Vignette of the a4Classif package
2024-10-29
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##
## a4Classif version 1.54.0## Loading required package: Biobase## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
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## Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## 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## 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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## I, expand.grid, unname##
## Attaching package: 'IRanges'## The following object is masked from 'package:grDevices':
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## windows## Loading required package: org.Hs.eg.db## ## 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 -0.048588632.2 PAM regression
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 -0.1693 0.4875 1 1confusionMatrix(resultPam)## predicted
## true B T
## B 95 0
## T 1 322.3 Random 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)
topTable(resultRf, n = 15)## Random forest selected 14 genes. The top 15 genes are:
##
## GeneSymbol
## 1039_s_at HIF1A
## 1497_at LTBR
## 31843_at ESRRG
## 33369_at MSMO1
## 33376_at <NA>
## 33433_at FAM98A
## 34362_at SLC2A5
## 35743_at CPSF4
## 37948_at ATP6V1C1
## 39604_at CYTIP
## 40109_at SRF
## 40606_at ELL2
## 41448_at HOXA10
## 446_at CSNK1G22.4 ROC curve
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.
3 Appendix
3.1 Session information
## R version 4.4.1 (2024-06-14 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=C LC_CTYPE=English_United States.utf8 LC_MONETARY=English_United States.utf8 LC_NUMERIC=C LC_TIME=English_United States.utf8
##
## time zone: America/New_York
## tzcode source: internal
##
## 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_3.20.0 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
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.9 varSelRF_0.7-8 shape_1.4.6.1 RSQLite_2.3.7 lattice_0.22-6 digest_0.6.37 evaluate_1.0.1 grid_4.4.1 iterators_1.0.14 fastmap_1.2.0 blob_1.2.4 foreach_1.5.2 jsonlite_1.8.9 glmnet_4.1-8 Matrix_1.7-1 GenomeInfoDb_1.42.0 DBI_1.2.3 survival_3.7-0 httr_1.4.7 UCSC.utils_1.2.0
## [21] Biostrings_2.74.0 codetools_0.2-20 jquerylib_0.1.4 cli_3.6.3 crayon_1.5.3 rlang_1.1.4 XVector_0.46.0 pamr_1.57 bit64_4.5.2 splines_4.4.1 cachem_1.1.0 yaml_2.3.10 tools_4.4.1 parallel_4.4.1 memoise_2.0.1 GenomeInfoDbData_1.2.13 ROCR_1.0-11 vctrs_0.6.5 R6_2.5.1 png_0.1-8
## [41] lifecycle_1.0.4 zlibbioc_1.52.0 KEGGREST_1.46.0 randomForest_4.7-1.2 bit_4.5.0 cluster_2.1.6 pkgconfig_2.0.3 bslib_0.8.0 Rcpp_1.0.13 highr_0.11 xfun_0.48 knitr_1.48 htmltools_0.5.8.1 rmarkdown_2.28 compiler_4.4.1