If you are new to this package, then please consult the vignette Introduction: microarray quality assessment with arrayQualityMetrics.
This vignette addresses advanced topics. It explains how to customize the report by selecting specific modules and sections, or by adding your own ones. Furthermore, we will see how to (programmatically) postprocess the results of the outlier detection, or how to adapt the detection criteria to your needs.
Terminology: In the documentation of this package, we
refer to a module as a self-contained element of a report that
investigates one particular quality metric. A module consists of a
figure and an explanatory text. It may also contain a data structure
(an object of class outlierDetection) that
marks a subset of the arrays in the dataset as outliers
according to the particular metric investigated in the module. We
refer to a section as a collection of one or more modules that
are thematically related.
For the following examples, let us load the needed packages and some data.
library("arrayQualityMetrics")
library("ALLMLL")
data("MLL.A")Some of the computations that are needed for the modules are common
between several modules, and thus we perform them once,
beforehand. These functions are called prepdata() and
prepaffy(), and we refer to their manual page for
details. For example,
preparedData = prepdata(expressionset = MLL.A,
intgroup = c(),
do.logtransform = TRUE)The arguments intgroup and do.logtransform are the
same as for arrayQualityMetrics(), but in
prepdata() they have no defaults, so we need to set them
explicitely.
The package contains a variety of functions that compute modules, and they are listed on a manual page which you can access by typing:
?aqm.boxplotHere, let us create a report with only two quality metrics modules: boxplots and density plots.
bo = aqm.boxplot(preparedData)
de = aqm.density(preparedData)
qm = list("Boxplot" = bo, "Density" = de)The objects bo and de are of class
aqmReportModule; please consult the class’ manual page for
more information.
If you want to create your own modules, please have a look at the code
for the various existing functions for this purpose, and adapt it.
The function aqm.pca() is a good place to start.
Some of the modules perform outlier detection. For instance, in the
default report produced by arrayQualityMetrics(), the
module headed Boxplots is followed by one headed Outlier
detection for Boxplots. In the corresponding
aqmReportModule object, this is reflected by a non-trivial
value for the slot named outliers:
bo@outliers## An object of class "outlierDetection"
## Slot "statistic":
## JD-ALD009-v5-U133A.CEL JD-ALD051-v5-U133A.CEL JD-ALD052-v5-U133A.CEL
## 0.0720175 0.0995325 0.1592525
## JD-ALD057-v5-U133A.CEL JD-ALD078-v5-U133A.CEL JD-ALD180-v5-U133A.CEL
## 0.0607050 0.1188175 0.2300675
## JD-ALD226-NA-v5-U133A.CEL JD-ALD232-v5-U133A.CEL JD-ALD237-v5-U133A.CEL
## 0.2186025 0.0948925 0.1490025
## JD-ALD244-v5-U133A.CEL JD-ALD294-v5-U133A.CEL JD-ALD380-v5-U133A.CEL
## 0.1318325 0.3746250 0.0497100
## JD-ALD381-v5-U133A.CEL JD-ALD384-v5-U133A.CEL JD-ALD385-v5-U133A.CEL
## 0.0933025 0.4387025 0.1629675
## JD-ALD420-v5-U133A.CEL JD-ALD421-v5-U133A.CEL JD-ALD431-v5-U133A.CEL
## 0.1027575 0.1250250 0.1942300
## JD-ALD433-v5-U133A.CEL JD-ALD520-v5-U133A.CEL
## 0.1485525 0.0605000
##
## Slot "threshold":
## JD-ALD385-v5-U133A.CEL
## 0.3053506
##
## Slot "which":
## JD-ALD294-v5-U133A.CEL JD-ALD384-v5-U133A.CEL
## 11 14
##
## Slot "description":
## [1] "Kolmogorov-Smirnov statistic <i>K<sub>a</sub></i>"
## [2] "data-driven"The slot named statistic contains, for each array, a
single number based on which outlier detection is performed. For
instance, in the case of bo, the slot
bo@outliers@statistic is the Kolmogorov-Smirnov statistic
for the comparison between each array’s intensity distribution and the
distribution of the pooled data. The slot threshold contains
the threshold against which the valuess of statistic were
compared. Arrays with a value of statistic greater than
threshold are called outliers. Their indices are listed in
the vector which. Finally, the slot description
contains a textual description of the definition of
statistic and indicates how the threshold was chosen.
The actual details of outlier detection are different for each report
module, and are documented in the figure caption of the report
module. For more information, please look at the code of
the report module generating function of interest – for instance, at the first few
lines of the boxplot function. The code there uses the helper
functions outliers and boxplotOutliers, which
are documented in their manual pages.
A report is rendered by calling the function
aqm.writereport() with a list of aqmReportModule
objects.
outdir = tempdir()
aqm.writereport(modules = qm, reporttitle = "My example", outdir = outdir,
arrayTable = pData(MLL.A))
outdir## [1] "/tmp/RtmpEAwhSU"Point your browser to the index.html file in that directory.
## 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] ALLMLL_1.51.0 affy_1.89.0
## [3] Biobase_2.71.0 BiocGenerics_0.57.0
## [5] generics_0.1.4 arrayQualityMetrics_3.67.1
## [7] 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
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## [28] beadarray_2.61.2 jpeg_0.1-11 cluster_2.1.8.2
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## [73] scales_1.4.0 xtable_1.8-8 glue_1.8.0
## [76] Hmisc_5.2-5 tools_4.6.0 interp_1.1-6
## [79] hexbin_1.28.5 data.table_1.18.2.1 vsn_3.79.6
## [82] affyPLM_1.87.0 annotate_1.89.0 XML_3.99-0.23
## [85] grid_4.6.0 latticeExtra_0.6-31 AnnotationDbi_1.73.0
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