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 1.52.0
## Loading required package: Biobase
## Loading required package: BiocGenerics
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## Attaching package: 'BiocGenerics'
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## Position, rank, rbind, Reduce, rownames, sapply, 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
## Loading required package: stats4
## Loading required package: IRanges
## Loading required package: S4Vectors
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## Attaching package: 'S4Vectors'
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## findMatches
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## expand.grid, I, unname
## Loading required package: org.Hs.eg.db
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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 53 genes. The top 15 genes are:
##
## GeneSymbol B.score T.score av.rank.in.CV prop.selected.in.CV
## 38319_at CD3D -0.8044 2.3156 1 1
## 38147_at SH2D1A -0.4644 1.3369 2 1
## 33238_at LCK -0.3754 1.0808 4 1
## 35016_at CD74 0.3753 -1.0804 3.8 1
## 38095_i_at HLA-DPB1 0.3589 -1.0331 5.1 1
## 37039_at HLA-DRA 0.3536 -1.018 5.8 1
## 38096_f_at HLA-DPB1 0.3403 -0.9796 7.1 1
## 2059_s_at LCK -0.3243 0.9336 7.7 1
## 38833_at HLA-DPA1 0.2921 -0.8408 9.1 1
## 41723_s_at <NA> 0.2652 -0.7636 10.8 1
## 1110_at TRDC -0.2599 0.7481 11.7 1
## 38242_at BLNK 0.2387 -0.6871 13 1
## 1096_g_at CD19 0.2377 -0.6842 12.6 1
## 37344_at HLA-DMA 0.2303 -0.6631 13.6 1
## 39389_at CD9 0.2211 -0.6366 14 1
confusionMatrix(resultPam)
## predicted
## true B T
## B 95 0
## T 0 33
# 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 14 genes. The top 15 genes are:
##
## GeneSymbol
## 1650_g_at SMOX
## 32649_at TCF7
## 32756_at ECH1
## 32780_at DST
## 33098_at CCR3
## 33514_at CAMK4
## 34269_at EDRF1
## 35320_at SLC11A2
## 36020_at <NA>
## 36829_at PER1
## 38771_at HDAC1
## 39709_at SELENOW
## 39850_at ANK2
## 41136_s_at APP
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 4.4.0 beta (2024-04-14 r86421)
## Platform: x86_64-apple-darwin20
## Running under: macOS Monterey 12.7.1
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## 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_3.19.1 AnnotationDbi_1.66.0 IRanges_2.38.0 S4Vectors_0.42.0 ALL_1.45.0 Biobase_2.64.0 BiocGenerics_0.50.0 a4Classif_1.52.0 a4Preproc_1.52.0 a4Core_1.52.0
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## loaded via a namespace (and not attached):
## [1] sass_0.4.9 varSelRF_0.7-8 shape_1.4.6.1 RSQLite_2.3.6 lattice_0.22-6 digest_0.6.35 evaluate_0.23 grid_4.4.0 iterators_1.0.14 fastmap_1.1.1 blob_1.2.4 foreach_1.5.2 jsonlite_1.8.8 glmnet_4.1-8 Matrix_1.7-0 GenomeInfoDb_1.40.0 DBI_1.2.2 survival_3.6-4 httr_1.4.7 UCSC.utils_1.0.0
## [21] Biostrings_2.72.0 codetools_0.2-20 jquerylib_0.1.4 cli_3.6.2 crayon_1.5.2 rlang_1.1.3 XVector_0.44.0 pamr_1.56.2 bit64_4.0.5 splines_4.4.0 cachem_1.0.8 yaml_2.3.8 tools_4.4.0 parallel_4.4.0 memoise_2.0.1 GenomeInfoDbData_1.2.12 ROCR_1.0-11 vctrs_0.6.5 R6_2.5.1 png_0.1-8
## [41] lifecycle_1.0.4 zlibbioc_1.50.0 KEGGREST_1.44.0 randomForest_4.7-1.1 bit_4.0.5 cluster_2.1.6 pkgconfig_2.0.3 bslib_0.7.0 Rcpp_1.0.12 highr_0.10 xfun_0.43 knitr_1.46 htmltools_0.5.8.1 rmarkdown_2.26 compiler_4.4.0