1 Introduction

This document explains the functionalities available in the a4Classif package.

This package contains for classification of Affymetrix microarray data, stored in an ExpressionSet. This package integrates within the Automated Affymetrix Array Analysis suite of packages.

## Loading required package: a4Core
## Loading required package: a4Preproc
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## a4Classif version 1.38.0
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To demonstrate the functionalities of the package, the ALL dataset is used. The genes are annotated thanks to the addGeneInfo utility function of the a4Preproc package.

data(ALL, package = "ALL")
ALL <- addGeneInfo(ALL)
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ALL$BTtype <- as.factor(substr(ALL$BT,0,1))

2 Classify microarray data

2.1 Lasso regression

resultLasso <- lassoClass(object = ALL, groups = "BTtype")
plot(resultLasso, 
    label = TRUE, 
    main = "Lasso coefficients in relation to degree of penalization."
)

topTable(resultLasso, n = 15)
## The lasso selected 16 genes. The top 15 genes are:
## 
##             Gene Coefficient
## 38319_at    CD3D  0.95966733
## 35016_at    CD74 -0.60928095
## 38147_at  SH2D1A  0.49240967
## 35792_at    MGLL  0.46856925
## 37563_at  SRGAP3  0.26648240
## 38917_at  YME1L1  0.25100075
## 40278_at    GGA2 -0.25017550
## 41164_at    IGHM -0.12387272
## 41409_at THEMIS2 -0.10581122
## 38242_at    BLNK -0.10309606
## 35523_at   HPGDS  0.10169706
## 38949_at   PRKCQ  0.07832802
## 33316_at     TOX  0.06963509
## 33839_at   ITPR2  0.05801832
## 40570_at   FOXO1 -0.04858863

2.2 PAM regression

resultPam <- pamClass(object = ALL, groups = "BTtype")
plot(resultPam, 
    main = "Pam misclassification error versus number of genes."
)