Contents

0.1 Introduction

The {arrayQualityMetrics} package produces, through a single function call, a comprehensive HTML report of quality metrics about a microarray dataset (Kauffmann, Gentleman, and Huber 2009, @Kauffmann:2010:Genomics, @McCall2010). The quality metrics are mainly on the per array level, i.e., they can be used to assess the relative quality of different arrays within a dataset. Some of the metrics can also be used to diagnose batch effects, and thus the quality of the overall dataset.

The report can be extended to contain further diagnostics through additional arguments, and we will see examples for this in Section ??.

The aim of the {arrayQualityMetrics} package is to produce information that is relevant for your decision making - not, to make the decision. It will often be applied to two, somewhat distinct, use cases: (i) assessing quality of a ``raw’’ dataset, in order to get feedback on the experimental procedures that produced the data; (ii) assessing quality of a normalised dataset, in order to decide whether and how to use the dataset (or subsets of arrays in it) for subsequent data analysis.

Different types of microarray data (one colour, two colour, Affymetrix, Illumina) are represented by different object classes in Bioconductor. The function arrayQualityMetrics() will work in the same way for all of them. Further information about its arguments can be found in its manual page.

When the function arrayQualityMetrics() is finished, a report is produced in the directory specified by the function’s outdir argument. By default, a directory with a suitable name is created in the current working directory. This directory contains an HTML page index.html that can be opened by a browser. The report contains a series of plots explained by text. Some of the plots are interactive (see Figure ??). Technically, this is achieved by the use of SVG (scalable vector graphics) and JavaScript, and it requires that you use a recent (HTML5 capable) web browser.1 If in doubt, please see the notes about browser compatibility at the top of the report; or contact the Bioconductor mailing list. Other plots, where interactivity is less relevant, are provided as bitmaps (PNG format) and are also linked to PDF files that provide high resolution versions e.,g. for publication.

Plus (+) or minus (-) symbols at the begin of different section headings of the report (as in the left panel of Figure ??) indicate that you can show or hide these sections by clicking on the heading. After (re)loading, all sections are shown except for the Outlier detection barplots, which are hidden and can be expanded by clicking on them.

knitr::include_graphics("pcaplot.png")
knitr::include_graphics("arraymetadatatable.png")
An example plot from the report. The plot shows the arrays (points) in a two-dimensional plot area spanned by the first two axes of a principal component analysis (PCA). By moving the mouse over the points, the corresponding array's metadata is displayed in the table to the right of the plot. By clicking on a point, it can be selected or deselected. Selected arrays are indicated by larger points or wider lines in the plots and by ticked checkboxes in the array table shown in the *right* panel. Arrays can also be (de)selected by clicking the checkboxes. Initially, when the report is loaded (or reloaded) by the browser, all arrays are selected that were called outliers by at least one criterion.An example plot from the report. The plot shows the arrays (points) in a two-dimensional plot area spanned by the first two axes of a principal component analysis (PCA). By moving the mouse over the points, the corresponding array's metadata is displayed in the table to the right of the plot. By clicking on a point, it can be selected or deselected. Selected arrays are indicated by larger points or wider lines in the plots and by ticked checkboxes in the array table shown in the *right* panel. Arrays can also be (de)selected by clicking the checkboxes. Initially, when the report is loaded (or reloaded) by the browser, all arrays are selected that were called outliers by at least one criterion.

Figure 1: An example plot from the
report. The plot shows the arrays (points) in a two-dimensional plot area spanned by the first two axes of a principal component analysis (PCA). By moving the mouse over the points, the corresponding array’s metadata is displayed in the table to the right of the plot. By clicking on a point, it can be selected or deselected. Selected arrays are indicated by larger points or wider lines in the plots and by ticked checkboxes in the array table shown in the right panel. Arrays can also be (de)selected by clicking the checkboxes. Initially, when the report is loaded (or reloaded) by the browser, all arrays are selected that were called outliers by at least one criterion.

Metadata about the arrays is shown at the top of the report as a table (see Figure ??). It is extracted from the phenoData slot of the data object supplied to arrayQualityMetrics(). It can be useful to adjust the contents this slot before producing the report, and to make sure it contains the right quantity of information to make an informative report - not too much, not too little.

In the case of AffyBatch input, some Affymetrix specific sections are added to the standard report. Also for other types of arrays, sections can be added to the standard report if certain metadata are present in the input object (see Section ??).

The function arrayQualityMetrics() also produces an R object (essentially, a big list) with all the information contained in the report, and this object can be used by downstream tools for programmatic analysis of the report. This is discussed in the vignette

0.2 Basic use

0.2.1 Affymetrix data - before preprocessing

If you are working with Affymetrix GeneChips, an AffyBatch object is the most appropriate way to import your raw data into Bioconductor. Starting from CEL files, this is typically done using the function ReadAffy() from the {affy} package2 For more information on how to produce an AffyBatch from your data, please see the documentation of the {affy} package.

Here, we use the dataset MLL.A, an object of class AffyBatch provided in the data package {ALLMLL}.

library("ALLMLL")
data("MLL.A")

Now that the data are loaded, we can call arrayQualityMetrics().3 For this vignette, in order to save computation time, we only call the function on the first 5 arrays; in your own application, you can call it on the complete data object.

library("arrayQualityMetrics")
arrayQualityMetrics(expressionset = MLL.A[, 1:5],
                    outdir = "Report_for_MLL_A",
                    force = TRUE,
                    do.logtransform = TRUE)

This is the simplest way of calling the function. We give a name to the directory (outdir) and we overwrite the possibly existing files of this directory (force). Finally, we set do.logtransform to logarithm transform the intensities. You can then view the report by directing your browser to the file index.html in the directory whose name is indicated by outdir.

0.2.2 Affymetrix data - after preprocessing

We can call the RMA algorithm on MLL.A to obtain a preprocessed dataset. The preprocessing includes background correction, between array intensity adjustment (normalisation) and probeset summarisation. The resulting object nMLL is of class ExpressionSet and contains one value (expression estimate) for each gene for each array.

nMLL = rma(MLL.A)

We can then call again the function arrayQualityMetrics().

arrayQualityMetrics(expressionset = nMLL,
                    outdir = "Report_for_nMLL",
                    force = TRUE)

We do not need to set do.logtransform as after rma() the data are already logarithm transformed.

0.2.3 ExpressionSet and ExpressionSetIllumina

If you are working on one colour arrays other than Affymetrix genechips, you can load your data into Bioconductor as an ExpressionSet object , or if you work with Illumina data and the {beadarray} package, as an ExpressionSetIllumina object. You can then proceed exactly as above.

0.2.4 Two colour arrays, NChannelSet, RGList, MAList

The package {limma} imports a wide range of data formats used for two colour arrays and produces objects of class RGList or MAList. When presented with an object of these classes, arrayQualityMetrics() tries to convert them into an NChannelSet and then proceeds with calling its NChannelSet method.

Alternatively, you can create an NChannelSet to contain your data `from scratch''. The documentation of the{Biobase}` package gives instructions on how to do so.

The arrayQualityMetrics() function expects the assayData slot of the NChannelSet object to contain the elements R and G, for the red'' and thegreen’’ intensities. Optionally, it can contain elements Rb and Gb for associated `background'' intensities. As an alternative to all that, thearrayQualityMetrics()function also acceptsNChannelSetobjects with a single slotexprs, and will then simply behave like it does for (single-colour)ExpressionSet` objects.

As an example, we consider the dataset CCl4 from the data package {CCl4} and normalize it using the variance stabilization method available in the package {vsn}.

library("vsn")
library("CCl4")
data("CCl4")
nCCl4 = justvsn(CCl4, subsample = 15000)
arrayQualityMetrics(expressionset = nCCl4,
                    outdir = "Report_for_nCCl4",
                    force = TRUE)

0.2.5 Loading data from ArrayExpress

You can use the {ArrayExpress} package (Kauffmann et al. 2009) to download datasets from the EBI’s ArrayExpress database. The resulting ExpressionSet, AffyBatch or NChannelSet objects can be directly fed into arrayQualityMetrics().

0.3 Making the report more informative by adding a factor of interest

A useful feature of arrayQualityMetrics() is the possibility to show the results in the context of an experimental factor of interest, i.,e. a categorical variable associated with the arrays such as hybridisation date, treatment level or replicate number. Specifying a factor does not change how the quality metrics are computed. By setting the argument intgroup() to contain the names of one or multiple columns of the data object’s phenoData slot, a bar on the side of the heatmap with colours representing the respective factors is added. Similarly, the colours of the boxplots and density plots reflect the levels of the first of the factors named by intgroup().

We use the nMLL example again, and create artificial array metadata factors condition and batch (see Section~?? for a more realistic example).

pData(nMLL)$condition = rep(letters[1:4], times = 5)
pData(nMLL)$batch = rep(paste(1:4), each = 5)
arrayQualityMetrics(expressionset = nMLL,
                    outdir = "Report_for_nMLL_with_factors",
                    force = TRUE,
                    intgroup = c("condition", "batch"))

0.4 Extended use

{#sec:ext}

Some of the quality metrics that the package can compute require specific information about the features on the arrays. To use these, you need to make sure that this information is provided in your input object. We use the nCCl4 example again.

0.4.1 Spatial layout of the array

{#sec:spatial}

To plot the spatial distributions of the intensities of the arrays, arrayQualityMetrics() needs the spatial coordinates of the features on the chip. For AffyBatch or BeadLevelList, this information is automatically available without further user input. For the other types of objects, two columns corresponding to \(X\) and \(Y\) coordinates of the features are required in the featureData slot of the object. These columns should be named “X” and “Y”. If the arrays are split into blocks, rows and columns, then the function addXYfromGAL() (please check its manual page for details) can used to convert the row, column and blocks indices into absolute “X” and “Y” coordinates on the array. In the example of the dataset CCl4, the coordinates of the spots are in the columns named “Row” and “Column” of the featureData (the slot of the object containing the annotation of the probes). We copy this information into columns named “X” and “Y” respectively

featureData(nCCl4)$X = featureData(nCCl4)$Row
featureData(nCCl4)$Y = featureData(nCCl4)$Column

The next call to arrayQualityMetrics() with this refined version of nCCl4 (see Section~??) will now include this information in the report, and the spatial distribution of the intensities will be shown.

0.4.2 Mapping of the reporters

The report can also include an assessment of the effect of the target mapping of the reporters. You can define a featureData column named hasTarget that indicates, by logical TRUE, if the reporter matches a known transcript, and by FALSE, if not. In the CCl4 example, many of the reporter names are RefSeq identifiers, while others are not. Thus, we let hasTarget indicate whether the name begins with “NM”.

featureData(nCCl4)$hasTarget = (regexpr("^NM", featureData(nCCl4)$Name) > 0)
table(featureData(nCCl4)$hasTarget)
## 
## FALSE  TRUE 
## 33296 10332

The next call to arrayQualityMetrics() with this refined version of nCCl4 (see Section ??) will now include this information in the report, and the spatial distribution of the intensities will be shown.

0.4.3 RNA quality

{#sec:rin}

The RNA hybridized to the arrays in the {CCl4} dataset was intentionally made to good, medium or poor quality, and this is recorded by a so-called RIN value (see CCl4 vignette).

pd = pData(CCl4)
rownames(pd) = NULL
pd
##     Cy3  Cy5 RIN.Cy3 RIN.Cy5
## 1  DMSO CCl4     9.0     9.7
## 2  DMSO CCl4     9.0     5.0
## 3  DMSO CCl4     9.0     2.5
## 4  DMSO CCl4     9.0     2.5
## 5  CCl4 DMSO     9.7     9.0
## 6  CCl4 DMSO     5.0     9.0
## 7  CCl4 DMSO     2.5     9.0
## 8  DMSO CCl4     9.0     9.7
## 9  CCl4 DMSO     9.7     9.0
## 10 CCl4 DMSO     5.0     9.0
## 11 CCl4 DMSO     2.5     9.0
## 12 DMSO CCl4     9.0     9.7
## 13 DMSO CCl4     9.0     5.0
## 14 DMSO CCl4     9.0     5.0
## 15 DMSO CCl4     9.0     2.5
## 16 CCl4 DMSO     9.7     9.0
## 17 CCl4 DMSO     5.0     9.0
## 18 CCl4 DMSO     2.5     9.0

The RIN is always 9 for the reference (DMSO), the relevant value is that for the test sample (CCl4).

RIN = with(pd, ifelse( Cy3=="CCl4", RIN.Cy3, RIN.Cy5))
fRIN = factor(RIN)
levels(fRIN) = c("poor", "medium", "good")
pData(nCCl4)$"RNA-integrity" = fRIN

Now we can use this to set the argument intgroup when calling the function arrayQualityMetrics().

arrayQualityMetrics(expressionset = nCCl4,
                    outdir = "Report_for_nCCl4_with_RIN",
                    force = TRUE,
                    intgroup = "RNA-integrity")

Boxplots, PCA plot and heatmap in the report will now indicate the values of the factor RNA-integrity for each array.

0.5 Session Info

R Under development (unstable) (2026-03-05 r89546) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 24.04.4 LTS

Matrix products: default BLAS: /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0

locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB 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

time zone: America/New_York tzcode source: system (glibc)

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] CCl4_1.49.0 limma_3.67.0
[3] vsn_3.79.6 hgu133acdf_2.18.0
[5] ALLMLL_1.51.0 affy_1.89.0
[7] Biobase_2.71.0 BiocGenerics_0.57.0
[9] generics_0.1.4 arrayQualityMetrics_3.67.1 [11] BiocStyle_2.39.0

loaded via a namespace (and not attached): [1] DBI_1.3.0 deldir_2.0-4 gridExtra_2.3
[4] rlang_1.1.7 magrittr_2.0.4 otel_0.2.0
[7] matrixStats_1.5.0 compiler_4.6.0 RSQLite_2.4.6
[10] png_0.1-9 systemfonts_1.3.2 vctrs_0.7.1
[13] reshape2_1.4.5 stringr_1.6.0 pkgconfig_2.0.3
[16] crayon_1.5.3 fastmap_1.2.0 backports_1.5.0
[19] XVector_0.51.0 labeling_0.4.3 rmarkdown_2.30
[22] preprocessCore_1.73.0 bit_4.6.0 xfun_0.57
[25] cachem_1.1.0 jsonlite_2.0.0 blob_1.3.0
[28] BeadDataPackR_1.63.0 beadarray_2.61.2 jpeg_0.1-11
[31] cluster_2.1.8.2 R6_2.6.1 bslib_0.10.0
[34] stringi_1.8.7 RColorBrewer_1.1-3 genefilter_1.93.0
[37] rpart_4.1.24 GenomicRanges_1.63.1 jquerylib_0.1.4
[40] Rcpp_1.1.1 Seqinfo_1.1.0 bookdown_0.46
[43] knitr_1.51 gcrma_2.83.0 base64enc_0.1-6
[46] IRanges_2.45.0 Matrix_1.7-5 splines_4.6.0
[49] nnet_7.3-20 tidyselect_1.2.1 rstudioapi_0.18.0
[52] dichromat_2.0-0.1 yaml_2.3.12 codetools_0.2-20
[55] hwriter_1.3.2.1 lattice_0.22-9 tibble_3.3.1
[58] plyr_1.8.9 withr_3.0.2 KEGGREST_1.51.1
[61] S7_0.2.1 evaluate_1.0.5 foreign_0.8-91
[64] survival_3.8-6 Biostrings_2.79.5 pillar_1.11.1
[67] affyio_1.81.0 BiocManager_1.30.27 MatrixGenerics_1.23.0 [70] KernSmooth_2.23-26 checkmate_2.3.4 stats4_4.6.0
[73] S4Vectors_0.49.0 ggplot2_4.0.2 scales_1.4.0
[76] xtable_1.8-8 glue_1.8.0 Hmisc_5.2-5
[79] tools_4.6.0 interp_1.1-6 hexbin_1.28.5
[82] data.table_1.18.2.1 affyPLM_1.87.0 annotate_1.89.0
[85] XML_3.99-0.23 grid_4.6.0 latticeExtra_0.6-31
[88] AnnotationDbi_1.73.0 colorspace_2.1-2 htmlTable_2.4.3
[91] Formula_1.2-5 cli_3.6.5 textshaping_1.0.5
[94] gridSVG_1.7-7 setRNG_2024.2-1 svglite_2.2.2
[97] dplyr_1.2.0 gtable_0.3.6 sass_0.4.10
[100] digest_0.6.39 htmlwidgets_1.6.4 farver_2.1.2
[103] memoise_2.0.1 htmltools_0.5.9 lifecycle_1.0.5
[106] httr_1.4.8 statmod_1.5.1 bit64_4.6.0-1

References

Kauffmann, Audrey, Robert Gentleman, and Wolfgang Huber. 2009. “arrayQualityMetrics - a Bioconductor Package for Quality Assessment of Microarray Data.” Bioinformatics 25: 415–16.

Kauffmann, Audrey, and Wolfgang Huber. 2010. “Microarray data quality control improves the detection of differentially expressed genes.” Genomics 95: 138–42.

Kauffmann, Audrey, Tim F. Rayner, Helen Parkinson, Misha Kapushesky, Margus Lukk, Alivs Brazma, and Wolfgang Huber. 2009. “Importing ArrayExpress datasets into R/Bioconductor.” Bioinformatics 25: 2092–4.

McCall, Matthew N., Peter N. Murakami, and Rafael A. Irizarry. 2010. “Assessing Microarray Quality.” Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.