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 1.54.0
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## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table,
## tapply, union, unique, unsplit, which.max, which.min
## Welcome to Bioconductor
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## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
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)
## Loading required package: hgu95av2.db
## Loading required package: AnnotationDbi
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## findMatches
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ALL$BTtype <- as.factor(substr(ALL$BT,0,1))
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
resultPam <- pamClass(object = ALL, groups = "BTtype")
plot(resultPam,
main = "Pam misclassification error versus number of genes."
)
topTable(resultPam, n = 15)
## Pam selected 1 genes. The top 15 genes are:
##
## GeneSymbol B.score T.score av.rank.in.CV prop.selected.in.CV
## 38319_at CD3D -0.127 0.3656 1 1
confusionMatrix(resultPam)
## predicted
## true B T
## B 95 0
## T 11 22
# select only a subset of the data for computation time reason
ALLSubset <- ALL[sample.int(n = nrow(ALL), size = 100, replace = FALSE), ]
resultRf <- rfClass(object = ALLSubset, groups = "BTtype")
plot(resultRf)
topTable(resultRf, n = 15)
## Random forest selected 4 genes. The top 15 genes are:
##
## GeneSymbol
## 36109_at PEPD
## 38385_at DSTN
## 39350_at GPC3
## 39781_at IGFBP4
ROCcurve(gene = "ABL1", object = ALL, groups = "BTtype")
## Warning in ROCcurve(gene = "ABL1", object = ALL, groups = "BTtype"): Gene ABL1 corresponds to 6 probesets; only the first probeset ( 1635_at ) has been displayed on the plot.
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] hgu95av2.db_3.13.0 org.Hs.eg.db_3.20.0 AnnotationDbi_1.68.0 IRanges_2.40.0 S4Vectors_0.44.0 ALL_1.47.0 Biobase_2.66.0 BiocGenerics_0.52.0 a4Classif_1.54.0 a4Preproc_1.54.0 a4Core_1.54.0
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