Using Bioconductor for Microarray Analysis

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1 Version Info

R version: R version 3.5.0 (2018-04-23)
Bioconductor version: 3.7
Package version: 1.4.0

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2 Sample Workflow

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 10.53327

A 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

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3 Installation and Use

Follow installation instructions to start using these packages. You can install affy and limma as follows:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite(c("affy", "limma"))

To install additional packages, such as the annotations associated with the Affymetrix Human Genome U95A 2.0, use

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("hgu95av2.db")

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.

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4 Exploring Package Content

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")
?topTable

to 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.

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5 Pre-Processing Resources

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.

sessionInfo()

## R version 3.5.0 (2018-04-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.7-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=en_US.UTF-8    
##  [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_1.58.0         Biobase_2.40.0     
## [4] BiocGenerics_0.26.0 limma_3.36.1        arrays_1.4.0       
## [7] BiocStyle_2.8.1    
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.16          AnnotationDbi_1.42.1  knitr_1.20           
##  [4] magrittr_1.5          IRanges_2.14.10       zlibbioc_1.26.0      
##  [7] bit_1.1-13            blob_1.1.1            stringr_1.3.1        
## [10] tools_3.5.0           xfun_0.1              DBI_1.0.0            
## [13] htmltools_0.3.6       bit64_0.9-7           yaml_2.1.19          
## [16] rprojroot_1.3-2       digest_0.6.15         preprocessCore_1.42.0
## [19] bookdown_0.7          affyio_1.50.0         S4Vectors_0.18.2     
## [22] memoise_1.1.0         RSQLite_2.1.1         evaluate_0.10.1      
## [25] rmarkdown_1.9         stringi_1.2.2         compiler_3.5.0       
## [28] BiocInstaller_1.30.0  backports_1.1.2       stats4_3.5.0