1 Introduction
Gene expression patterns are commonly assessed by microarrays that can measure thousands of genes simultaneously. However, in a typical microarray experiment, only a small fraction of the genes are informative which motivated the development of gene filtering methods. Gene filtering aims at reducing the dimensionality of data by filtering redundant features prior to the actual statistical analysis. This has been shown to improve differential expression analysis with Affymetrix microarrays (Calza, W. Raffelsberger, A. Ploner, J. Sahel, T. Leveillard, and Y. Pawitan 2007; e.g., Talloen, D.-A. Clevert, S. Hochreiter, D. Amaratunga, L. Bijnens, S. Kass, and H. W. H. Göhlmann 2007; Kasim, D. Lin, S. Van Sanden, D.-A. Clevert, L. Bijnens, H. Göhlmann, D. Amaratunga, S. Hochreiter, Z. Shkedy, and W. Talloen 2010).
Although different microarrays platforms share the same principle of
hybridizing DNA to a complementary probe, they differ considerably by
design. Unlike the Affymetrix microarrays which have sets of unique
probes targeting a particular gene (resulting in a probe set for a
targeted gene), Illumina microarrays have sets of identical probes.
Thus, the existing filtering methods can not be readily ported to the
Illumina platform. As a result, Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement (2012) developed a filtering
method for Illumina bead arrays. Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement (2012) equally showed that
filtering improves the analysis of differential expression. We provide
the implementation of their method in the
beadarrayFilter
R software package. The beadarrayFilter package can take a normalized
data frame or a normalized bead array ExpressionSetIllumina object
(obtained using the summarize or readBeadSummaryData functions in
the Bioconductor package beadarray by Dunning, M. L. Smith, M. E. Ritchie, and S. Tavaré 2007) or a normalized
LumiBatch object as input and returns a list containing a filtered data
frame or a filtered bead array ExpressionSetIllumina object or a
filtered LumiBatch object, respectively. The package can also process
summarized and normalized average intensities (eSet), their standard
errors (seSet) and the number of beads used for summarization (nSet) as
input and returns a list of components including the intra-cluster
correlations (ICC), which can be used to assess different filtering
strategies.
The paper contains a brief background of the filtering methodology followed by the introduction of the beadarrayFilter package with illustrative examples.
2 Filtering Illumina bead types
Let \(Y_{kij}\) be the bead-level expression intensity of bead \(j\) in sample (array) \(i\) in treatment group \(k\), \(i = 1,\ldots, n_k\), \(j = 1,\ldots,m_{ki}\), \(k = 1,\ldots, K\), then, Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement (2012) proposed the following filtering model:
\[Y_{kij} = \mu + b_i + \epsilon_{kij}, \label{eq:filter} \tag{1} \]
where \(\mu\) represents the average expression for a bead type across all samples, \(b_i\) is the array specific random effect and \(\epsilon_{kij}\) are the measurement errors, both normally distributed with mean zero and variance \(\tau^2\) and \(\sigma^2\), respectively:
\(b_i \sim N(0,\tau^2)\), \(\epsilon_{kij} \sim N(0,\sigma^2)\).
The \(\tau^{2}\) captures the between-array variability while \(\sigma^2\) models the within-array variability. Model ([eq filter]) implies a common ICC, (Verbeke and G. Molenberghs 2000), given by
\[\rho = \frac{\tau^2}{\tau^2 + \sigma^2} , \label{eq:intracl} \tag{2} \]
which is the correlation among the replicate probes on the array.
An informative bead type is expected to have a relatively high value of \(\rho\) since all corresponding beads have the same sequence. As such, the between-array variability is the dominant variance component for informative bead types while the within-array variability is the largest variance component for the non-informative bead types.
For Illumina bead arrays, within-array variability is expected to vary across all arrays and treatment groups in the experiment. In this regard, Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement (2012) included an array/treatment specific variance component to adjust the model for heteroscedasticity i.e., \(\epsilon_{kij} \sim N(0,\sigma^2_{ki})\). Hence, the ICC becomes bead/array/group specific,
\[\rho_{ki} = \frac{\tau^2}{\tau^2 + \sigma^2_{ki}, } \label{eq:intrac} \tag{3} \]
and \(\sum_{k=1}^K n_k\) ICCs are obtained for each bead type (more details can be found in Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement 2012).
ICC based filtering of Affymetrix microarray data has been proposed in the literature (e.g., Talloen, D.-A. Clevert, S. Hochreiter, D. Amaratunga, L. Bijnens, S. Kass, and H. W. H. Göhlmann 2007; Kasim, D. Lin, S. Van Sanden, D.-A. Clevert, L. Bijnens, H. Göhlmann, D. Amaratunga, S. Hochreiter, Z. Shkedy, and W. Talloen 2010). Typically, an ICC cut-off \(\delta = 0.5\) has been used to declare a transcript informative. Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement (2012) also relied on the ICC as a filtering criterion and proposed different thresholding schemes based on the five number summaries, i.e., a bead type can be called informative based on thresholding (1) the minimum ICC, (2) 25, (3) 50, (4) 75 quantiles or (5) the maximum ICC of all the arrays. Filtering is expected to become less stringent from strategy (1)–(5). These different thresholding strategies are implemented within the package.
3 beadarrayFilter package
The beadarrayFilter package can either take a normalized
ExpressionSetIllumina object, a normalized LumiBatch object, a
normalized data.frame or a normalized eSet, seSet and nSet as input and
returns a list. We refer the user to the documentation of the
Bioconductor packages beadarray and lumi for more details on
generating ExpressionSetIllumina objects or LumiBatch objects. For each
bead type, the ICCs can be summarized using the 5 number summary or user
specified quantiles. The corresponding ICC summaries are used for
obtaining informative bead types. The package contains two major
functions, which we refer to as: (1) a low level function iccFun and
(2) a wrapper function beadtypeFilter. Model fitting is done using a
modified version of the MLM.beadarray function of (Kim and J. Lin 2011).
Details on the functions can be obtained by using the help function in R
(?beadtypeFilter or ?iccFun).
Installation of beadarrayFilter
The beadarrayFilter package is available at CRAN, and can be installed
using
install.packages("beadarrayFilter").
beadtypeFilter function
The beadtypeFilter function is a wrapper function for the iccFun
function and is designed for users with a primary interest in obtaining
filtered bead types. This function takes a normalized
ExpressionSetIllumina object, a normalized LumiBatch object or a
normalized data.frame and returns the names of the informative bead
types. Optionally, the filtered ExpressionSetIllumina object or the
filtered data.frame can also be returned. The filtered
ExpressionSetIllumina object, or filtered LumiBatch object or the
filtered data.frame can then be used for the downstream analysis.
iccFun function
iccFun is a low level function. It is designed for users who want to
assess different filtering strategies. It takes a normalized eSet, seSet
and nSet and the bead types identification variable (ProbeID), fits the
filtering Model ((1)), calculates the ICC for each bead
type on each array/treatment group, summaries the ICCs at the specified
quantiles, and returns the ICC summaries, the within-array variances,
the between-array variances as well as all ICCs. The ICCs output can
later be used for filtering or to assess different filtering strategies.
Note the information printed as you execute the beadtypeFilter or
iccFun functions:
\([1]\) "Number of converged transcripts: ... "
This indicates the number of transcripts for which the filtering model has already converged while "Now ... remaining..." tells the number of transcripts still to be processed.\([1]\) "Computing ICC for each array"
This message is printed when the function begins to calculate the ICC for each array once the filtering model has been fitted to all the transcripts.\([1]\) "Summarising the ICC at supplied quantile(s)"
This message indicates the last stage of the filtering function where the ICCs are summarized at the supplied quantiles (see Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement 2012).
The emCDF function within the beadarrayFilter package is used to
plot the empirical cumulative density functions (edcfs) for the
different threshold strategies discussed above. It processes the
iccFun output and plots the empirical cumulative density functions
(ecdf) for the different threshold strategies as discussed in
Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement (2012). It is expected that the filtering becomes more stringent
as the ICC threshold increases and/or as the the thresholded ICC
quantile decreases.
Informative bead types will have larger between-array variances as
compared to the within-array variances. The varianceplot function
takes the estimated between- and within-array variances from the
iccFun function as inputs and plots them. The variances of
noninformative bead types are plotted in blue while those of the
informative bead types are displayed in red.
Upon filtering the MLM.beadarray function can be used for downstream
analysis of single factor designs. For more details, see
(Kim and J. Lin 2011).
A summary of the functions within the beadarrayFilter package and their use is presented in Table 1.
| Function | Description |
|---|---|
beadtypeFilter() |
Fits the filtering model as in Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement (2012), |
| computes the ICC and filters the bead types. | |
iccFun() |
Fits the filtering model as in Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement (2012), and computes |
| the ICC which can later be used for filtering or to assess different | |
| filtering strategies | |
MLM.beadarray() |
Function to fit the filtering model or for the downstream analysis |
| of single factor experimental designs | |
emCDF() |
Plots the ecdf for the different threshold strategies |
varianceplot() |
Plots the the between-array and the within-array variances |
4 Examples
The data set exampleSummaryData from the Bioconductor package
beadarrayExampleData (Dunning, M. L. Smith, M. E. Ritchie, and S. Tavaré 2007), is used to illustrate filtering
of ExpressionSetIllumina objects while the publicly available spike-in
data (Dunning, N. B. Morais, A. Lynch, S. Tavaré, and M. Ritchie 2008) are used to process data.frames. The \(\log_2\)
summarized expression intensities, obtained from Brain and Universal
Human Reference RNA (UHRR) samples in the data set exampleSummaryData
will be used. There are 12 arrays, 6 for the Brain samples and 6 for the
UHRR samples. The spike-in dataset consists of 48 arrays and an array
contained \(\sim\) 48,000 bead types (non-spikes) and 33 bead types
(spikes) targeting bacterial and viral genes absent in the mouse genome.
We use the same subset of the spike-in data as in (Kim and J. Lin 2011).
This dataset includes 34,654 bead types targeting annotated genes with
Genebank IDs and the 33 spikes. The data can be downloaded from
https://ephpublic.aecom.yu.edu/sites/rkim/Supplementary/files/KimLin2010.html
with the folder name “A normalized dataset for example analysis". We
also show how the downstream analysis can be performed upon filtering
using the spike-in data. Filtering of LumiBatch objects is illustrated
using the BeadStudio output used in the vignette”beadsummary” from the
beadarray package, which can be downloaded from
http://www.switchtoi.com/datasets.ilmn.
beadtypeFilter function
This subsection shows how to use the beadtypeFilter function to filter
normalized ExpressionSetIllumina objects, LumiBatch objects and
data.frames. For an ExpressionSetIllumina, this is done using the
exampleSummaryData data set from the beadarrayExampleData package.
# Normalize the log2 transformed data
> library("beadarrayFilter")
> data("exampleSummaryData", package = "beadarrayExampleData")
> exampleSummaryDataNorm <- normaliseIllumina(channel(exampleSummaryData, "G"),
+ method = "quantile", transform = "none")
# Filter the ExpressionSetIllumina
> iccResults <- beadtypeFilter(exampleSummaryDataNorm, Quantile = 1,
+ keepData = TRUE, delta = 0.5)By specifying iccQuant = 1 and delta = 0.5, the bead types are
filtered using the maximum ICC at a cutoff of 50%. The output of the
beadtypeFilter function can then be observed as follows:
> head(iccResults$InformProbeNames)
[1] "ILMN_1802380" "ILMN_1736104" "ILMN_1792389" "ILMN_1705423" "ILMN_1697642"
[6] "ILMN_1788184"
> exprs(iccResults$informData)[1:6, 1:5]
4613710017_B 4613710052_B 4613710054_B 4616443079_B 4616443093_B
ILMN_1802380 8.216547 8.229713 8.097047 8.343822 8.249190
ILMN_1736104 5.317065 5.470957 5.054653 5.100678 5.446530
ILMN_1792389 6.725049 7.003632 6.783809 7.214921 7.257032
ILMN_1705423 5.496207 4.845898 5.394206 5.422772 5.479191
ILMN_1697642 7.977234 7.912246 7.668253 7.850134 7.758535
ILMN_1788184 5.291988 5.614500 5.565426 5.473346 5.573395
> head(fData(iccResults$informData))
ArrayAddressID IlluminaID Status
ILMN_1802380 10008 ILMN_1802380 regular
ILMN_1736104 10017 ILMN_1736104 regular
ILMN_1792389 10019 ILMN_1792389 regular
ILMN_1705423 10039 ILMN_1705423 regular
ILMN_1697642 10044 ILMN_1697642 regular
ILMN_1788184 10048 ILMN_1788184 regular
> dim(exampleSummaryDataNorm)
Features Samples Channels
49576 12 1
> dim(iccResults$informData)
Features Samples Channels
23419 12 123419 out of the 49576 bead types were declared informative using the maximum ICC at a cutoff point of 50%.
For a LumiBatch object, filtering is illustrated using the
non-normalized data, an output of BeadStudio used in the “beadsummary”
vignette from the beadarray package. The data file,
AsuragenMAQC_BeadStudioOutput.zip, can be downloaded from
http://www.switchtoi.com/datasets.ilmn. Once the file has been
downloaded, unzip its content to your R working directory.
> require(lumi)# Set the working directory to the directory where the unzipped data file was saved.
> setwd("C:/Multi_level_Illumina_feb2011/RPackageFinal/beadstudiooutputData")
# Read in the data using lumiR to obtain a LumiBatch object
> x.lumi <- lumiR("AsuragenMAQC-probe-raw.txt")
# Normalize the data without any further transformation step
> lumi.N <- lumiN(x.lumi, "rsn")
# Filter the LumiBatch
> iccResult <- beadtypeFilter(lumi.N, Quantile = 1, keepData = TRUE, delta = 0.5)By specifying iccQuant = 1 and delta = 0.5, the bead types are
filtered using the maximum ICC at a cutoff of 50%.
> dim(lumi.N)
Features Samples
48701 6
> dim(iccResult$informData)
Features Samples
1195 6Only 1195 of the 48701 bead types were declared informative using the maximum ICC at a cutoff of 50%. This may be due to the way the data was summarized and normalized. How the processing of bead array data affects bead types filtering is a topic of future research.
For a data.frame, the beadtypeFilter function is illustrated using the
Illumina spike-in data. Read the data from the file location where the
data had been downloaded, unzipped and saved.
> filepath <- "C:/Multi_level_Illumina_feb2011/log2scale.normalized.txt"
> dt <- read.delim(filepath, header = TRUE, as.is = TRUE, row.names = NULL)[,-1]
> dt[1:6,1:5]
ProbeID X1377192001_A.AVG_Signal X1377192001_A.Detection.Pval
1 50014 6.150486 0.579207900
2 50017 6.616132 0.074257430
3 50019 8.164317 0.000000000
4 50020 7.414991 0.001856436
5 50022 5.804593 0.974628700
6 50025 6.412067 0.173267300
X1377192001_A.Avg_NBEADS X1377192001_A.BEAD_STDERR
1 27 0.09889349
2 40 0.05644992
3 25 0.06384269
4 27 0.07853792
5 38 0.08098911
6 28 0.08153830Note, that the data.frame supplied to the beadtypeFilter function
should contain the summarized intensities (eSet), standard errors
(seSet) and the number of beads used for the summarization (nSet).
When using a data frame, column names should be conform to BeadStudio
output, i.e., the column names for eSet should end on "Signal", those
for seSet on "STDERR" and the columns corresponding to nSet should end
on "NBEADS". It is preferable to use an identification variable with a
unique ID for each bead type. In the spike-in data, the spikes all have
the same TargetID, thus the ProbeID is preferred. Similar to the
ExpressionSetIllumina example, the beadtypeFilter function is used for
filtering.
> iccResults <- beadtypeFilter(dt, Quantile = 0.5, keepData = TRUE, delta = 0.5)By specifying Quantile = 0.5, bead types are filtered using the median
ICC.
> head(iccResults$InformProbeNames)
[1] 50280 50440 70594 110138 110685 130402
> dim(dt)
[1] 34687 193
> dim(iccResults$informData)
[1] 238 193238 of the 34687 bead types were declared informative based on thresholding the median ICC at a cutoff of 50%. A large number of bead types have been filtered out. It should be noted that this is probably due to the artificial nature of the spike-in data and we would expect lesser bead types to be filtered out in real life data.
iccFun function
The examples in this section show how the iccFun function can be used
to process different data types, observe its output and assess the
filtering strategies.
Processing data using the iccFun function
Processing a data.frame
> filepath <- "C:/Multi_level_Illumina_feb2011/log2scale.normalized.txt" > dt <- read.delim(filepath, header = TRUE, as.is = TRUE, row.names = NULL)[,-1] > eSet <- dt[, grep("Signal", names(dt))] > seSet <- dt[, grep("STDERR", names(dt))] > nSet <- dt[, grep("NBEADS", names(dt))] > ProbeID <- dt[, 2] > iccResults <- iccFun(eSet, seSet, nSet, ProbeID = ProbeID, iccQuant = c(0, 0.25, 0.5, 0.75, 0.8, 1), diffIcc = TRUE, keepData = TRUE)Processing a LumiBatch object
> setwd("C:/Multi_level_Illumina_feb2011/RPackageFinal/beadstudiooutputData") # Read in the data using \code{lumiR} to obtain a LumiBatch object > x.lumi <- lumiR("AsuragenMAQC-probe-raw.txt") > lumi.N <- lumiN(x.lumi, "rsn") > eSet <- exprs(lumi.N) > seSet <- se.exprs(lumi.N) > nSet <- beadNum(lumi.N) > group <- c(1:dim(eSet)[2]) > ProbeID = fData(lumi.N)$ProbeID > iccResults <- iccFun(eSet, seSet, nSet, ProbeID = ProbeID, + iccQuant = c(0, 0.25, 0.5, 1), + diffIcc = TRUE, keepData = TRUE)Processing an ExpressionSetIllumina object
> exampleSummaryDataNorm <- + normaliseIllumina(channel(exampleSummaryData, "G"), + method = "quantile", transform = "none") > aaa <- + na.omit(data.frame(I(rownames(exprs(exampleSummaryDataNorm))), + exprs(exampleSummaryDataNorm))) > ProbeID <- aaa[, 1] > eSet <- na.omit(exprs(exampleSummaryDataNorm)) > stddev <- na.omit(se.exprs(exampleSummaryDataNorm)) > nSet <- na.omit(attributes(exampleSummaryDataNorm)$assayData$nObservations) > seSet <- stddev/sqrt(nSet) > iccResults <- iccFun(eSet, seSet, nSet, ProbeID = ProbeID, + iccQuant = c(0, 0.25, 0.5, 1))
The output of the iccFun function
In this subsection, we illustrate how the output of the iccFun
function can be observed. Note that we display the results for
exampleSummaryData, the output for the spike-in data can be found in
Forcheh, G. Verbeke, A. Kasim, D. Lin, Z. Shkedy, W. Talloen, H. W. Göhlmann, and L. Clement (2012).
> head(iccResults$betweenvar)
ProbeID fit1.tau2
1 ILMN_1802380 1.3154886475
2 ILMN_1893287 0.0202718744
3 ILMN_1736104 0.7883136626
4 ILMN_1792389 0.5374776179
5 ILMN_1854015 0.0000000000
6 ILMN_1904757 0.0004272419
> iccResults$withinvar[1:6, 1:6]
ProbeID sigma2.4613710017_B sigma2.4613710052_B sigma2.4613710054_B
ILMN_1802380 ILMN_1802380 0.08024396 0.1133679 0.07562057
ILMN_1893287 ILMN_1893287 0.15510050 0.1495736 0.31645854
ILMN_1736104 ILMN_1736104 0.22109680 0.2449570 0.16237022
ILMN_1792389 ILMN_1792389 0.16305881 0.2232660 0.25316536
ILMN_1854015 ILMN_1854015 0.31302729 0.1367953 0.29684239
ILMN_1904757 ILMN_1904757 0.11065525 0.2427457 0.35319329
sigma2.4616443079_B sigma2.4616443093_B
ILMN_1802380 0.1715118 0.1282700
ILMN_1893287 0.4629203 0.1956166
ILMN_1736104 0.2781976 0.2364219
ILMN_1792389 0.1187983 0.1560972
ILMN_1854015 0.2776505 0.4338320
ILMN_1904757 0.2621982 0.4215812
> head(iccResults$iccAll)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.942507641 0.920658321 0.945640089 0.884659197 0.911155532 0.945550561
[2,] 0.115593318 0.119354803 0.060202088 0.041954058 0.093899753 0.076838800
[3,] 0.780964425 0.762930437 0.829206926 0.739151757 0.769284994 0.672257600
[4,] 0.767237213 0.706516107 0.679798133 0.818981163 0.774938161 0.794058983
[5,] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[6,] 0.003846168 0.001756947 0.001208193 0.001626811 0.001012401 0.003354805
[,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0.813348960 0.2851197747 0.924933768 0.921820518 0.953747680 0.9672588084
[2,] 0.146496212 0.0086476707 0.079882094 0.046679979 0.083112294 0.0577318494
[3,] 0.862368982 0.4039671466 0.788168517 0.745235220 0.651296881 0.5468796285
[4,] 0.795226413 0.2446849630 0.818919242 0.889560842 0.859993713 0.7440351650
[5,] 0.000000000 0.0000000000 0.000000000 0.000000000 0.000000000 0.0000000000
[6,] 0.002726754 0.0005326228 0.001815729 0.005423024 0.002744552 0.0009235276
> head(iccResults$icc)
ProbeID q0 q0.25 q0.5 q1
1 ILMN_1802380 0.2851197747 0.904531448 0.923377143 0.967258808
2 ILMN_1893287 0.0086476707 0.054968882 0.078360447 0.146496212
3 ILMN_1736104 0.4039671466 0.667017420 0.754082829 0.862368982
4 ILMN_1792389 0.2446849630 0.734655400 0.784498572 0.889560842
5 ILMN_1854015 0.0000000000 0.000000000 0.000000000 0.000000000
6 ILMN_1904757 0.0005326228 0.001159245 0.001786338 0.005423024If desired, the iccResults$icc output from the iccFun function can
be used for filtering and assessing the different filtering strategies.
# Obtaining the number of informative bead types at each of the specified ICC quantiles
> apply(iccResults$icc[, -1], 2, function(x, thres) sum(x >= thres), thres = 0.5)
q0 q0.25 q0.5 q1
4699 15784 17757 23419
# Obtaining the informative bead types using the minimum ICC
> filterDataNorm <- exampleSummaryDataNorm[subset(iccResults$icc,
+ iccResults$icc[, 2] >= 0.5)[, 1], ]
> dim(filterDataNorm)
Features Samples Channels
4699 12 1Assessing the filtering strategies
This is done using the emCDF function (Figure 1).
> emCDF(iccResults, iccQuant = c(0, 0.25, 0.5, 1))
Further, the within- and between-array variances at the minimum ICC can
be observed using the varianceplot function (Figure 2).
> varianceplot(iccResults, q = 1, delta = 0.8)
By specifying q = 1 and delta = 0.8, the informative beads
(displayed in red) are obtained using the minimum ICC at a cutoff of
80%.
Analysis of differential expression
Once the data have been filtered, they can be used for downstream
analysis. Here, we assess differential expression using a filtered
data.frame. For the example 608 bead types were declared informative
based on the maximum ICC. We refer to the help file of the
MLM.beadarray function in the beadarrayFilter package for an example
on a ExpressionSetIllumina object.
> iccResults <- beadtypeFilter(dt, Quantile = 1, keepData = TRUE, delta = 0.5)
> dim(iccResults$informData)
> dat <- iccResults$informData
> eSet <- dat[, grep("Signal", names(dat))]
> seSet <- dat[, grep("STDERR", names(dat))]
> nSet <- dat[, grep("NBEADS", names(dat))]We define the group variable to compare concentrations 0.3 and 0.1 pM in the spike-in data. This is done by selecting the column numbers of the arrays corresponding to the concentrations of interest.
> group1 <- c(26, 32, 38, 44)
> group2 <- c(27, 33, 39, 45)
> fit1 <- MLM.beadarray(eSet, seSet, nSet, list(group1, group2),
+ var.equal = TRUE, max.iteration = 20, method = "ML")The output of the MLM.beadarray function can then be used to test for
equality of mean expression between the two concentrations
> df <- length(group1) + length(group2) - 2
> fit1$pvalue <- 2 * (1-pt(abs(fit1$t.statistics), df))
> fit1$pvalAdjust <- p.adjust(fit1$pvalue, method = "fdr", n = length(fit1$pvalue))
> length(which(fit1$pvalAdjust < 0.05))
[1] 29i.e., 29 bead types were found to be differentially expressed between concentrations 0.3 and 0.1 pM. Note that 22 of these 29 bead types are true positives (spikes).
5 Discussion
The beadarrayFilter package can be used to filter Illumina bead array
data. The beadtypeFilter function can filter normalized
ExpressionSetIllumina objects, normalized LumiBatch objects as well as
normalized data.frames and returns the names of the informative bead
types. Optionally, the user can also obtain the filtered data. This,
however, does not return the required outputs to assess different
filtering strategies nor the variances using the emCDF or the
varianceplot functions, respectively. The iccFun function can be
used to customize filtering strategies. It returns the required outputs
for assessing different filtering strategies and the between- and
within-array variances.
6 Acknowledgements
We acknowledge the support from IAP research network grant nr. P6/03 of the Belgian government (Belgian Science Policy), SymBioSys, the Katholieke Universiteit Leuven center of Excellence on Computational Systems Biology, (EF/05/007), and Bioframe of the institute for the Promotion of Innovation by Science and technology in Flanders (IWT: 060045/KUL-BIO-M$S-PLANT).
We are also grateful to (Kim and J. Lin 2011) for making the MLM.beadarray
function available.
CRAN packages used
CRAN Task Views implied by cited packages
Note
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