R version: R version 3.6.0 (2019-04-26)
Bioconductor version: 3.9
Package version: 1.10.0
The following code illustrates a typical R / Bioconductor
session. It uses RMA from the affy package to pre-process Affymetrix
arrays, and the limma package for assessing differential expression.
## Load packages
library(affy) # Affymetrix pre-processing
library(limma) # two-color pre-processing; differential
# expression
## import "phenotype" data, describing the experimental design
phenoData <-
read.AnnotatedDataFrame(system.file("extdata", "pdata.txt",
package="arrays"))
## RMA normalization
celfiles <- system.file("extdata", package="arrays")
eset <- justRMA(phenoData=phenoData,
celfile.path=celfiles)## Warning: replacing previous import 'AnnotationDbi::tail' by 'utils::tail'
## when loading 'hgfocuscdf'## Warning: replacing previous import 'AnnotationDbi::head' by 'utils::head'
## when loading 'hgfocuscdf'## ## differential expression
combn <- factor(paste(pData(phenoData)[,1],
pData(phenoData)[,2], sep = "_"))
design <- model.matrix(~combn) # describe model to be fit
fit <- lmFit(eset, design) # fit each probeset to model
efit <- eBayes(fit) # empirical Bayes adjustment
topTable(efit, coef=2) # table of differentially expressed probesets## logFC AveExpr t P.Value adj.P.Val B
## 204582_s_at 3.468416 10.150533 39.03471 1.969915e-14 1.732146e-10 19.86082
## 211548_s_at -2.325670 7.178610 -22.73165 1.541158e-11 6.775701e-08 15.88709
## 216598_s_at 1.936306 7.692822 21.73818 2.658881e-11 7.793180e-08 15.48223
## 211110_s_at 3.157766 7.909391 21.19204 3.625216e-11 7.969130e-08 15.24728
## 206001_at -1.590732 12.402722 -18.64398 1.715422e-10 3.016740e-07 14.01955
## 202409_at 3.274118 6.704989 17.72512 3.156709e-10 4.626157e-07 13.51659
## 221019_s_at 2.251730 7.104012 16.34552 8.353283e-10 1.049292e-06 12.69145
## 204688_at 1.813001 7.125307 14.75281 2.834343e-09 3.115297e-06 11.61959
## 205489_at 1.240713 7.552260 13.62265 7.264649e-09 7.097562e-06 10.76948
## 209288_s_at -1.226421 7.603917 -13.32681 9.401074e-09 7.784531e-06 10.53327A top table resulting from a more complete analysis, described in Chapter 7 of Bioconductor Case Studies, is shown below. The table enumerates Affymetrix probes, the log-fold difference between two experimental groups, the average expression across all samples, the t-statistic describing differential expression, the unadjusted and adjusted (controlling for false discovery rate, in this case) significance of the difference, and log-odds ratio. These results can be used in further analysis and annotation.
ID logFC AveExpr t P.Value adj.P.Val B
636_g_at 1.10 9.20 9.03 4.88e-14 1.23e-10 21.29
39730_at 1.15 9.00 8.59 3.88e-13 4.89e-10 19.34
1635_at 1.20 7.90 7.34 1.23e-10 1.03e-07 13.91
1674_at 1.43 5.00 7.05 4.55e-10 2.87e-07 12.67
40504_at 1.18 4.24 6.66 2.57e-09 1.30e-06 11.03
40202_at 1.78 8.62 6.39 8.62e-09 3.63e-06 9.89
37015_at 1.03 4.33 6.24 1.66e-08 6.00e-06 9.27
32434_at 1.68 4.47 5.97 5.38e-08 1.70e-05 8.16
37027_at 1.35 8.44 5.81 1.10e-07 3.08e-05 7.49
37403_at 1.12 5.09 5.48 4.27e-07 1.08e-04 6.21[ Back to top ]
Follow installation instructions to start using these
packages. You can install affy and limma as follows:
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager")
BiocManager::install(c("affy", "limma"), dependencies=TRUE)To install additional packages, such as the annotations associated with the Affymetrix Human Genome U95A 2.0, use
BiocManager::install("hgu95av2.db", dependencies=TRUE)Package installation is required only once per R installation. View a /packagesfull list of available packages.
To use the affy and limma packages, evaluate the commands
library("affy")
library("limma")These commands are required once in each R session.
[ Back to top ]
Packages have extensive help pages, and include vignettes highlighting common use cases. The help pages and vignettes are available from within R. After loading a package, use syntax like
help(package="limma")
?topTableto obtain an overview of help on the limma package, and the
topTable function, and
browseVignettes(package="limma")to view vignettes (providing a more comprehensive introduction to
package functionality) in the limma package. Use
help.start()to open a web page containing comprehensive help resources.
[ Back to top ]
The following provide a brief overview of packages useful for pre-processing. More comprehensive workflows can be found in documentation (available from package descriptions) and in Bioconductor Books and monographs.
BiocManager::install()BiocManager::install()BiocManager::install()BiocManager::install()lumiHumanAll.db and lumiHumanIDMapping)illuminaHumanv1BeadID.db and illuminaHumanV1.db)[ Back to top ]
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] hgfocuscdf_2.18.0 affy_1.62.0 Biobase_2.44.0
## [4] BiocGenerics_0.30.0 limma_3.40.0 arrays_1.10.0
## [7] BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.1 AnnotationDbi_1.46.0 knitr_1.22
## [4] magrittr_1.5 IRanges_2.18.0 zlibbioc_1.30.0
## [7] bit_1.1-14 blob_1.1.1 stringr_1.4.0
## [10] tools_3.6.0 xfun_0.6 DBI_1.0.0
## [13] htmltools_0.3.6 bit64_0.9-7 yaml_2.2.0
## [16] digest_0.6.18 preprocessCore_1.46.0 bookdown_0.9
## [19] affyio_1.54.0 BiocManager_1.30.4 S4Vectors_0.22.0
## [22] memoise_1.1.0 RSQLite_2.1.1 evaluate_0.13
## [25] rmarkdown_1.12 stringi_1.4.3 compiler_3.6.0
## [28] stats4_3.6.0[ Back to top ]