A Java-based tool for the design of classification microarrays.

Abstract

Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria.
The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID.
In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.

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BioMed Central
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BMC Bioinformatics
Open Access
Software
A Java-based tool for the design of classification microarrays
Da Meng1, Shira L Broschat*1,2,3 and Douglas R Call2,3
Address: 1School of Electrical Engineering and Computer Science, Washington State University, Pullman, USA, 2Department of Veterinary
Microbiology and Pathology, Washington State University, Pullman, USA and 3Center for Integrated Biotechnology, Washington State University,
Pullman, USA
Email: Da Meng - dmeng@eecs.wsu.edu; Shira L Broschat* - shira@eecs.wsu.edu; Douglas R Call - drcall@vetmed.wsu.edu
* Corresponding author
Abstract
Background: Classification microarrays are used for purposes such as identifying strains of
bacteria and determining genetic relationships to understand the epidemiology of an infectious
disease. For these cases, mixed microarrays, which are composed of DNA from more than one
organism, are more effective than conventional microarrays composed of DNA from a single
organism. Selection of probes is a key factor in designing successful mixed microarrays because
redundant sequences are inefficient and limited representation of diversity can restrict application
of the microarray. We have developed a Java-based software tool, called PLASMID, for use in
selecting the minimum set of probe sequences needed to classify different groups of plasmids or
bacteria.
Results: The software program was successfully applied to several different sets of data. The utility
of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a
virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover,
use of data from expression microarray experiments demonstrated the generality of PLASMID.
Conclusion: In this paper we describe a new software tool for selecting a set of probes for a
classification microarray. While the tool was developed for the design of mixed microarrays–and
mixed-plasmid microarrays in particular–it can also be used to design expression arrays. The user
can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-
based genetic algorithm), several probe ranking methods, and several different display methods. A
novel approach is used for probe redundancy reduction, and probe selection is accomplished via
stepwise discriminant analysis. Data can be entered in different formats (including Excel and
comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several
different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis
can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to
construct virtual microarrays with genomes from public databases, which can then be used to
identify an optimal set of probes.
Background
The majority of DNA microarrays in use today are created
from single genomes that do not reflect the genetic diver-
sity of a group of heterogeneous entities. Mixed-DNA
Published: 4 August 2008
BMC Bioinformatics 2008, 9:328doi:10.1186/1471-2105-9-328
Received: 30 April 2008
Accepted: 4 August 2008
This article is available from: http://www.biomedcentral.com/1471-2105/9/328
© 2008 Meng et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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microarrays offer an alternative for "capturing" genetic
diversity and can be used for classification purposes such
as identifying pathogens or determining genetic relation-
ships for epidemiology studies [1-4]. DNA from one or
more reference strains or plasmids is shotgun-cloned, and
a mixed-genome or mixed-plasmid microarray is gener-
ated from randomly selected, PCR-amplified clone inserts
[2,3]. Unlike most fingerprinting tools, the mixed-array
format permits identification of informative probes that
can be retrieved from the clone library for sequencing [5].
However, redundant sequences and limited representa-
tion of diversity can limit the application of these tools
[3,4]. Fortunately, a growing public database of genomes
offers a new opportunity to incorporate non-redundant
and diverse sequences into a mixed-microarray format.
These arrays can be used to quickly assess the distribution
of genetic diversity across multiple species and niches.
This work focuses on the optimal design of classification
arrays. By optimal we mean minimizing the complexity
and cost of an array by using as few probes as possible
while still rendering sufficient information to discrimi-
nate between strains or groups of organisms and to avoid
bias; the goal is to remove irrelevant probes (probes that
contain no useful information) and reduce the number of
redundant probes (probes that contain the same informa-
tion) in such a way that the chosen probes will allow us to
perform the desired classification task accurately. Selec-
tion of an optimal set of probes is a key factor in designing
a successful mixed microarray to suit a particular need.
The effects of probe length and the number of probes per
gene have been discussed in [6]. A method for finding
unique and valid oligonucleotides or probes was pro-
posed in [7], which tries to identify probes for a gene such
that there is no similar occurrence in other locations of a
genome. A tool for choosing optimal DNA oligos is
reported in [8], which identifies oligo sequences that
occur in members of the target group but not in the non-
target group. However, these methods are used for
genome-wide probe selection and are not intended to
identify minimum probe sets for classification problems.
A number of methods have been introduced for designing
optimal probe sets. Pre-filtering methods [9] use cluster-
ing of all probes to find similar probe groups. Similar
probes are discarded; the remaining probes are ranked,
and top-ranked probes are kept for further analysis. A sim-
ilar method [10] uses K-means to cluster all genes, and the
means of different gene clusters are used as prototype
genes. The limitation of these methods is that the number
of clusters must be specified. A hybrid approach [11]
ranks the probes first and selects a set of top-ranked
probes. Hierarchical clustering is then used on these
probes to generate a dendrogram. The optimal probes are
selected by collapsing dense clusters. In this manner a
small set of probes is identified that has a similar predic-
tion accuracy to one that uses more probes.
The methods described above identify optimal probes
using training data when the structure of the data is given.
Such information, however, is usually unavailable for
microarray data sets. A tool is still needed to help design
mixed microarrays when prior knowledge of a microarray
data set is unavailable. The focus of this paper is a software
program, PLASMID, used for selecting an optimal set of
probe sequences without a priori knowledge that will ena-
ble correct classification of groups of plasmids or bacteria.
Data used to identify probe candidates can be either exist-
ing microarray data (or similar hybridization data) or
sequence data from a public database such as GenBank.
The latter are converted to "probe" sequences, and virtual
hybridization is used to generate data for probe selection
[1]. To demonstrate the generality of PLASMID, we
include an example whereby the program can also be
applied to develop a minimum probe set to distinguish
between two classes of leukemia using data from an
expression array.
Methods
Finding meaningful clusters in hybridization data
Finding meaningful clusters of samples (e.g., plasmids)
from a given set of hybridization or sequence data is the
starting point for the design of an optimal microarray; our
tool provides several clustering options. Clustering meth-
ods can be divided into two general groups: distance-
based methods and model-based methods. Distance-
based methods are either non-hierarchical or hierarchical,
and each method has its particular strengths and weak-
nesses. Currently our tool includes the K-means non-hier-
archical clustering algorithm and hierarchical clustering
by means of Unweighted Pair Group Method with Arith-
metic mean (UPGMA), neighbor joining, or Ward's mini-
mum variance method, all of which are widely used in
microarray data analysis [12]. Two distance metrics have
been implemented, Euclidean distance and Pearson's cor-
relation coefficient, from which users can choose. The dis-
tance-based methods listed above are standard clustering
techniques. In addition to these, we have also imple-
mented the model-based clustering method described
below.
Model-based genetic clustering
Distance-based methods are simple to use, and the clus-
tering results are easy to explain. However, it is hard to
obtain information about the number of clusters, the con-
fidence level of the clustering results, and so on, from
these methods. To avoid some of these issues, model-
based clustering methods can be used as an alternative.
Model-based clustering methods assume that the data can
be clustered according to a set of underlying distributions.

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These underlying distributions can be modeled, and find-
ing a suitable model can be construed as an optimization
problem. We assume that M is the underlying model for a
data set represented by a matrix X where each row of the
matrix represents the data for a given sample (e.g., plas-
mid). The best clustering result is represented by partition
P of X. A measure is used to determine which P is most
likely for X. In our tool the measure is the likelihood of all
possible partitions P. A number of different optimization
methods can be used to find the solution for P. In our
tool, we have chosen to use a genetic algorithm because of
its simplicity and efficiency in addition to its ability to
find the optimal solution. Usually model-based clustering
methods are based on the Expectation-Maximization
(EM) method. However, EM algorithms tend to break
down for microarray data because an inversion of the cov-
ariance matrix must be performed. In genetic algorithms,
a search method is used to circumvent the need for this
computation, thereby making genetic model-based meth-
ods more stable.
To find the best partition P we want to maximize the pos-
terior probability f(P|X). According to Bayes' theorem,
fP f P
f
( )X
where f(P) is the prior probability.
Recasting Bayes' theorem in terms of the likelihood
(X|P) gives f(P|X) ∝ (X|P) f (P)–that is, the posterior
probability is proportional to the product of the likeli-
hood and prior probability. Now if we assume a uniform
distribution for P, then f(P) is constant and maximizing
the posterior probability f(P|X) is equivalent to maximiz-
ing the likelihood (X|P).

If we assume the rows of the matrix X in each cluster of the
partition are independent and identically distributed, we
can compute the likelihood of a partition. For this work,
we assume the rows in each cluster are normally distrib-
uted with mean μi and variance
mal distribution for all μi and an inverse-Γ distribution for
σi
, and we assume a nor-
all . This leads to:
where k is the index of clusters, j is the index of probes, nk
is the number of samples in the kth cluster, ki is the index
of samples in the kth cluster, and μ0 and
all mean and variance of all the data [13].
are the over-
Using this as a measure, the genetic algorithm is used to
find the partition that maximizes the likelihood. The steps
of the genetic algorithm are summarized as follows:
1. Generate N random partitions. Each partition is repre-
sented by a vector [1 2 1 ?] where each term is the index
of a cluster.
2. Prior knowledge of pairs of samples highly unlikely to
be in the same cluster can be incorporated into the parti-
tion likelihood by creating a text file with each pair of
samples, together with a small weighting factor, on one
line. The weighting factor must be smaller than 1, but how
much smaller has to be determined empirically based on
the end result. A weighting factor of zero indicates that the
pair cannot be in the same cluster.
3. Compute the likelihood for all partitions.
4. Repeat the following steps until the the maximum iter-
ations (Max) has been reached or the difference between
the likelihood of two successive iterations is less than ε,
where Max and ε are given.
(a) Select the two partitions with the highest scores.
(b) Do crossover and mutation on these two partitions to
generate new partitions. Crossover is accomplished by
randomly selecting sections of equal length from each
partition and exchanging them. Mutation is performed
following crossover and is accomplished by randomly
selecting one term in each of the partitions and changing
it to a different value.
(c) Compute the likelihood
tions (offspring).
for these two new parti-
(d) Replace the two lowest-ranked partitions with the off-
spring.
Other measures can be used including Bayesian Informa-
tion Criteria and minimum description length. These
measures will be included in future versions of PLASMID.
Probe ranking for classification
In a DNA microarray data set there are usually many more
probes than the number of samples (e.g., plasmids) to be
classified, and often some probes either convey no useful
information or convey the same information. Thus, in the
design of an optimal probe set for sample classification,
one objective is to identify and remove irrelevant and
redundant probes. In this section, we describe our method
for removing irrelevant probes; in the next section redun-
dancy reduction is described.
f P
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Irrelevant probes are removed using probe ranking on the
clusters of samples obtained in the previous step. There
are two basic approaches to probe ranking: filter tech-
niques and wrapper techniques. Because of their simplic-
ity, filter procedures are used most commonly for DNA
microarrays. The filter procedure ranks each probe using a
metric based on its classification relevance. Top-ranked
probes are then selected to perform classification. Numer-
ous filter metrics are described in the literature [14]: prob-
abilistic and distance metrics, dependence measures,
scores based on information theory, etc. In our tool, filter
metrics are determined using two different statistical tests,
the ANOVA-F and Brown-Forsythe tests. Other tests con-
sidered were the Welch, adjusted Welch, Cochran, and
Kruskal-Wallis test statistics [15].
The test statistic is used as a metric to evaluate the discrim-
inating power of a probe. Higher values represent more
discriminating probes. For some applications, clusters
may include an insufficient number of samples for mean-
ingful statistical analysis. Such cases can be handled by
generating random samples that differ only slightly from
the original samples. These samples can be included in the
statistical analysis and then discarded without compro-
mising the probe ranking procedure. The purpose of add-
ing these samples is for computational convenience only;
they do not add more information.
The end result of the probe ranking function is a list of all
probes ranked by their classification relevance. At this
point, the user can either stop and use some chosen
number of the top-ranked probes for the array probe set
or continue with probe reduction and stepwise discrimi-
nant analysis to remove redundant probes and assign
weights to the probes.
Stepwise discriminant analysis
Probe ranking is used to remove irrelevant probes that
convey little or no information. Nevertheless, while the
top-ranked probes are informative, at least some of them
are likely to convey redundant information. The next task
is to remove this unnecessary redundancy. K-means clus-
tering is usually used to cluster samples (e.g., plasmids) as
described in an earlier section, but here we use it in a
novel way to cluster probes. A set of top-ranked probes is
clustered into κ groups where the value of κ is evaluated
empirically to maximize classification accuracy; probes in
the same group are highly correlated with each other but
uncorrelated or loosely correlated with probes in other
groups. The probe closest to the center of a group is cho-
sen to be representative of that group, and the κ represent-
ative probes are used with stepwise discriminant analysis
(SDA) [16] which identifies the optimal probe set from
the κ probes. At each step of the SDA, an
computed for each probe; this value is used to determine
whether including the probe or excluding the probe from
will significantly improve sample differentiation. The
SDA process starts with an empty probe set
ative process of adding a probe to
from continues until no probes can be added or

statistic is
, and an iter-
or removing a probe
removed.
is used for the probes not in
is used for the probes in , and
. The probe in with the
smallest value of less than a chosen threshold
value, usually 1.0, is removed; the probe not in
the largest value of
enter

with
greater than the threshold value
is added to . The formulas used to compute are:
values:
Wilks' Λ:
Within-group covariance matrix:
Among-group covariance matrix:
where q is the number of clusters, nm is the number of
samples in the cluster m, xmki is the value of the ith probe
for the kth sample in the mth cluster, n is the total number
of samples, r is the number of probes currently included
in , |p denotes a new group of probes which is
obtained by adding the probe p to
new group of probes which is obtained by removing the
probe p from .

, and \p denotes a





remove
enter

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
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


remove
n r q
q
p
=
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−
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


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At the conclusion of SDA, the optimal probe set is deter-
mined based on the prediction accuracy of the selected
probes. Because there are typically a small number of sam-
ples associated with microarray data, prediction accuracy
is computed using the leave-one-out (LOO) cross valida-
tion method [11,15]. The set of probes associated with the
highest LOO predication accuracy is written to a file
together with its associated weights. It is important to note
that when SDA is used to obtain the final probe set, the
weights associated with the probes must be used for clas-
sification of new empirical data obtained using the
probes. The probes should not be treated with equal
weight.
Probe selection for a classification microarray
In summary, the steps in our design of an optimal probe
set are:
1. Cluster the samples (e.g., plasmids) using microarray or
sequence data and select clusters of interest using a hierar-
chical, non-hierarchical, and/or model-based method. A
priori clustering is also permitted.
2. Use the probe ranking procedure with the sample clus-
ters to rank the probes for relevance.
3. Repeat K-means clustering of probes for probe reduc-
tion until satisfied:
(a) Select j top-ranked probes.
(b) Repeat for κ in a chosen range:
i. Cluster the j top-ranked probes into κ clusters.
ii. Choose κ representative probes, one from each cluster.
iii. Use SDA to find a set of probes from the κ representa-
tive probes and compute the LOO prediction accuracy.
4. Save the set of probes associated with the highest LOO
prediction accuracy together with its weights. After con-
structing the optimized microarray, a set of independent
control samples should be hybridized to empirically
assess the accuracy of the microarray results.
A flowchart of the process is shown in Fig. 1. It should be
pointed out that the optimal number of probes computed
by this process does not take into account the effects of
noise and other random experimental effects. The sample-
to-feature (SFR) ratio gives the minimum number of
probes that should be used to create a microarray. The rule
of thumb is given by [17]:
In this paper we refer to features as probes. The SFR should
be used in conjunction with the results to choose the opti-
mal probe set.
System Overview and Implementation
Our software tool PLASMID is implemented as a Java
application. The NetBeans platform was chosen for devel-
opment because addition of new functions is easily imple-
mented. Also, many of the tasks common to desktop
applications are provided by NetBeans. These include user
interface management (e.g., menus and toolbars), user
settings management, storage management (saving and
loading any kind of data), window management, and wiz-
ard framework (supporting step-by-step dialogs). Each
SFR
number of samples
number of features

=≤


1
5.
Flowchart of PLASMID
Figure 1
Flowchart of PLASMID. Flowchart of the probe selection
process using PLASMID.
Input Data Matrix
Sample Clustering:
• Non-hierarchical
• Hierarchical
• Model-based
Probe Ranking:
• ANOVA-F
• Brown-Forsythe
Redundancy Reduction:
Probe Clustering
Probe Selection:
Stepwise Discriminant
Analysis
Completion of Probe Set with
Linear Model Coefficients (if
appropriate)
A priori
clusters

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function is implemented as a NetBeans module and can
be installed or removed easily without affecting existing
functions. Java is a platform-independent programming
language, so although PLASMID has been developed
using the Windows operating system, it will be relatively
easy to adapt it to other operating systems. We intend to
extend PLASMID to both the Linux and Mac OS X operat-
ing systems. In addition to Java, PLASMID uses code writ-
ten using the C++ programming language. C++ is needed
for computationally intensive tasks that require greater
speed and efficiency. The use of two different program-
ming languages is transparent to the user.
PLASMID provides an integrated environment for design-
ing an optimal classification microarray. As such, PLAS-
MID v0.91 includes the following services:
1. Loading and management of different kinds of input
data, including plasmid sequence data, hybridization
data, virtual hybridization data, and probe sequences.
Data may be in tab-delimited or comma-delimited text
format or in Microsoft Excel spreadsheet format.
2. Different methods for processing hybridization data.
The tool provides several data preprocessing methods,
including normalization and noise filtering. It also pro-
vides hierarchical, non-hierarchical, and model-based
methods for clustering samples; two different statistical
tests for ranking probes; use of K-means clustering for
reduction of probe redundancy; and stepwise discrimi-
nant analysis with assignment of weights to probes.
3. Design of mixed arrays using existing hybridization
data or virtual hybridization data. An optimal set of
probes is identified, and weights associated with each
probe are stored for analysis of experimental results.
4. Construction of virtual microarrays to obtain virtual
hybridization data using genomes from the National
Center for Biotechnology Information (NCBI) database.
Genomes for probes can be chosen by accession number
or by gene sequence.
5. Visualization of microarray data and data processing
results, including dendrograms, heat maps, and scatter
plots. Plots can be saved in different image formats.
6. Automatic probe design after the user has specified the
parameters. A step-by-step wizard guides the user through
the various steps.
Experimental data obtained from microarrays designed
using PLASMID can be used as input data and analyzed
using the weighted classification function obtained in 3.
Results and Discussion
In this section we present results obtained using PLASMID
to analyze a mixed-plasmid microarray data set [4] and a
simulated mixed-genome microarray data set [1]. We also
present results for publically-available leukemia expres-
sion array data [18]. For this latter data set, clusters (i.e.,
types of leukemia) are pre-assigned so only probe ranking,
reduction of probe redundancy, and stepwise discrimi-
nant analysis (SDA) are used to determine the optimal
probe set. PLASMID's performance in probe selection is
evaluated using the leave-one-out (LOO) approach for
which one sample is excluded and the remaining samples
are used to obtain the discriminant functions. Each sam-
ple is, in turn, excluded and a corresponding set of discri-
minant functions is used to classify it. The prediction
accuracy, the percentage of times the withheld samples are
correctly classified, is used as the performance metric.
Mixed-plasmid microarray data
A mixed-plasmid microarray has been used to compare
the genetic composition of plasmids [4]. The microarray
consists of 576 probes composed of randomly selected
fragments of plasmid DNA, and the data were obtained
from hybridization experiments with 43 plasmids. The
data are composed of hybridization signal intensities for
each microarray probe [see Additional file 1].
First we used the Ward's minimim variance hierarchical
clustering algorithm to create a dendrogram. To test the
two-class problem, we divided the dendrogram into two
clusters. One cluster consisted of 15 plasmids which, with
one exception (the peSSuTet plasmid), have the blaCMY-2
antibiotic resistance gene; the other cluster consisted of 28
plasmids. We then used the probe ranking function,
choosing the ANOVA-F test statistic, and generated a scat-
ter plot (Fig. 2). The scatter plot shows that the majority of
the probes have statistical values close to zero and, thus,
that ANOVA-F test statistics can be used to distinguish
between informative (F > 0) and uninformative (F ≈ 0)
probes. This result also serves to highlight the need for
optimization algorithms, as the majority of probes pro-
vide limited discrimination.
For the two-cluster case, we chose 1, 20, and 200 top-
ranked probes for comparison. Using reduction of probe
redundancy and SDA, we found that a single probe (5-E3,
a transposase gene associated with the blaCMY-2 element
[19]) correctly classified all but two of the plasmids [4].
Interestingly, in the original study one of these two plas-
mids (pe1171sT) was classified with plasmids that harbor
the blaCMY-2 gene even though it does not carry this gene.
Analysis with PLASMID separated pe1171sT from the
blaCMY -2 plasmids. In addition, a different plasmid
(pe7594T) that harbors the blaCMY-2 gene was classified
with other blaCMY-2 positive plasmids. Thus, analysis using

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PLASMID more accurately reflects the phenotypic proper-
ties of the plasmids included in the study. The one excep-
tion was the peSSuT plasmid that was consistently
classified with blaCMY-2 plasmids while not harboring this
gene [8].
Next we divided the original dendrogram into five plas-
mid clusters and ranked probes as before. As expected, the
number of probe clusters κ specified for the reduction of
probe redundancy affects the prediction accuracy (Table
1). Small values of κ certainly reduce redundancy, but
they also reduce specificity. The optimal set of probes is
identified using SDA with the LOO method to determine
the highest prediction accuracy. In this case, the smallest
number of probes from the top-most ranked probes with
the highest prediction accuracy is 10. Thus, PLASMID
analysis reduced the original data set of 576 probes to 10
probes that are needed to accurately classify plasmids into
one of five groups. Non-hierarchical clustering followed
by probe ranking, probe reduction, and SDA gave similar
results (data not shown).
In addition to hierarchical and non-hiearchical clustering
methods, we can obtain classification results using our
model-based method, which is based on a genetic algo-
rithm. The genetic algorithm predicted that the most
likely number of plasmid clusters is five (Table 2). Com-
parison of Tables 1 and 2 shows that prediction accuracies
depend on the initial clustering method used. For this
case, the prediction accuracies for the model-based clus-
tering method are larger for a given number of probe clus-
ters than those obtained via the hierarchical method.
Furthermore, the variance in prediction accuracies is
lower as a function of the number of top-ranked probes
when clusters are initially assigned using the model-based
method. For other data sets, however, another clustering
model might give the best results.
Based on the sample-to-feature ratio (SFR), at least 9
probes (features) are required for classifying 43 plasmids
(samples). Tables 1 and 2 show several choices for 10
probes with equivalent performance. When additional
information is available, it should be used to assist with
the choice of a final set.
Virtual Streptococcus mixed-genome microarray data
A virtual Streptococcus mixed-genome microarray was con-
structed by Wan et al. [1]. To create the equally-repre-
sented, 4000-probe virtual array, 800 gene segments each
600-bp long were randomly selected from genomes of fif-
Scatter plot of ANOVA-F test statistics for the mixed-plas-mid microarray probes
Figure 2
Scatter plot of ANOVA-F test statistics for the
mixed-plasmid microarray probes. The scatter plot
shows that the majority of the probes have statistical values
close to zero and, thus, that ANOVA-F test statistics can be
used to distinguish between informative (F > 0) and unin-
formative (F ≈ 0) probes.
0100200300400500600
0
50
100
150
200
ANOVA−F statistics
probe index
statistic value
Table 1: Classification accuracy of mixed-plasmid data using hierachical clustering with five sample (plasmid) clusters. PA is the
prediction accuracy.
Number of clusters of probes, κ
25102030 40
Number of
top-ranked
probes
PA (%) No. of
probes
PA (%)No. of
probes
PA (%) No. of
probes
PA (%)No. of
probes
PA (%) No. of
probes
PA (%) No. of
probes
100
150
200
250
300
350
400
72.09
86.05
74.42
76.74
46.51
76.74
69.77
2
2
2
2
2
2
2
72.09
93.02
90.70
95.35
88.37
93.02
93.02
5
5
5
5
5
5
5
72.09
95.35
93.02
95.35
93.02
93.02
90.70
10
10
10
10
10
10
10
69.77
95.35
93.02
95.35
93.02
95.35
93.02
19
20
20
20
20
20
20
69.77
95.35
93.02
95.35
93.02
95.35
90.70
29
29
30
30
30
30
30
69.77
95.35
95.35
90.70
90.70
95.35
93.02
32
36
35
34
35
33
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teen strains of five bacterial species–that is, each species
was represented by 800 different probes. Virtual hybridi-
zation was accomplished using BLAST scores as proxies
for array probe intensities [see Additional file 2], and
PLASMID was used to analyze the data. In the initial anal-
ysis one bacterial species was excluded from the study
because it was represented by only a single strain (S.
mutans UA159). Because we knew a priori that the samples
belonged to four different species, the goal was to find an
optimal set of probes to classify these four. ANOVA-F tests
were used to rank the 4000 probes, and LOO analysis was
performed on different numbers of the highest ranked
probes. In fact, we found the LOO prediction accuracy to
be 100% for differentiating the four different species using
only the single top-ranked probe. On examination we
found that the hybridization values (BLAST scores) for
this probe for strains from different groups were well sep-
arated (i.e., different from each other), while the hybridi-
zation values for strains from the same group were very
similar. While it appears that successful classification can
be achieved with a single probe when classification relies
on differences in hybridization signal, given inherent
sources of variation in microarray hybridization, it would
be prudent to include additional probes to increase classi-
fication confidence for empirical data. For example, the
minimum recommended probe set in this case would be
3 according to the SFR.
In the second analysis, our model-based clustering
method identified two clusters, one with the two S. pneu-
moniae strains and the other with the remaining 13
strains. After probe ranking, reduction of probe redun-
dancy, and SDA, a single probe could be used to differen-
tiate these two groups. We also used non-hierarchical
clustering of the samples followed by probe ranking,
probe reduction, and SDA. When the number of clusters
was chosen to be k = 2, the result was identical to the result
obtained using our model-based cluster method. When
the number of clusters was chosen to be k = 3 or k = 4, the
two S. pneumoniae genomes were placed into different
groups. A dendrogram constructed using the neighbor
joining method shows a clear distinction between the two
S. pneumoniae samples and the remaining bacteria (Fig. 3).
When these two samples are excluded, PLASMID groups
the remaining thirteen samples correctly into four species
clusters. The results shown in Table 3 are obtained using
non-hierarchical clustering, probe ranking, probe reduc-
tion, and SDA. As this table illustrates, only 2 probes are
needed to obtain 100% prediction accuracy by species.
These 2 probes are from the genomes of S. pneumoniae
TIGR4 and either S. pyogenes M1 GAS or S. pyogenes
MGAS5005. Based on the SFR rule of thumb, at least 3
probes are needed. Several choices exist that suffice for
this condition.
For virtual microarrays, BLAST scores are used to obtain
hybridization intensities, and the accuracy of the scores
will affect the choice of an optimal probe set. While error
could be modeled from real data, the best measure of reli-
ability will be obtained using actual hybridization experi-
ments.
Public ALL/AML leukemia data
The ALL/AML leukemia data set, obtained from expres-
sion arrays, has been widely used in the literature. It con-
sists of two classes of leukemia, acute lymphoblastic
leukemia (ALL) and acute myeloblastic leukemia (AML),
and there are 72 samples (47 ALL and 25 AML) and 7129
probes. Table 4 shows prediction accuracy results after
probe ranking, probe redundancy reduction, and SDA
have been performed. When the top 50 probes were
selected, the highest accuracy was achieved when probes
were clustered into 10 groups. A set of 10 probes was iden-
tifed with a prediction accuracy of 97.22%. Using addi-
tional probes does not lead to improvement. According to
the SFR rule of thumb, at least 20 probes should be used
Table 2: Classification accuracy of mixed-plasmid data with model-based clustering. PA is the prediction accuracy.
Number of clusters of probes, κ
25 1020 30 40
Number of
top-ranked
probes
PA (%)No. of
probes
PA (%)No. of
probes
PA (%)No. of
probes
PA (%) No. of
probes
PA (%) No. of
probes
PA (%)No. of
probes
100
150
200
250
300
350
400
83.72
53.49
79.07
76.74
69.77
67.44
69.77
2
2
2
2
2
2
2
95.35
90.70
93.02
95.35
93.02
93.02
93.02
5
5
5
5
5
5
5
95.35
93.02
93.02
95.35
93.02
93.02
93.02
10
10
10
10
10
10
10
95.35
93.02
93.02
95.35
95.35
93.02
93.02
19
20
20
20
20
20
20
95.35
93.02
93.02
95.35
95.35
93.02
93.02
36
36
36
35
34
35
37
95.35
93.02
93.02
93.02
95.35
93.02
95.35
33
28
35
32
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in the actual microarray design; several choices of 20
probes exist and all produce robust prediction results
(Table 4).
Conclusion
In this paper we describe a new software tool, PLASMID,
for selecting an optimal set of probes for the design of a
classification microarray. The tool provides the user with
several clustering methods, a probe ranking method,
probe redundancy reduction, and probe selection using
stepwise discriminant analysis. Images can be saved in
several different formats, and weights generated using
SDA can be stored for use in analysis of experimental data.
In addition, PLASMID can be used to construct virtual
microarrays with genomes from public databases; these
can then be used to determine an optimal probe set for
use in actual microarray experiments. The software pack-
age has been applied to data from a mixed-plasmid micro-
array, a virtual mixed-genome microarray, and an
expression microarray. Robust results have been obtained
for all three sets of data.
Dendrogram for Streptococcus MGM data
Figure 3
Dendrogram for Streptococcus MGM data. The dendrogram constructed using the neighbor joining method shows a clear
distinction between the two S. pneumoniae samples and the remaining bacteria.
Table 3: Classification accuracy using mixed-genome array data with non-hierarchical clustering for four sample (bacterial species)
clusters. PA is the prediction accuracy.
Number of clusters of probes, κ
2510203040
Number of
top-ranked
probes
PA (%)No. of
probes
PA (%)No. of
probes
PA (%)No. of
probes
PA (%) No. of
probes
PA (%)No. of
probes
PA (%)No. of
probes
50
100
100
100
2
2
100
100
5
5
100
100
7
7
100
100
7
7
100
100
1
7
75
100
1
1

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Although many methods are available for determining a
set of probes for a given microarray data set, these meth-
ods require the classification information to be known in
advance. PLASMID was designed to be used prior to
implementation of a microarray when no such informa-
tion is available, although the program can also be used
when clusters are known a priori.
PLASMID can be obtained by following the link from
http://www.vetmed.wsu.edu/research_vmp/MicroArray
Lab/.
Availability and requirements
• Project name: PLASMID
• Project home page: http://www.vetmed.wsu.edu/
research_vmp/MicroArrayLab/
• Operating system: Windows but to be ported to Linux
and Mac OS X
• Programming languages: Java and C++ (with gcc com-
piler)
• Other requirements: Java Runtime Environment
• License: Free to academic and nonprofit organizations
Authors' contributions
DM and SLB developed PLASMID, DM was responsible
for programming PLASMID, and DRC provided the neces-
sary microbiology expertise. All authors have read and
approved the final manuscript.
Additional material
Acknowledgements
This project has been funded by the National Institute of Allergy and Infec-
tious Diseases, National Institutes of Health, Department of Health and
Human Services, under Contract No. NO-1-AI-30055; by the Agricultural
Animal Health Program, College of Veterinary Medicine, Washington State
University, Pullman, WA; and by the Carl M. Hansen Foundation.
References
1.Wan Y, Broschat SL, Call DR: Validation of mixed-genome
microarrays as a method for genetic discrimination. Appl
Environ Microbiol 2007, 73(51425-1432 [http://aem.asm.org/cgi/con
tent/abstract/73/5/1425].
2.Borucki MK, Krug MJ, Muraoka WT, Call DR: Discrimination
among Listeria monocytogenes isolates using a mixed genome
DNA microarray. Veterinary Microbiology 2003, 92(4):351-362.
3. Call DR, Borucki MK, Besser TE: Mixed-genome microarrays
reveal multiple serotype and lineage-specific differences
among strains of Listeria monocytogenes. J Clin Microbiol 2003,
41(2632-639 [http://jcm.asm.org/cgi/content/abstract/41/2/632].
4.Call D, Kang MS, Daniels J, Besser T: Assessing genetic diversity
in plasmids from Escherichia coli and Salmonella enterica
using a mixed-plasmid microarray. Journal of Applied Microbiology
Additional file 1
MPM. This file contains the information for the mixed-plasmid microar-
ray in the form of a 44-row × 577-column matrix. The first row gives the
576 probe names, and the first column gives the 43 plasmid names. The
remaining rows and columns contain the hybridization intensity data.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-328-S1.txt]
Additional file 2
MGM. This file contains the information for the mixed-genome virtual
microarray in the form of a 15-row × 4001-column matrix where the first
column gives the 15 names of the streptococcus strains. The probes were
not given specific names. All remaining columns contain the virtual
hybridization intensity data.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-328-S2.txt]
Table 4: Classification accuracy using ALL/AML leukemia data. PA is the prediction accuracy.
Number of clusters of probes, κ
25 1020 30 40
Number of
top-ranked
probes
PA (%)No. of
probes
PA (%) No. of
probes
PA (%)No. of
probes
PA (%) No. of
probes
PA (%) No. of
probes
PA (%)No. of
probes
50
100
150
200
250
94.44
88.89
83.33
79.17
79.17
2
2
2
2
2
94.44
95.83
95.83
80.56
79.17
5
5
5
5
5
97.22
94.44
97.22
97.22
97.22
10
10
10
10
10
97.22
97.22
97.22
97.22
97.22
20
20
20
20
20
97.22
97.22
97.22
97.22
97.22
30
30
30
30
30
97.22
97.22
97.22
97.22
97.22
39
40
39
39
39

Page 11

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BMC Bioinformatics 2008, 9:328http://www.biomedcentral.com/1471-2105/9/328
Page 11 of 11
(page number not for citation purposes)
2006,
10.1111/j.1365-2672.2005.02775.x].
Soule M, Kuhn E, Loge F, Gay J, Call D: Using DNA microarrays
to identify library-independent markers for bacterial source
tracking. Appl Environ Microbiol 2006, 72:1843-1851.
Chou CC, Chen CH, Lee TT, Peck K: Optimization of probe
length and the number of probes per gene for optimal
microarray analysis of gene expression. Nucl Acids Res 2004,
32(12e99 [http://nar.oxfordjournals.org/cgi/content/abstract/32/12/
e99].
Hyyro H, Juhola M, Vihinen M: Genome-wide selection of unique
and valid oligonucleotides. Nucl Acids Res 2005, 33(13e115 [http:/
/nar.oxfordjournals.org/cgi/content/abstract/33/13/e115].
Emrich SJ, Lowe M, Delcher AL: PROBEmer: a web-based soft-
ware tool for selecting optimal DNA oligos. Nucl Acids Res
2003, 31(133746-3750 [http://nar.oxfordjournals.org/cgi/content/
abstract/31/13/3746].
Jaeger J, Sengupta R, Ruzzo W: Improved gene selection for clas-
sification of microarrays. Pac Symp Biocomput 2003:53-64 [http://
citeseer.ist.psu.edu/jaeger02improved.html].
Hanczar B, Courtine M, Benis A, Hennegar C, Clément K, Zucker JD:
Improving classification of microarray data using prototype-
based feature selection. SIGKDD Explor Newsl 2003, 5(2):23-30.
Wang Y, Makedon FS, Ford JC, Pearlman J: HykGene: a hybrid
approach for selecting marker genes for phenotype classifi-
cation using microarray gene expression data. Bioinformatics
2005, 21(81530-1537 [http://bioinformatics.oxfordjournals.org/cgi/
content/abstract/21/8/1530].
Jagota A: Microarray Data Analysis and Visualization Bioinformatics By
The Bay Press; 2001.
Qin ZS: Clustering microarray gene expression data using
weighted Chinese restaurant process. Bioinformatics 2006,
22(161988-1997 [http://bioinformatics.oxfordjournals.org/cgi/con
tent/abstract/22/16/1988].
Su Y, Murali T, Pavlovic V, Schaffer M, Kasif S: RankGene: identifi-
cation of diagnostic genes based on expression data. Bioinfor-
matics 2003,
19(121578-1579
bioinformatics.oxfordjournals.org/cgi/content/abstract/19/12/1578].
Chen D, Liu Z, Ma X, Hua D: Selecting genes by test statistics.
J Biomed Biotechnol 2005, 2005:132-138.
Jennrich RI: Stepwise discriminant analysis. In Statistical Methods
for Digital Computers Volume III. Edited by: Enslein K. John Wiley & Sons
Inc; 1977:76-95.
Somorjai R, Dolenko B, Baumgartner R: Class prediction and dis-
covery using gene microarray and proteomics mass spec-
troscopy data: curses, caveats, cautions. Bioinformatics 2003,
19(121484-1491 [http://bioinformatics.oxfordjournals.org/cgi/con
tent/abstract/19/12/1484].
Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov
JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD,
Lander ES: Molecular classification of cancer: class discovery
and class prediction by gene expression monitoring. Science
1999, 286(5439531-537 [http://www.sciencemag.org/cgi/content/
abstract/286/5439/531].
Kang MS, Besser TE, Call DR: Variability in the region down-
stream of the blaCMY-2 β-lactamase gene in Escherichia coli
and Salmonella enterica plasmids. Antimicrob Agents Chemother
2006, 50(4):1590-1593.
100:15-28 [http://www.blackwell-synergy.com/doi/abs/
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
[http://
15.
16.
17.
18.
19.