec=k.class, labels=k.class)