Group Title: BMC Bioinformatics
Title: Evaluation of gene importance in microarray data based upon probability of selection
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Title: Evaluation of gene importance in microarray data based upon probability of selection
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Publisher: BMC Bioinformatics
Publication Date: 2005
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Abstract: BACKGROUND:Microarray devices permit a genome-scale evaluation of gene function. This technology has catalyzed biomedical research and development in recent years. As many important diseases can be traced down to the gene level, a long-standing research problem is to identify specific gene expression patterns linking to metabolic characteristics that contribute to disease development and progression. The microarray approach offers an expedited solution to this problem. However, it has posed a challenging issue to recognize disease-related genes expression patterns embedded in the microarray data. In selecting a small set of biologically significant genes for classifier design, the nature of high data dimensionality inherent in this problem creates substantial amount of uncertainty.RESULTS:Here we present a model for probability analysis of selected genes in order to determine their importance. Our contribution is that we show how to derive the P value of each selected gene in multiple gene selection trials based on different combinations of data samples and how to conduct a reliability analysis accordingly. The importance of a gene is indicated by its associated P value in that a smaller value implies higher information content from information theory. On the microarray data concerning the subtype classification of small round blue cell tumors, we demonstrate that the method is capable of finding the smallest set of genes (19 genes) with optimal classification performance, compared with results reported in the literature.CONCLUSION:In classifier design based on microarray data, the probability value derived from gene selection based on multiple combinations of data samples enables an effective mechanism for reducing the tendency of fitting local data particularities.
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BMC Bioinformatics


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Bio.-- Central


Methodology article


Evaluation of gene importance in microarray data based upon
probability of selection
Li M Fu*1,2 and Casey S Fu-Liul


Address: 'Pacific Tuberculosis and Cancer Research Organization, Pasadena, California, USA and 2University of Florida, Gainesville, Florida, USA
Email: Li M Fu* lifu@patcar.org; Casey S Fu-Liu casey@patcar.org
* Corresponding author


Published: 22 March 2005
8MC Bioinformatics 2005, 6:67 doi:10.1186/1471-2105-6-67


Received: 19 November 2004
Accepted: 22 March 2005


This article is available from: http://www.biomedcentral.com/1471-2105/6/67
2005 Fu and Fu-Liu; 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.



Abstract
Background: Microarray devices permit a genome-scale evaluation of gene function. This
technology has catalyzed biomedical research and development in recent years. As many important
diseases can be traced down to the gene level, a long-standing research problem is to identify
specific gene expression patterns linking to metabolic characteristics that contribute to disease
development and progression. The microarray approach offers an expedited solution to this
problem. However, it has posed a challenging issue to recognize disease-related genes expression
patterns embedded in the microarray data. In selecting a small set of biologically significant genes
for classifier design, the nature of high data dimensionality inherent in this problem creates
substantial amount of uncertainty.
Results: Here we present a model for probability analysis of selected genes in order to determine
their importance. Our contribution is that we show how to derive the P value of each selected gene
in multiple gene selection trials based on different combinations of data samples and how to
conduct a reliability analysis accordingly. The importance of a gene is indicated by its associated P
value in that a smaller value implies higher information content from information theory. On the
microarray data concerning the subtype classification of small round blue cell tumors, we
demonstrate that the method is capable of finding the smallest set of genes (19 genes) with optimal
classification performance, compared with results reported in the literature.
Conclusion: In classifier design based on microarray data, the probability value derived from gene
selection based on multiple combinations of data samples enables an effective mechanism for
reducing the tendency of fitting local data particularities.


Background
Based on the concept of simultaneously studying the
expression of a large number of genes, a DNA microarray
is a chip on which numerous probes are placed for hybrid-
ization with a tissue sample. The DNA microarray has
recently emerged as a powerful tool in molecular biology
research, offering high throughput analysis of gene expres-
sion on a genomic scale. However, biological complexity


encoded by a deluge of microarray data is being translated
into all sorts of computational, statistical or mathematical
problems.

Driven by the growing genomic technology, molecular
medicine has become a rapidly advancing field. An
important research topic is to identify disease-related gene
expression patterns based on microarray analysis. In one


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approach, genes are selected for constructing a clinically
useful classifier for disease diagnosis. The genes thus
selected often shed light on the fundamental molecular
mechanisms of the disease [1]. As addressed in several
research works [1-5], the problem of gene selection con-
sidered in this context is a difficult one because there are
thousands of genes at hand but only a very limited
number of samples are available. Mathematically, this
problem is characterized by high data dimensionality. To
develop a classifier, dimensionality reduction by gene
selection is essential. Genes selected for constructing a
classifier are believed to be important. Typically, only a
small fraction of genes differentially expressed in the dis-
eased tissue will be selected.

There exist two related but different objectives for gene
selection. As mentioned above, one objective is to con-
struct a classifier or predictor for classifying, diagnosing,
or predicting the type of cancer tissue according to the
expression pattern of selected genes in the tissue [6]. The
other objective is to determine whether the changes in
gene expression across two conditions are significant (e.g.,
SAM) [7]. The present work is developed in the first
context.

Here, we report new theoretical developments and
research results as an extension of our earlier work [4,8],
presenting a new probabilistic analysis of gene selection
from microarray data, which distinguishes our work from
other related work.

Results
Probability analysis of selected genes
Under very high data dimensionality, questions can be
raised of whether genes could have been selected by
chance and whether selected genes are sufficiently signifi-
cant beyond any doubt due to inherent uncertainty or
data particularity. Quite often, not identical sets of genes
are selected from different subsets of the data. At the fun-
damental level, it would be important to distinguish
between the case of diverse patterns and the case of false
patterns. To address the problem, we take the approach
that takes into account both statistical significance and
performance issues. The bootstrapping technique lends
itself well as far as the first issue is concerned.

Suppose we randomly draw samples from a given domain
and conduct a gene selection experiment. Assume that we
select one gene out of a total of p genes. The probability of
the event that a particular gene is selected in a single trial
is 1/p. According to the information theory, the smaller
the probability is, the more informative the event is.
Given a large p, it seems that the event is significant, and
this would be true only if we have a particular gene in
mind before gene selection; otherwise, the probability


should be adjusted for the presence of p genes, and then it
becomes clear that any gene selected in a single trial is
non-informative. Now suppose we conduct multiple trials
and ask the question of whether any gene repeatedly
selected across trials is significant. Here we devise an anal-
ysis for the question.

Theorem
In r multiple independent trials conducted for gene selec-
tion, select one gene out of a total of p genes in each trial.
Given the level of significance (x, a gene is considered sig-
nificant if it is selected r times in r trials and

r log(a /p)
log(1/p)
Proof
The probability of the event that the same gene is selected
r times in r trials is (1/p)'. Since there are p genes, the
adjusted probability (analogous to Bonferroni's correc-
tion) is p(1/p)'. Therefore,


p( )r p


Equivalently,

Ir a
p p
Thus,


rlog(-) P P


1
Note that the value of log(-)

lows. C


is negative. The result fol


Corollary I
The minimum threshold value of r for reaching the given
level of significance is

rlog(a/p) ]
r [io = P) 1 (1)
log(1/ p)
where is the ceiling operator. This is because r must be an
integer greater than or equal to the real threshold.

For example, consider the leukemia data [1]. There are
7129 genes. Assume ( = 0.05. From Eq. (1), ro= 2.

Consider a more general case: what is the probability of
the event that a gene is selected r times in m trials? The
adjusted probability becomes


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P \1 1) r n (m-r)
Pl ll -


where is the combinatorial function that returns the

number of possibilities for choosing r from m objects.


Assume a large p so that -
P


0 Then, we have


log(a/(p r)

log(1/p)


ro


The level of significance (a in Eq. (1) and (2)) is set to
0.05 by convention in the present work.

Reliability analysis of gene selection
The innovative feature of our method is to conduct relia-
bility analysis for arriving at the gene expression signature.
The analysis assesses the repeatability of genes selected
and determines the repeatability for gene selection using
M-fold cross-validation.

In the 10-fold cross-validation approach, the data set is
divided into 10 disjoint subsets of about equal size. Genes
are selected on the basis of nine of these subsets, and then
the remaining subset is used to estimate the predictive
error of the trained classifier using only the selected genes.
This process is repeated 10 times, each time leaving one
set out for testing and the others for training. The cross-
validation error rate is given by the average of the 10 esti-
mates of the error rate thus obtained.

In each cross-validation cycle, we conduct SVM-RFE gene
ranking and selection operations, as described in the
Methods section. We select a minimal set of genes by col-
lecting from the top rank one by one and picking the set
associated with minimum error in each cross-validation
cycle. There is no guarantee that the same subset of genes
will be selected in each of the 10 cycles in 10-fold cross-
validation. However, vital genes tend to be selected more
consistently than others across cycles. The significance of
a gene is correlated with the repeatability of selection
according to the probabilistic analysis given earlier. We
associate each selected gene with a repeatability value
indicating how many times it is selected in the cross-vali-
dation experiment. The biological or clinical interpreta-
tion of "repeatability" would depend on the objective and
design of the microarray experiment. We may consider the
validity of a selected gene by its reliability in the sense that


the more often a gene is selected, the less likely chance is
a factor.

To select the final set of genes, we need to determine the
repeatability threshold. A gene is in the final set if its
repeatability reaches (i.e., no less than) the threshold. To
this end, second 10-fold cross-validation is performed.
Then we choose the repeatability threshold that is associ-
ated with the minimal cross-validation error under the
given level of significance (a = 0.05). Recall that a gene
with a higher repeatability is associated with a small P
value, as shown earlier.

To extend the method from two-class to multi-class classi-
fication, we adopt the one-against-all others strategy
under which genes are selected for each class one at a time
and then combined. For each class, all the other classes are
grouped as a single class. In this way, a multi-class gene
selection problem is converted into a series of two-class
problems. The program was written in Matlab [9]. An
SVM Matlab toolbox as well as Mathlab is required for the
program use.

Case analyses
In cancer research, our current goal is to develop a molec-
ular classifier based on tissue gene expression patterns for
diagnosis and subtype classification. With this in mind,
we evaluate our method using well-known benchmark
microarray data sets including those concerning small
round blue cell tumors, colon cancer, leukemia as well as
perturbed data sets.

The small round blue cell tumors (SRBCTs) data set
includes 63 training samples and 25 test samples derived
from both tumor biopsy and cell lines [101. In consistency
with other reports in the literature, we used the test set of
20 samples after 5 non-SRBCT samples were removed.
The data set consists of four types of tumor in childhood,
including Ewing's sarcoma (EWS), rhabdomyosarcoma
(RMS), neuroblastoma (NB), and Burkitt lymphoma
(BL). After initial screening, the data set in the public
domain contains 2308 genes.

The colon cancer data set contains 62 tissue samples, each
with 2000 gene expression values [111. The tissue samples
include 22 normal and 40 colon cancer cases. In this
study, we used all the 62 samples in the original data.

The leukemia data consist of 72 tissue samples, each with
7129 genes expression values [1]. The samples include 47
ALL (acute lymphoblastic leukemia) and 25 AML (acute
myeloid leukemia). The original data have been divided
into a training set of 38 samples and a test set of 34
samples.



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Table I: Genes selected by our method on the microarray dataset of small round blue-cells tumors. Those genes also selected using
the methods of Tibshirani et al. [ 13] and Khan et al. [ 10] are respectively marked by the symbol *.


P Value Gene Description


Image ID

21652
878280
377461
325182
1435862
42558
812105
41591
810057
183337
796258
1409509
788107
770394
82225
814260
784224
308163
212542


The reference method with which we compared our
method applied a technique referred to as SVM-RFE [3] to
select genes from the training data without reliability
assessment. The reference method [12] is a multi-class
extension of the SVM-RFE method used for two-class clas-
sification. The SVM-RFE method (two-class or multi-class)
has not been applied to the SRBCT data before. We imple-
mented the computer algorithm of the reference method
for comparison with ours. The same experimental condi-
tions were applied to both methods.

Small round blue cell tumor classification
On the SRBCT data, our method selected 19 genes (Table
1) from the microarray gene expression data of the 63
training samples. The SVM classifier trained on the 63
training samples using the 19 selected genes was tested on
the 20 different test samples. Both the training and test
predictive accuracies were 100%. That is, the trained SVM
classifier can accurately predict the tumor class using the
19 gene expression data for both seen and unseen sam-
ples. Since the classifier may tend to fit the training data,
the generalization performance of the classifier is indi-
cated by the test accuracy.

The reference method selected 8 genes with 100% training
accuracy but with only 90% test accuracy. It seemed that
the reference method did not select enough genes even
though the selected genes could correctly classify all the
training samples an example of over-generalization,
whereas our bootstrap-like strategy adequately dealt with


this problem by taking into account of both reliability and
diversity in gene selection.

We examined the consensus of genes selected by our
method and by two other best-known methods: the
method of Khan et al. [10] based on artificial neural net-
works and the method of Tibshirani et al. [13] based on
shrunken centroids, and we found that there was high
consensus between our and their results. Out of the 19
genes selected by our method, 18 genes were also selected
by Khan's method and 16 genes by Tibshirani's method
(Table 1). While agreement among results produced by
different methods may imply similarities in the inductive
biases, these two other methods use fundamentally differ-
ent representational biases. Thus, such agreement should
not be taken for granted and would instead serve as sub-
stantial evidence indicative of the validity and significance
of our method.

Whether the selected genes served as meaningful markers
for cancer classification was further confirmed by cluster
analysis and visualization. To this end, we applied a hier-
archical clustering program developed by Eisen [ 14] to the
gene expression data of the selected genes. By visual
inspection of the gene expression map, four clearly sepa-
rated clusters (Figure 1) were identified. Upon verifica-
tion, each cluster corresponded exactly to a distinct tumor
group with 100% accuracy. Thus, a diagnostic chip can be
designed based on the selected genes. This result also pro-
vides additional evidence to support our method.



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Tibshirani et al.


catenin (cadherin-associated protein), alpha I
collapsin response mediator protein I
caveolin I, caveolae protein
cadherin 2, N-cadherin (neuronal)
MIC2 surface antigen (CD99)
L-arginine:glycine amidinotransferase
transmembrane protein
meningioma I
cold shock domain protein A
major histocompatibility complex, class II, DM alpha
sarcoglycan, alpha
troponin TI, skeletal, slow
amphiphysin-like
Fc fragment of IgG, receptor, transporter, alpha
secreted frizzled-related protein I
follicular lymphoma variant translocation I
fibroblast growth factor receptor 4
ESTs
cDNA DKFZp586J2 18


Khan et al.


2.3 x 10-s
2.3 x 10-s
< 0.000001
2.3 x 10-s
0.02
0.02
< 0.000001
< 0.000001
< 0.000001
0.02
< 0.000001
0.02
< 0.000001
< 0.000001
0.02
< 0.000001
< 0.000001
2.3 x 10-s
< 0.000001


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oMMOf.^ "A rl r . -

oMAMAimh:mm:ummmmmemmme -7l*C...ri.. .^
I I IIJ U~JJJ~ '4


A
ii -



* *
" 4
a.
4'
4' 4 ~
'4
~
I~.
I,


Figure I
The gene expression map of the 19 genes selected by our method in the domain concerning classification of SRBCTs. The map
was generated by Eisen's hierarchical clustering program called CLUSTER and viewed by the TREEVIEW program. Four sample
clusters are visually recognizable, corresponding exactly to the four predefined tumor classes (NB, EWS, BL, and RMS) with
100% accuracy.






Table 2: I 5 genes selected from the colon cancer microarray data set (62 samples) using our method.


Gene Accession #


H20709
X57351
T94579
T47377
T98835
T61661
X67325
T58861
T61446
H88360
L38810
T57882
T92451
J02854
K03474


P value


< 0.000001
< 0.000001
< 0.000001
< 0.000001
< 0.000001
3.0 x 10-s
3.0 x 10-s
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02


Colon cancer diagnosis
In performance analysis, we conducted multiple experi-
ments with random data partitions. In each experiment,
the data were randomly and equally split into training and
test sets. The training set was used for gene selection and


Definition

myosin light chain alkali, smooth-muscle isoform
interferon-inducible protein 1-8D
human chitotriosidase precursor
S-100P protein (human)
alpha trans-inducing protein (bovine herpesvirus type I)
profilin I (human)
H. sapiens p27
60s ribosomal protein L30E
putative DNA binding protein A20
guanine nucleotide-binding protein G(OLF), alpha subunit
Homo sapiens thyroid receptor interactor (TRIPI)
myosin heavy chain, nonmuscle type A
tropomyosin, fibroblast and epithelial muscle-type
myosin regulatory light chain 2, smooth muscle isoform
human mullerian inhibiting substance gene






classifier training, and the test set for determining the pre-
dictive performance of the classifier based on the genes
selected by the given algorithm. Our method outper-
formed the reference method by a small margin. This
result reflects the underlying fact that there are multiple


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!i N! !- 4 !!!i!!i! i11iilrvJM4 M M h* tiw .P r"J fl h* i N i
MUIPXl(JS Jl< lSMM i i dli hh*f b 3Uk bJSiSi~ ll~L li.. 6 iliili6&khfli b 3. l .uli
tu b 1 ehth6iiili hi &t &L 6eemm&46adu6.Js t&d.mm


Figure 2
The gene expression map of the 15 genes selected from the colon cancer microarray data set using our method. Two major
sample clusters can be recognized by visual inspection, corresponding to normal and cancer tissue samples, respectively.


possible ways of selecting genes for constructing a classi-
fier with comparable performance using different
methods.

Our program selected 15 genes from the colon cancer data
(Table 2). The selected genes allow the separation of can-
cer from normal samples in the gene expression map (Fig-
ure 2, Table 3). Some genes were selected because their
activities resulted in the difference in the tissue composi-
tion between normal and cancer tissue. Other genes were
selected because they played a role in cancer formation or
cell proliferation. It was not surprise that some genes
implicated in other types of cancer such as breast and
prostate cancers were identified in the context of colon
cancer because these tissue types shared similarity.

Our method is supported by the meaningful biological
interpretation of selected genes, as discussed below. New
biological hypotheses can be formulated to further inves-
tigate the relationship of a particular gene with colon can-
cer. For example, what is the role of profilin 1 protein in
colon cancer? Some discovered genes could potentially
serve as novel targets for drugs, vaccines, or gene therapy.

Leukemia classification
On the leukemia data, our method selected four genes
(Table 4) from the microarray gene expression data of 38
training samples. The SVM classifier trained on the 38
training samples using the selected genes was tested on
the 34 different test samples. The training and test accura-
cies were 100% and 97.06%, respectively. In addition, the
AML and ALL samples formed separate clusters in the gene
expression map of the selected genes.


The reference method also selected four genes and
achieved the same level of test accuracy as our method.
The original algorithm of SVM-RFE [3] selected 8 or 16
genes on this data set. The method based on shrunken
centroids [13] selected 21 genes on this data set. A recent
study indicated that the unbiased error estimate of the
classifier using a small number of selected genes was vir-
tually non-zero on the leukemia data set [6]. Taken
together, the evidence showed that our method produced
optimum results in terms of both predictive performance
and the number of selected genes.

Perturbed data
In practical circumstances, noise may arise during sample
collection and handling, slide preparation, hybridization,
or image analysis, as reflected by variations in microarray
results generated from different laboratories. To address
this issue, we also conducted performance evaluation of
our gene selection method based on perturbed data. 20
data sets were produced by randomly perturbing 5%
(rounded up to the nearest integer) of the training cases,
reversing their class labels and leaving the test cases intact,
in the domains of colon cancer diagnosis and leukemia
classification (ten in each domain). The average test pre-
dictive accuracies with our method in the two domains
were 85.49% and 88.61%, respectively, compared with
80.65% and 86.11% with the reference method. The
result suggests the potential advantage with our method
in smoothing out data variations due to various sources in
practice.






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Table 3: Diagnosis results of the colon cancer data samples based
on 15 selected genes, in correspondence with the gene
expression map.


Normal Tissue
Sample
Normal-0 I
Normal-02
Normal-03
Normal-04
Normal-05
Normal-06
Normal-07
Normal-08
Normal-09
Normal- 10
Normal- I I
Normal- 12
Normal- 13
Normal- 14
Normal- 15
Normal- 16
Normal- 17
Normal- 18
Normal- 19
Normal-20
Normal-21 I
Normal-22


Cancer Tissue
Diagnosis Sample
normal Cancer-0 I
normal Cancer-02
normal Cancer-03
normal Cancer-04
normal Cancer-05
normal Cancer-06
normal Cancer-07
cancer Cancer-08
normal Cancer-09
normal Cancer- 10
normal Cancer- I I
normal Cancer- 12
normal Cancer- 13
normal Cancer- 14
normal Cancer- 15
normal Cancer- 16
normal Cancer- 17
normal Cancer- 18
normal Cancer- 19
cancer Cancer-20
normal Cancer-21 I
normal Cancer-22
Cancer-23
Cancer-24
Cancer-25
Cancer-26
Cancer-27
Cancer-28
Cancer-29
Cancer-30
Cancer-3 I
Cancer-32
Cancer-33
Cancer-34
Cancer-35
Cancer-36
Cancer-37
Cancer-38
Cancer-39
Cancer-40


Discussion
Both cross-validation and bootstrapping are standard sta-
tistical methods for arriving at an unbiased estimate of the
true error rate associated with a classifying or predicting
system. Bootstrapping has also been used for assessing the
reliability or stability of phylogenetic trees [15] or cluster
analysis [161. Bootstrapping is a method for random re-
sampling with replacement for a number of times and
estimates the error rate by the average error rate over the
number of iterations. Cross-validation is a method of
assessing the reliability of error; however, its application


Diagnosis
cancer
normal
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
cancer
normal
cancer
normal
cancer
cancer
cancer
cancer
cancer
normal
cancer
cancer
cancer
cancer


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to learning the pattern in the data is novel. As discussed
later, stability emerges as an important issue in gene
selection. Here we propose to use bootstrapping or cross-
validation for analyzing the issue. Our experience showed
that cross-validation was more efficient than bootstrap-
ping. For instance, genes selected based on a single 10-
fold cross-validation were more accurate in prediction
than those selected using bootstrapping with 10 re-sam-
pling iterations. Since the SVM-based gene selection algo-
rithm is time-consuming, we consider only cross-
validation for assessment of error and stability in this
study.

In the original SVM-RFE algorithm [31, error estimation
and gene selection are not independent processes because
both are based on the same training set. However, it is
important to correct for the selection bias by performing a
cross-validation or applying a bootstrap external to the
selection process [6,171. Our implementation of SVM-RFE
is based on this idea.

Genes selected for cancer diagnosis or classification can be
validated by their biological significance since these genes
are expected to show differential expression between nor-
mal and cancer tissue or among subtypes of cancer, and as
such, they are implicated in cancer-related mechanisms or
pathways. Genes with unknown roles may be discovered
through gene selection and later verified by biological
studies.

From the SRBCT data set, genes selected by our method
for a particular type of cancer/tumor against other types
are generally consistent with its tissue of origin. For exam-
ple, genes selected for neuroblastoma (NB) are character-
istic for nerve cells, such as neuronal N-cadherin, and
meningioma 1; genes selected for rhabdomyosarcoma
(RMS) are characteristic for muscle cells, such as alpha sar-
coglycan, and slow skeletal troponin TI; genes selected for
Burkitt lymphoma (BL) are characteristic for lymphocytes
or blood cells, such as major histocompatibility complex
(class II, DM alpha). Some genes discovered by means of
microarray analysis have been reported in the biological
literature, e.g., over-expression of MIC2 in Ewing's sar-
coma (EWS) [18]. Some genes are over-expressed in a cer-
tain type of tumor but lack specificity. For instance, FGFR4
(fibroblast growth factor receptor 4) was noted to be
highly expressed only in RMS and not in normal muscle,
but it is also expressed in some other cancers and normal
tissues [ 10]. A gene that is under-expressed in a particular
type of tumor compared with other types can also be
selected as a diagnostic marker. For instance, cold shock
domain protein A selected for NB was under-expressed in
this tumor, consistent with the fact that this gene is
expressed in B cells and skeletal muscle but not in the
brain [13].


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Table 4: Genes selected by our method on the leukemia microarray dataset. Those genes also selected using the methods of Golub et
al.[l] and SVM-RFE (the reference algorithm) are respectively marked by the symbol *.


P Value


< 0.000001
< 0.000001


0.006
0.006


Gene Description

CST3 Cystatin C
INTERLEUKIN-8
PRECURSOR
MPO Myeloperoxidase
Ly-GDI


Golub et al.


SVM-RFE


With our method, four muscle-related genes (H20709,
T57882, T92451, and JO02854) were selected from the
colon cancer data, reflecting the fact that normal colon tis-
sue had higher muscle content, whereas colon cancer tis-
sue had lower muscle content (biased toward epithelial
cells) [11]. The selection of 60s ribosomal protein L30E
agreed with an observation that ribosomal protein genes
had lower expression in normal than in cancer colon tis-
sue [11]. The selected interferon inducible protein 1-8D
genes were found to be expressed in adenocarcinoma cell
lines [19]. There was a potential connection of another
selected gene, human chitotriosidase, to cancer [3]. The
implications of cancer among other selected genes are
explained as follows. S-100 protein can stimulate cellular
proliferation and may function as a tumor growth factor
[20]. Profilin 1 protein can suppress tumorigenicity in
breast cancer cells. A study showed consistently lower pro-
filin 1 levels in tumor cells [21]. The reduced expression
of P27 protein was linked to the possibility of colon carci-
noma [22]. The A20 protein can inhibit a specific apop-
totic pathway [23]. Recall that apoptosis is a major
mechanism for tumor suppression. The guanine nucle-
otide-binding protein is involved in signal transduction
and its abnormality may contribute to cancer
development [24]. A thyroid receptor interactor could be
a target gene of a certain oncogene. The alpha trans-induc-
ing protein (bovine herpesvirus type 1) may be linked to
oncogenic activity.

In the related work [3], 7 genes were selected from the
colon cancer data: H08393, M59040, T94579, H81558,
R88740, T62947, and H64807. For all of them, a possible
link to cancer was found in the biological literature. These
7 genes, however, do not include any muscle-specific
gene, despite that muscle content offered a discriminating
index for colon cancer [ 11].

In a typical microarray data analysis problem, the data
dimensionality is high and the sample size is relatively
small. Under this condition, the problem of finding a clas-
sification model is under-constrained, and the model
found tends to fit the training data so closely that it fails
to generalize to unseen data. To address the issue of data


overfitting, the SVM has the capability of controlling the
model complexity to the point where a satisfactory solu-
tion can be produced. On the other hand, the ability of
causal discovery based on the SVM-RFE approach or an
alternative approach is discounted by the finding that
most genes selected are selected only once from one data
split to another in M-fold cross-validation [25]. This
means that the SVM is not free of the data-overfitting
problem at least in the context of gene selection from
microarray data, and it raises the question about stability
or reliability of gene selection, as we address here.

The research finding that the SVM may assign zero weights
to strongly relevant variables and non-weights to weakly
relevant (red-herring) features [26] implies the disadvan-
tage with this approach for discovery of causal variables
associated with the target variable concerned. This how-
ever can be understood since the SVM-RFE is aimed to
identify the best features for maximum margin of separa-
tion between different classes of samples, regardless of
causal implications. In reality, causal variables are not
necessarily most discriminant, as the target variable is not
always categorized according to its causal factors. The
issue of causality becomes even more complicated
because of confounding variables leading to so-called
spurious causation. The method presented here is devel-
oped in the context of cancer subtype classification and
evaluated in terms of predictive performance rather than
the capability of causal inference. However, some meth-
ods are both predictive and causal [26,271.

We emphasize the importance of holding back some data
to improve generalization and diversity of the learning
outcome. In application of M-fold cross-validation to n
samples, M can assume a value ranging from 2 to n. A
small M is not sufficient to assess the repeatability of
selected genes while a large M (e.g., M = n in the leave-
one-out experiment) is associated with high degree of
redundancy on data for training and low diversity of genes
selected. This argument suggests that there exists an opti-
mum M value. So we conducted experiments to compare
predictive accuracies for three cases: M = 5, 10, and 15.
Among the three cases, 10-fold cross-validation achieved


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M27891
Y00787

M19507
L20688


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the best results. It is thus consistent with our intuitive
analysis. However, there is no proof that 10-fold cross-val-
idation is always the best choice. In practice, the optimum
M value should be determined by the value associated
with the best cross-validation accuracy.

This study highlights the importance of reliability assess-
ment of genes selected from a large-scale microarray data.
We show how to derive the P value of each selected gene
in multiple gene selection trials based on different data
partitions. The importance of a gene is indicated by its
associated P value. The distinctive feature of our method
is that gene selection is determined by both ranking and
reliability analyses. Reliability analysis is conducted using
M-fold cross-validation. Some gene selection methods
[3,28] use cross-validation to determine the number of
selected genes by minimum cross-validation error but not
by optimum repeatability as in our method. Thus, relia-
bility analysis comprising repeatability measurement and
optimum repeatability determination defines the novelty
of our method, which has enabled a more accurate and
cost-effective cancer classifier to be constructed, compared
with other methods. Notice, however, the argument about
reliability or stability must rest on the assumption of
sound performance, as will be clear from the apparent sta-
bility with some trivial approaches to gene selection such
as the one based on lexicographic ordering of gene names.
In fact, the theory behind the analytical scheme we devel-
oped is a general one and can therefore be extended to
other performance-based gene selection methods.

Conclusion
The DNA microarray technology has become a standard
tool for gathering genome-wide gene expression informa-
tion. Molecular classification based on gene expression
information has emerged as an important approach to
cancer diagnosis. A cost-effective approach is to select a
small set of genes for classifier design. Moreover, it may be
ineffective to use whole microarray data for classification
purposes because the data dimensionality (i.e., the
number of variables/genes) is often several orders of mag-
nitude greater than the available sample size.

Experience shows that different sets of genes can be
selected from different combinations of microarray data
instances with the same gene selection algorithm. At the
same time, it is noticed that a biologically significant gene
tends to be selected repeatedly across different
combinations of data instances. We have developed a
method for analyzing this situation. In the domain of
small round blue cell tumor subtype classification, we
have demonstrated that the method we developed
selected only 19 genes that provided 100% accuracy on
both training and test data sets. In comparison, the
approach based on artificial neural networks [10] selected


96 genes, and the shrunken centroid method [13] selected
43 genes. Thus, our method suggests a mechanism for
effectively reducing the tendency of fitting local data par-
ticularities in the process of gene selection for classifier
design based on microarray data.

Methods
This section provides the details of the methods, but the
novelty aspects are described in the "Results" section.

Classification based on support vector machines
We use the method of support vector machines (SVM)
[29,30] for classification. The SVM has been demon-
strated as a useful tool for analyzing microarray data [31].
Consider n training samples {( i y,) 1 < i < n }, where
xi, is the input feature vector for the ith sample and y, is
the corresponding target class (output). The basic prob-
lem for training an SVM can be reformulated as: given a
set of n training instances, each represented as ( i, Yi),
maximize


=11


1 1,j
211,=


subject to

n
yiai = 0 and a, > 0, for 1 < i < n.
i=1
The optimal hyperplane that separates different classes of
objects can be constructed from the solutions ou's to this
maximization problem. The SVM can perform a nonlinear
transformation via the inner-product kernel K(i(, ij) to
map the input space into a new high-order feature space
where the patterns are linearly separable with high proba-
bility. The use of such a kernel function can lead to a deci-
sion function that is non-linear in the input space but its
image is linear in the transformed space. When the sam-
ples are not linearly separable, whether in the input or
transformed space, a soft-margin algorithm as an exten-
sion of the basic algorithm is available [32].

The SVM used in this study employed the linear kernel
since we found that it yielded a better result than a non-
linear kernel for the data under investigation, and this
observation is also consistent with the literature [3]. All
SVM parameters were set to the standard values in accord-
ance with the convention: s = 0 (C-SVM), t = 0, c = 100, v
= 10.

Data normalization in the case of cDNA arrays proceeded
as follows: the local background intensity is subtracted
from the value of each spot on the array; the two channels


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are normalized against the median values on that array;
the Cy5/Cy3 fluorescence ratios and log0o-transformed
ratios are calculated from the normalized values. In addi-
tion, genes that do not change significantly can be
removed through a filter in a process called data filtration.

Gene selection
An SVM-based gene selection algorithm has two main
components: gene ranking and gene selection. Gene rank-
ing results in a sorted list of genes in decreasing order of
importance for classification. This issue is complicated
since some genes become important only if combined
with other genes. After genes are ranked, genes are selected
according to their ranks.

When there are a large number of features, a conservative
strategy is to determine the least important feature one at
a time recursively. In this work, we adopted the SVM-RFE
recursivee feature elimination) algorithm [3] where the
least important feature is identified and removed in each
iteration, remaining features are re-evaluated, and the
process repeats until no more features are left for consid-
eration. For the linear kernel, the importance of a feature
is determined by the associated weight magnitude, and
the least important feature refers to the one with the
smallest weight value. SVM-RFE essentially implements
the strategy of backward feature elimination. In principle,
feature ranking becomes more accurate as less important
features are removed successively. To improve the speed,
a chunk of least important features was eliminated per
step until there were 256 genes remained, from which
point, one gene was remove per step. The RFE ranking cri-
terion is given by

Rank(g0) Order-of-Elimination(gi) >Order-of-
Elimination(g1)

That is, the later a gene is eliminated, the higher (smaller)
rank it has. So, the first-rank gene is last removed.

Authors' contributions
L. Fu developed the method and conducted the experi-
ments. C. Fu-Liu interpreted the data. Both authors
drafted, read, and approved the manuscript.

Acknowledgements
This work is supported by National Institutes of Health and National Sci-
ence Foundation under grants HL-08031 I and IIS-0221954. E. S. Youn
assisted in coding the algorithm.

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