Vignette of the a4Classif package
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##
## a4Classif version 1.61.0## Loading required package: Biobase## Loading required package: BiocGenerics## Loading required package: generics##
## Attaching package: 'generics'## The following objects are masked from 'package:base':
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## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
## setequal, union##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs## The following object is masked from 'package:utils':
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## data## The following objects are masked from 'package:base':
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## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, saveRDS, scale, sequence, table,
## tapply, transform, 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.
## Loading required package: hgu95av2.db## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: IRanges## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:utils':
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## findMatches## The following objects are masked from 'package:base':
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## expand.grid, I, unname## Loading required package: org.Hs.eg.db## ## Classify microarray data
Lasso regression
resultLasso <- lassoClass(object = ALL, groups = "BTtype")
plot(resultLasso,
label = TRUE,
main = "Lasso coefficients in relation to degree of penalization."
)
## 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 -0.04858863PAM regression
resultPam <- pamClass(object = ALL, groups = "BTtype")
plot(resultPam,
main = "Pam misclassification error versus number of genes."
)
## 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 -0.1693 0.4875 1 1## predicted
## true B T
## B 95 0
## T 1 32Random forest
# 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)
## Random forest selected 11 genes. The top 15 genes are:
##
## GeneSymbol
## 160_at STAM
## 1792_g_at CDK2
## 32587_at ZFP36L2
## 34685_at PYROXD1
## 35084_at AMH
## 36028_at TCIRG1
## 37190_at WASF1
## 38067_at SEPTIN8
## 39706_at CPNE3
## 40000_f_at VPS13A
## 40960_at B4GALT1
Appendix
Session information
## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] hgu95av2.db_3.13.0 org.Hs.eg.db_3.23.1 AnnotationDbi_1.75.0 IRanges_2.47.2 S4Vectors_0.51.5 ALL_1.55.0 Biobase_2.73.1 BiocGenerics_0.59.8 generics_0.1.4 a4Classif_1.61.0 a4Preproc_1.61.0 a4Core_1.61.0 rmarkdown_2.31
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.10 varSelRF_0.7-9 shape_1.4.6.1 RSQLite_3.53.3 lattice_0.22-9 digest_0.6.39 evaluate_1.0.5 grid_4.6.1 iterators_1.0.14 fastmap_1.2.0 blob_1.3.0 foreach_1.5.2 jsonlite_2.0.0 glmnet_5.0 Matrix_1.7-5 DBI_1.3.0 survival_3.8-6 httr_1.4.8 Biostrings_2.81.3 codetools_0.2-20 jquerylib_0.1.4 cli_3.6.6 crayon_1.5.3
## [24] rlang_1.2.0 XVector_0.53.0 pamr_1.57 bit64_4.8.2 splines_4.6.1 cachem_1.1.0 yaml_2.3.12 tools_4.6.1 parallel_4.6.1 memoise_2.0.1 ROCR_1.0-12 png_0.1-9 buildtools_1.0.0 vctrs_0.7.3 R6_2.6.1 lifecycle_1.0.5 Seqinfo_1.3.0 KEGGREST_1.53.4 randomForest_4.7-1.2 bit_4.6.0 cluster_2.1.8.2 pkgconfig_2.0.3 bslib_0.11.0
## [47] Rcpp_1.1.1-1.1 xfun_0.59 sys_3.4.3 knitr_1.51 htmltools_0.5.9 maketools_1.3.2 compiler_4.6.1