Choosing the right path: enhancement of biologically relevant sets of genes or proteins using pathway structure.

Abstract

A method is proposed that finds enriched pathways relevant to a studied condition using the measured molecular data and also the structural information of the pathway viewed as a network of nodes and edges. Tests are performed using simulated data and genomic data sets and the method is compared to two existing approaches. The analysis provided demonstrates the method proposed is very competitive with the current approaches and also provides biologically relevant results.

Full text

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Genome Biology 2009, 10:R44
Open Access
2009Thomas et al.Volume 10, Issue 4, Article R44
Method
Choosing the right path: enhancement of biologically relevant sets
of genes or proteins using pathway structure
Reuben Thomas¤*, Julia M Gohlke¤*, Geffrey F Stopper†,
Frederick M Parham* and Christopher J Portier*
Addresses: *Environmental Systems Biology Group, Laboratory of Molecular Toxicology, National Institute of Environmental Health Sciences,
RTP, NC 27709, USA. †Department of Biology, Sacred Heart University, Fairfield, CT 06825, USA.
¤ These authors contributed equally to this work.
Correspondence: Christopher J Portier. Email: portier@niehs.nih.gov
© 2009 Thomas 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.
Finding enriched pathways<p>A method is proposed that finds enriched pathways relevant to a studied condition, using molecular and network data.</p>
Abstract
A method is proposed that finds enriched pathways relevant to a studied condition using the
measured molecular data and also the structural information of the pathway viewed as a network
of nodes and edges. Tests are performed using simulated data and genomic data sets and the
method is compared to two existing approaches. The analysis provided demonstrates the method
proposed is very competitive with the current approaches and also provides biologically relevant
results.
Background
Data on the molecular scale obtained under different sam-
pling conditions are becoming increasingly available from
platforms like DNA microarrays. Generally, the reason for
obtaining molecular data is to use these data to understand
the behavior of a system under insult or during perturbations
such as occurs following exposure to certain toxicants or
when studying the cause and progression of certain diseases.
Toxins or diseases will hereafter be commonly referred to as
perturbations to the biological system. Genomics is capable of
providing information on the gene expression levels for an
entire cellular system. When faced with such large amounts of
molecular data, there are two options available that can ena-
ble one to focus on a small number of interesting sets of genes
or proteins. One can cluster the data [1] and use the clusters
to identify sets of genes that were significantly affected by the
perturbations. This represents an unsupervised approach.
Other similar approaches include principal component anal-
ysis [2] and self-organizing maps [3].
Alternatively, biologically relevant sets of genes/proteins are
deduced to exist a priori in the form of biochemical pathways
and cytogenetic sets. A supervised approach can be linked
with the data to identify these a priori-defined sets that are
significantly affected by the perturbations seen in the data.
The method proposed in this paper is an example of this
approach applied to the scenario of distinguishing between
two conditions (such as normal patient versus disease
patient, or unexposed versus exposed). The data we wish to
link to a given set of pathways are assumed to be genomic data
such as gene expression levels or the presence of gene poly-
morphisms known to be associated with diseases.
Supervised approaches for the identification of biologically
relevant gene expression sets have typically been identified as
Published: 24 April 2009
Genome Biology 2009, 10:R44 (doi:10.1186/gb-2009-10-4-r44)
Received: 21 November 2008
Revised: 19 March 2009
Accepted: 24 April 2009
The electronic version of this article is the complete one and can be
found online at http://genomebiology.com/2009/10/4/R44

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'gene set' or 'pathway enrichment' methods in the literature.
Recent years have seen significant work done on proposals for
new approaches guided by criticisms and limitations of the
existing ones; references [4-8] provide a critical review of the
existing methods in terms of their different features, such as
the null hypotheses of the underlying statistical tests used and
the independence assumption between genes. These reviews
essentially inform us that the pathway enrichment methods
can be viewed as falling on two sides of a number of different
coins. A few of these classifications are given below.
Firstly, methods could be interested in testing either whether
the genes in a specific pathway of interest are affected as a
result of a treatment (the implied null hypothesis has been
referred to as 'self-contained' [4] or denoted as 'Q2' [9]) or
whether the genes in the pathway of interest are more
affected than the other genes in the system (this implied null
hypothesis has been referred to as 'competitive' [4] or as 'class
1, 2, 3' [6] or denoted as 'Q1' [9]). There are of course good
reasons for preferring either of these null hypotheses. One
would prefer the 'competitive' hypothesis if the treatment had
a wide ranging impact on the genes in the system. This could
have an undesirable consequence of having randomly chosen
(and hence not biologically relevant) sets of genes attaining
significance for the 'self-contained' tests; a nice illustration of
a case like this is provided in [10]. One could use a 'self-con-
tained' test if the belief is that the treatment had quite a
restricted impact on the genes in the system and/or if their
only focus is on one or a small number of pathways.
Some of the pathway enrichment methods treat the genes in
the system as being independent of each other [7,9,11-22].
Ignoring the gene-gene correlations has been shown to have
the effect of elevated false-positive discoveries [4,6]. How-
ever, the need to prioritize the different biological pathways
with respect to their relevance to the treatment and the lack
of a sufficient number of biological replicates (one in some
cases) may force the need for this independence assumption.
Examples of methods that try to take into account the gene-
gene correlations include [6,9,10,23-37].
Pathway enrichment methods can be distinguished by the use
or the absence of an explicit gene-wise statistic to measure the
gene's association with the treatment in determining a path-
way's relevance to the treatment. Examples of gene-wise sta-
tistics used include the two-sample t-statistic, log of fold
change [35], the significance analysis of microarrays (SAM)
statistic [25] and the maxmean statistic [10]. Methods like
those in [24,30,31,34,37,38] treat the problem as a multivar-
iate statistical one and avoid the need for an explicit defini-
tion of a gene-wise statistic.
The method proposed in this paper defines versions for both
the 'self-contained' and the 'competitive' null hypotheses and
utilizes the idea of the maxmean statistic [10]. It improves
upon the previous methods by its use of structural informa-
tion present in biochemical pathways. A pathway is said to
have structural information if its components can be placed
on a network of nodes and edges. For example, a gene set cor-
responding to a pathway can be viewed to be associated with
a network where the nodes represent the gene products (that
is, proteins, protein complexes, mRNAs) while the edges rep-
resent either signal transfer between the gene products in sig-
naling pathways or the activity of a catalyst between two
metabolites in metabolic pathways.
Classic signal transduction pathways, such as the mitogen-
activated protein kinase (MAPK) pathways, transduce a large
variety of external signals, leading to a wide range of cellular
responses, including growth, differentiation, inflammation
and apoptosis. In part, the specificity of these pathways is
thought to be regulated at the ligand/receptor level (for
example, different cells express different receptors and/or
ligands). Furthermore, the ultimate response is dictated by
the downstream activation of transcription factors. Alterna-
tively, intermediate kinase components are shared by numer-
ous pathways and, in general, do not convey specificity nor do
they directly dictate the ultimate response (see [39] for a
review). Therefore, we test the value of implementing a Heavy
Ends Rule (HER) in which the initial and final components of
a signaling pathway are given a higher weight than interme-
diate components.
Signal transduction relies on the sequential activation of com-
ponents in order to implement an ultimate response. There-
fore, we hypothesize that activation of components that are
directly connected to each other in a pathway conveys greater
significance than activation of components that are not
closely connected to each other. Therefore, we also test the
implementation of a Distance Rule (DR) scoring rule in which
genes that are closely connected to each other are given a
higher score.
The use of structural information based on an underlying net-
work in an analysis of gene expression data is not new. Simi-
lar ideas have been used to identify activated pathways from
time profile data (here the attempt was to distinguish
between two phenotypes) [40], while structural information
of the pathways has been used to enhance the clusters
deduced from the gene expression data [41] and to find differ-
entially expressed genes [42]. The study by Draghici et al.
[43] appears to be the only existing work that incorporates
pathway network information to the problem of pathway
enrichment. However, this appears to be limited by the need
to define an arbitrary cut-off for differential expression, the
assumption of independence between genes and the paramet-
ric assumption of an exponential distribution for computing
the significance.

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Results and discussion
The method proposed in this paper is named 'structurally
enhanced pathway enrichment analysis' (SEPEA). It is a
pathway enrichment method that incorporates the associated
network information of the biochemical pathway using two
rules, the HER and DR. SEPEA provides three options for null
hypothesis testing (SEPEA_NT1,
SEPEA_NT3) that depend on the goal of the pathway enrich-
ment analysis and the properties of genomic data available.
SEPEA_NT1 and SEPEA_NT2 require multiple array sam-
ples per gene and are tests that take into account inherent
gene-gene correlations. SEPEA_NT3 just requires a sum-
mary statistic per gene (that indicates association with the
treatment) but assumes that genes are independent of each
other. The need for the test SEPEA_NT3 is motivated by the
fact that there are situations where the data are just not suffi-
cient to estimate gene-gene correlations, such as the case
where the only information available is whether a gene is or is
not affected by the treatment; analyzing the situation of hav-
ing a set of gene polymorphisms known to be associated with
breast cancer is one such example. SEPEA_NT1 and
SEPEA_NT3 are proposed to be used in situations where the
goal is to compare the genes in the pathway of interest to the
other genes in the system in terms of their associations with
SEPEA_NT2 and
the treatment. SEPEA_NT2 is used for analyses involving
only the genes in the pathway in relation to the treatment. The
main objective of this paper is to demonstrate the utility of
incorporating pathway network information in a pathway
enrichment analysis. Therefore, comparisons are made with
results from corresponding versions of SEPEA that do not use
the network information - SEPEA_NT1*, SEPEA_NT2* and
SEPEA_NT3*. In addition, two literature methods are used
for comparison with the results from SEPEA_NT1 - gene set
enrichment analysis (GSEA) [35] and the maxmean method
[10] - the null hypotheses of GSEA and maxmean being very
similar to SEPEA_NT1.
Motivation for the Heavy Ends Rule score
By giving greater weight to genes whose products are nearest
to the terminal gene products of a pathway, the HER score
gives more weight to genes specific to a particular pathway.
This is illustrated in Figure 1, which uses the concept of termi-
nal gene products. They are gene products like either recep-
tors that initiate the pathway activity or transcription factors
that are made to initiate transcription as a result of the path-
way activity (see Materials and methods for a more mathe-
matical definition). The genes involved in each of the
signaling pathways in the Kyoto Encyclopedia of Genes and
Empirical distribution function of number of pathways associated with genes at given distances from terminal nodes
Figure 1
Empirical distribution function of number of pathways associated with genes at given distances from terminal nodes. Empirical cumulative distribution
function of the number of pathways that are associated with genes that have gene products located at a given distance, d (= 0, 1, 2, 3, 4), from a terminal
node of the pathway network. Gene products that are at a distance d = 0 are the terminal gene products. The data used were those of all the genes
associated with human signaling pathways in the KEGG pathway database [44].
02468 10 12 14161820
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x, no. of pathways
Fraction of genes associated with less than x pathways
Empirical CDF


d=0
d=1
d=2
d=3
d=4

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Genomes (KEGG) pathway database [44] were evaluated for
the position of their gene products with respect to the termi-
nal gene products and the total number of signaling pathways
that these genes are involved in. It is clear from Figure 1 that
genes associated with products that are closer to the terminal
gene products are more pathway-specific.
Justification for the Distance Rule score
To illustrate the utility of the DR as a scoring method, we con-
sider the linkage between the full set of pathways in KEGG
[44]; that is, the pathways themselves can be viewed to be
part of a higher level network, the nodes of which are path-
ways while the edges indicate the transfer of signal or mate-
rial between pathways (Figure S1 in Additional data file 2).
For example, the MAPK signaling pathway and the p53 sign-
aling pathway can be considered to be linked. It seems rea-
sonable to expect that after perturbation of the system, the
affected pathways that are linked are more likely to respond
similarly. We test this intuition using different microarray
data (from the Gene Expression Omnibus (GEO) database
[45] in a statistical test on the above network of pathways. The
details are provided in the Materials and methods section.
The P-values for the eight comparisons (estimated using
1,000 random networks) are given in Table 1. Significant P-
values across the comparisons support our use of the DR as a
reasonable score for differentiating between pathways.
Analysis using simulated data
Simulated data were generated from two pathway networks
having different patterns of correlation between the various
genes in the pathway, with each network having genes in a
pool of genes representing a biological system. The pair of
networks and the correlation patterns of genes in the path-
way, denoted by pattern numbers, are listed in Table 2. Pat-
terns 1, 2, 3 and 4 have non-zero correlation between a subset
of genes in the system. All genes in pattern 5 are assumed to
be independent of each other. Patterns 1 and 3 are biased to
the scoring rules proposed here whereas patterns 2 and 4 are
not. The treatments had the effect of increasing (as given in
the variable, pert) the expressions of certain genes in the sys-
tem.
Table 3 gives estimates of the type 1 errors of the five meth-
ods, at the 0.01 and 0.05 significance levels, for patterns 1 and
5. Table 4 gives estimates of the power of the SEPEA_NT1,
GSEA and SEPEA_NT2 methods at 0.01 and 0.05 signifi-
cance levels, for a pert value of 1.2 and for patterns 1-4. The
empirical sizes of the methods maxmean and SEPEA_NT3 do
not match their nominal sizes. So the results are provided at
empirical sizes of 0.07 and 0.05 (corresponding to a nominal
size of 0.001 for both cases).
Only patterns 1 and 5 were used to analyze the type 1 error
behavior because they represented the two scenarios (pres-
ence or absence of gene-gene correlations) where pathway
enrichment methods have been shown to have different
behaviors [4,10]. Because of the presence of correlations in
the data, SEPEA_NT3 gives an incorrect type 1 error value for
pattern 1 (Table 3). As has been stated previously, in spite of
this incorrect behavior, there are situations (like those in
which the only information available for each gene is a sum-
mary statistic representing the effect of the treatment) where
methods like SEPEA_NT3 need to be used in order to create
relevant hypotheses regarding affected processes due to the
treatment. SEPEA_NT1, SEPEA_NT2 and GSEA do maintain
the right type 1 error behavior in both the presence and
absence of gene-gene correlations. In the presence of gene-
gene correlations, the maxmean method [10] also does not
maintain the appropriate type 1 error behavior. As expected,
the power estimates of all three SEPEA methods for patterns
1 and 3 were significantly higher (P < 0.05, two-sample test of
proportions) than those for patterns 2 and 4, respectively.
The power estimates for patterns 1 and 3 using SEPEA_NT1
were higher than those for GSEA, demonstrating improve-
Table 1
Significance of observed pattern of DR scores across all KEGG pathways for different GEO datasets
GEO accession number Description
P-value
[GEO:GDS2744]
[GEO:GDS2649](1)
[GEO:GDS2649](2)
[GEO:GDS2649](3)
[GEO:GDS2852](1)
[GEO:GDS2852](2)
[GEO:GDS2852](3)
[GEO:GDS2852](4)
MCF-7 breast cancer cells - dioxin treatment versus control
Early HIV infection CD8+T cells versus uninfected
Chronic HIV infection CD8+T cells versus uninfected
Non-progressive HIV infection CD8+T cells versus uninfected
Bronchial A549 cells - cytokine treatment at 0 h versus control
Bronchial A549 cells - cytokine treatment at 4 h versus control
Bronchial A549 cells - cytokine treatment at 12 h versus control
Bronchial A549 cells - cytokine treatment at 24 h versus control
0.005
<0.001
0.001
0.004
0.001
<0.001
<0.001
0.016
Different control versus treated conditions in three microarray datasets indicated by the GDS accession numbers [GEO:GDS2744],
[GEO:GDS2649] and [GEO:GDS2852] from the GEO database were used [45] to compare the DR scores across all the pathways on the pathway
network (Figure S1 in Additional data file 2) using the meta_DR term in Equation 9. The P-value for the significance of meta_DR is computed using
1,000 random networks whose generation is described in the Materials and methods section.

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ment in the ability to detect these biologically relevant pat-
terns. For the other two 'not-so-relevant' patterns (2 and 4),
SEPEA_NT1 was not always more powerful than the GSEA
method. This loss of power can again be explained by the bias
of SEPEA to detect conditions favored by the scoring rules.
For example, the power estimates of SEPEA_NT1 were also
higher than those for GSEA [35] for pattern 2 whereas this
was not the case for pattern 4. At an empirical size of 0.07,
maxmean does not appear to be competitive with the other
methods. SEPEA_NT1 also provides a more powerful method
than GSEA on pattern 1 across a range of perturbation levels
and signal to noise levels (Tables S3 and S4 in Additional data
file 1). In addition, power results for four other correlation
patterns are presented in Table S2 in Additional data file 1.
Analysis using lung cancer data
The study by Raponi et al. [46] analyzes gene expression data
taken from 130 lung cancer patients in different stages of the
disease. They also provide survival times for each patient. The
data are divided into two groups of 85 patients (training set)
and 45 patients (test set). This was done such that the propor-
tion of patients in each stage was approximately the same for
the two groups. Using these data, the Cox proportional haz-
ards statistic is computed for each gene on the microarray
(indicating how predictive it is of the survival time of a
patient). The next logical step is then an attempt to find what
biochemical pathways are predictive of survival. All of the
human KEGG [44] pathways are used in this analysis. The
methods used were SEPEA_NT1, GSEA and maxmean. Also,
to estimate the value of including information on the network
structure, SEPEA_NT1 was applied to the data assuming that
all the genes in the pathway are given equal weight and the
DR score is zero. This analysis is denoted by SEPEA_NT1*.
The goal of our analysis is to evaluate consistency in choosing
'significant' pathways found using the training set versus the
test set. Curves for sensitivity versus '1 - specificity' and posi-
tive predictive value versus negative predictive value are
obtained by using different cut-offs for the log of the P-values
obtained using each method; the results are shown in Figure
2. The sensitivity, specificity, positive predictive and negative
predictive values for SEPEA analyses have better ranges than
those for GSEA and maxmean. For a significant portion of the
ranges of sensitivity and specificity for GSEA and maxmean,
the SEPEA analyses provide higher sensitivity for a given
level of false positives (a point on the '1 - specificity' axis). The
same can be said about the portion of the ranges of the posi-
tive and negative predictive values of maxmean dominated by
the SEPEA analyses. From the curves for SEPEA_NT1 and
SEPEA_NT1*, we also observe the benefit of incorporating
pathway network information. An updated Figure 2 that also
includes results from SEPEA_NT2 and SEPEA_NT3 is pro-
vided as Figure S2 in Additional data file 3.
Analysis using exposure of Xenopus laevis to
cyclopamine data
Enriched KEGG pathways
SEPEA_NT2* (which is essentially the SEPEA_NT2 analysis
but does not make use of the network information of the path-
ways and is identical to the analysis of the Q2 test in [9])
methods were determined for a microarray dataset (see Mate-
rials and methods section) examining the consequences of
inhibition of Sonic hedgehog (SHH) signaling by cyclopamine
treatment of developing Xenopus laevis (Tables 5 and 6).
using SEPEA_NT2 and
Table 2
Simulation conditions for comparing various methods for path-
way enrichment
Pattern numberNetwork Correlated set () Target set ()
1
2
3
Linear
Linear
ErbbSignaling
{g1,..., g9}
UL
{g
ierb
4
5
ErbbSignaling
Linear
UE
Ø
Different correlation patterns (1-5) considered for the generation of
simulated data along with the underlying networks, the set of
correlated genes, , and the set of genes that are the targets of the
treatment, . UL denotes a uniformly randomly drawn set of nine genes
drawn from the set of genes associated with the pathway displayed in
Figure 1a. V41L denotes a set of 41 randomly drawn genes from the set
of 470 genes not associated with the pathway displayed in Figure 1a. UE
denotes a uniformly randomly drawn set of seven genes drawn from
the set of genes associated with the pathway displayed in Figure 1b. V3E
denotes a set of three randomly drawn genes from the set of 413
genes not associated with the pathway displayed in Figure 1b. Ø
denotes the empty set. The symbol  denotes the set union operation.
{
{
{
}g
g
g
VL
V
∪
}
1
V
∪
V
∪
1 41
∪
}
i
L
41
1
,,}g
ierb
17

V
ierb
L
3
∪
{
U
}g
i
L
43
1
LL
41
Table 3
Type 1 error of different pathway enrichment methods
Pattern number
ASEPEA_NT1 GSEA MaxmeanSEPEA_NT2 SEPEA_NT3
1 0.01
0.05
0.01
0.05
85 85
187
12
48
13
44
7
52
135
266
15
53
36
7
51
51
12
45
5
Type 1 errors (in terms of the number of experiments out of 1,000 that gave P-values for the randomization tests below  = 0.01 and 0.05 levels) for
each of the five methods and for correlation patterns 1 and 5.

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Based on the specificity of cyclopamine to inhibit the SHH
pathway, we expected to see the SHH signaling pathway sig-
nificantly enriched; however, the P-value for this pathway
was not significant using either method (SEPEA_NT2 and
SEPEA_NT2*). This may be due to the time point at which
gene expression was evaluated, which was optimized to eval-
uate downstream effectors of SHH pathway inhibition. Alter-
natively, this result may also reflect the limitation of the
method when using only gene expression datasets, as several
components of the SHH pathway, including Hedgehog (Hh)
and Patched (PTCH), are known to be regulated at the protein
level. Finally, when results obtained using SEPEA_NT2 ver-
Table 4
Power of different pathway enrichment methods
Pattern number
A SEPEA_NT1GSEA Maxmean SEPEA_NT2SEPEA_NT3
10.01
0.05
0.01
0.05
0.01
0.05
0.01
0.05
328
610
271
505
344
692
166
361
188
510
189
508
222
496
212
468
52 357
686
295
580
347
712
157
379
321
2 3739
3 32480
4 3211
Power estimates for the SEPEA_NT1, GSEA and SEPEA_NT2 methods (in terms of the number of experiments out of 1,000 that gave P-values for the
randomization tests below nominal sizes of  = 0.01 and 0.05). The estimates for maxmean are given at an empirical size of 0.07 (nominal size of
0.001) and those for SEPEA_NT3 at an empirical size of 0.05 (nominal size of 0.001). These are results from simulations in which the treatment
resulted in an over-expression of the mean expression of the target genes by the factor pert = 1.2. The methods were evaluated on correlation
patterns 1-4.
Receiver-operator characteristic and positive predictive power versus negative predictive power plots for lung cancer data
Figure 2
Receiver-operator characteristic and positive predictive power versus negative predictive power plots for lung cancer data. (a) Sensitivity versus '1 -
specificity' of enriched pathways that are predictive of survival from lung cancer for four methods: SEPEA_NT1, SEPEA_NT1*, GSEA and maxmean.
SEPEA_NT1* is the same analysis as SEPEA_NT1 except that the pathway network information was not used. (b) Positive predictive power (ppp) versus
negative predictive power (npp) for the same data and using the same methods of analysis as in (a).
0 0.050.1 0.15
1−specificity
0.2 0.250.30.35
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Sensitivity
(a)


0.5 0.60.7 0.80.91
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Negative predictive value
Positive predictive value
(b)


SEPEA_NT1
SEPEA_NT1*
GSEA
MaxMean
SEPEA_NT1
SEPEA_NT1*
GSEA
MaxMean

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sus SEPEA_NT2* are examined in the context of pathways
linked to the SHH pathway (Figure S1 in Additional data file
2), we see that only the MAPK and Proteasome pathways are
reachable from the SHH pathway by two and three edges,
respectively, suggesting that results from SEPEA_NT2 may
be more consistent with targets downstream of the SHH
pathway. None of the other pathways listed in Tables 5 and 6
were reachable along the network of pathways (Figure S1 in
Additional data file 2) from the SHH pathway. In fact, recent
evidence suggests that SHH promotion of proliferation and
differentiation in muscle [47] and gastric mucosal cells [48] is
through transcription-independent activation of the MAPK/
ERK pathway. This analysis suggests benefits of using path-
way network information. Additional results from analysis of
these data with SEPEA_NT1, SEPEA_NT3, GSEA and
maxmean are provided in Additional data file 4.
Analysis using OMIM breast cancer data
Genes associated with breast cancer were downloaded from
the Online Inheritance in Man (OMIM) database [49]. This
group of genes was pruned to include only those genes that
participate in a pathway in the KEGG pathway database [44].
The list of genes used is provided in Table S5 in Additional
data file 1. The SEPEA analysis was used to test whether there
is an overabundance of 'important' (as defined by the scoring
rules) breast cancer genes in pathways relative to the remain-
ing set of genes that participate in some pathway in the KEGG
pathway database [44]. Using these data, SEPEA_NT3 and
SEPEA_NT3* (which is essentially the SEPEA_NT3 analysis
but does not make use of the network information of the path-
ways and is very similar to those used in [7,9,11-22]) was used
to find the enriched human pathways associated; the results
are given in Table 7. Several of the pathways known to be
important for breast cancer initiation and progression are sig-
nificant using either method, such as the ErbB, p53, and
apoptosis pathways. In contrast, the adherens junction, regu-
lation of actin cytoskeleton, cell adhesion molecules, and
focal adhesion pathways are significant using SEPEA_NT3,
but are not considered significant using the SEPEA_NT3*
method (P  0.05). These pathways, in particular the focal
and cell adhesion pathways, all deal with cell to cell commu-
nication and are thought to be key modulators of progression
and invasion of malignant phenotypic characteristics [50]. In
fact, several novel cancer chemotherapy drugs are being
designed to specifically act on the focal adhesion pathway and
Table 5
Enriched X. laevis pathways due to cyclopamine treatment using
SEPEA_NT2
KEGG pathway ID Pathway description
P-value
[path:xla03022]
[path:xla04010]
[path:xla00460]
[path:xla00550]
[path:xla02010]
[path:xla03050]
[path:xla00982]
[path:xla00830]
[path:xla04630]
[path:xla04012]
Basal transcription factors
MAPK signaling
Cyanoamino acid metabolism
Peptidoglycan biosynthesis
ABC transporters
Proteasome
Drug metabolism - cytochrome P450
Retinol metabolism
Jak-STAT signaling
ErbB signaling
0.01
0.02
0.024
0.031
0.045
0.05
0.053
0.059
0.07
0.1
Enriched KEGG [44] pathways (with P-value  0.1) due to cyclopamine
treatment of developing X. laevis, designed to inhibit SHH signaling,
using microarray data from GEO [45] [GEO:GSE8293]. P-values were
obtained using the SEPEA_NT2 analysis with 1,000 randomizations to
compute significance.
Table 6
Enriched X. laevis pathways due to cyclopamine treatment using SEPEA_NT2*
KEGG pathway ID Pathway description
P-value
[path:xla00930]
[path:xla03030]
[path:xla00480]
[path:xla00561]
[path:xla03010]
[path:xla00982]
[path:xla00983]
[path:xla04012]
[path:xla03060]
[path:xla00562]
[path:xla04914]
[path:xla04020]
Caprolactam degradation
DNA replication
Glutathione metabolism
Glycerolipid metabolism
Ribosome
Drug metabolism - cytochrome P450
Drug metabolism - other enzymes
ErbB signaling
Protein export
Inositol phosphate metabolism
Progesterone-mediated oocyte maturation
Calcium signaling pathway
0.006
0.011
0.016
0.023
0.045
0.057
0.057
0.067
0.072
0.086
0.087
0.089
Enriched KEGG [44] pathways (with P-value  0.1) due to cyclopamine treatment of developing X. laevis, designed to inhibit SHH signaling, using
microarray data from GEO [45] [GEO:GSE8293]. P-values were obtained using the SEPEA_NT2* analysis with 1,000 randomizations to compute
significance.

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Genome Biology 2009, 10:R44
many standard chemotherapy drugs modulate this pathway
in conjunction with their primary mode of action [51]. So this
analysis again suggests gains in the pathway enrichment
analysis when network details of pathways are incorporated
in the analysis.
Conclusions
This paper presents a new method that uses biological data in
order to find biochemical pathways that are relevant to the
different responses of an organism to two different condi-
tions. Biochemical pathways, instead of being treated as just
sets of genes, are viewed as a network of interactions between
proteins or metabolites. The extensive analysis using simu-
lated and real data clearly demonstrates the utility of incorpo-
rating information on the interactions between the genes
present in a pathway network.
Materials and methods
Notation
Assume there are m genes (identified by indices in the set G =
{1, 2,..., m}) in the system and n array measurements (nc con-
trol and nt treated, nc + nt = n) per gene. We will analyze one
particular pathway made up of a subset mP of the m genes in
the system. Without loss of generality, assume that these
genes correspond to the first mP gene indices in G. The genes
in this pathway are part of an underlying network of their
gene products. On the basis of this network, gene i of the
pathway is assigned a weight wi and a gene pair (i and j) is
assigned two weights dij (denoting a measure of the distance
between these two genes on the network) and eij (which is
equal to 1 for a non-zero value of dij). Each of the m genes is
also assigned a value, tstat, k for gene k capturing the treatment
effect on it as found in the observed data. This value obtained
under the different null distributions (as defined in the next
section) is denoted by Tstat, i. The two scores, from the Heavy
Ends Rule and the Distance Rule are denoted by HER and
DR, respectively. They are a function of tstat, k. HERobs and
DRobs denote those obtained from the observed experimental
data while HERrand and DRrand those obtained from the dif-
ferent null distributions.
Null hypotheses
Null hypotheses for the three statistical tests performed are
given below and share similarities with those stated in [6].
Network test 1 (NT1): Tstat, i, i = 1, 2,...m are identically distrib-
uted (and possibly dependent) with common distribution, F0
corresponding to the lack of association with the treatment,
for each gene.
Network test 2 (NT2): Tstat, i, i = 1, 2,...mp (only genes in the
pathway) are identically distributed (and possibly dependent)
with common distribution, F0 corresponding to the lack of
association with the treatment, for each gene.
Table 7
Enriched human pathways for susceptibility to breast cancer
KEGG pathway ID Pathway descriptionSEPEA_NT3 SEPEA_NT3*
[path:hsa04370]
[path:hsa04662]
[path:hsa04630]
[path:hsa04520]
[path:hsa04810]
[path:hsa04150]
[path:hsa04664]
[path:hsa04510]
[path:hsa04012]
[path:hsa04210]
[path:hsa03440]
[path:hsa04660]
[path:hsa04010]
[path:hsa04910]
[path:hsa04514]
[path:hsa04115]
[path:hsa04620]
VEGF signaling pathway
B-cell receptor signaling
Jak-STAT signaling
Adherens junction
Regulation of actin cytoskeleton
mTOR signaling
Fc epsilon RI signaling
Focal adhesion
ErbB signaling
Apoptosis
Homologous recombination
T cell receptor signaling
MAPK signaling
Insulin signaling
Cell adhesion molecules
P53 signaling
Toll-like receptor signaling pathway
1.69E-04
3.32E-04
8.91E-04
0.0014
0.0027
0.0047
0.0081
0.0103
0.0103
0.0108
0.0147
0.0182
0.0183
0.0191
0.0274
0.0306
0.0391
5.14E-04
3.51E-04
0.0417
0.1438
0.0717
0.0052
5.99E-04
0.0648
8.51E-04
7.97E-04
0.0016
0.001
0.0183
0.0032
0.2407
0.0093
0.0193
Enriched KEGG [44] pathways (with P-value  0.05) obtained using genes from the OMIM database [49] that confer susceptibility to breast cancer. P-
values were obtained using the SEPEA_NT3 and SEPEA_NT3* analysis.

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Network test 3 (NT3): Tstat, i, i = 1, 2,...m are independent and
identically distributed with a common distribution, F (which
can take any form).
In all three hypotheses, HERobs and DRobs are each drawn
from the distribution of HERrand and DRrand, respectively.
Association value computations
For each gene we define by a pair of values (, ) corre-
sponding to the association with the treatment in the context
of the observed data. The association of any given gene with
treatment is given in terms of the square of the two-sample t-
statistic (similar to what has been done in [6,25,35]) and also
shares similarities with the maxmean statistic defined in [10].
Mathematically:
where , are the sample mean gene expression for gene
gi of the control and treated data, respectively,
associated standard deviations, INT1 is equal to 1 when the NT1
, are the
test is being used and is equal to zero otherwise, denotes
the position of gene i in the sorted (in descending order) list
of max(tstat, k, 0) over all the m genes, and, similarly,
denotes the position of gene i in the sorted (in ascending
order) list of min(tstat, k, 0). a and b are parameters chosen
empirically in order to control for the selection of the pathway
with the most significant genes (relative to the other genes in
the system). The first terms in the products on the right-hand
side of Equation 2 will be called importance factors for a gene.
These are values between 0 and 1. The functions 'mean' and
'var' refer to the standard definitions of mean and variance.
The term CF denotes a (competitive) factor that is a measure
of difference in the mean of differential expression of the
genes in the pathway and that of the other genes in the sys-
tem. Higher CF values indicate higher individual association
values for genes in the pathway relative to the other genes and
vice versa. Therefore, for similar values for changes in gene
expression (tstat, i s) the power to detect treatment effect
decreases as the CF factor decreases (or as more genes in the
system are affected as a result of the treatment). For high val-
ues of the CF factor, parameter a controls the (decreasing)
importance of genes along the sorted list. The parameter b
provides a much steeper decrease in the importance of genes
down the sorted list for small values of the CF factor.
Here, tstat, i is the standard two sample t-statistic. In some
instances, the only information of the association of a gene
with a treated condition may be just a summary statistic. For
example, there are a set of known gene polymorphisms asso-
ciated with breast cancer; in trying to identify pathways rele-
vant for breast cancer, these genes would then be arbitrarily
assigned a tstat, i equal to 1 while the other genes would be
given values of 0. Note that in these situations, n, the number
of array measurements per gene, is zero.
Definition of the scoring rules
The score for linking the observed expression data to a given
pathway has two components. The first component is called
the Heavy Ends Rule score HERobs and will have a high value
when a combination of the more 'important' genes (those
associated with gene products close to a terminal of a path-
way) is significantly associated with the treated condition.
The second component called the Distance Rule score DRobs
has a high value when the genes that are significantly associ-
ated with the treated condition have their gene products
located close together. It is in fact the reciprocal of the
weighted average distance between the genes in the network.
The weights wi, dij and eij are defined in a subsequent section.
Each score is defined as the maximum of individual expres-
sions dependent either only on the genes whose expression
increased due to the treatment or on the genes whose expres-
sion decreased as a result of the treatment. This should make
it more robust to detect changes in both scale and location as
discussed in [10]. The two scores are defined as:
ti
+
ti
−
t
xi
t
xi
+(
c
si
t
nt
si
c
nc
stat i ,=
−
⎛
⎝⎜
⎞
⎠⎟
())
22
(1)
t
ri
m
t
t
ri
m m
i
a be
+
I
stat i
i
CF
NT
+
−
=−
+
⎛
⎜
⎜⎜
⎝
⎞
⎟
⎟⎟
⎠
⋅
()
=−
−
−
10
1
1
2
()
,
max,
t
a be
+
I
stat i
CF
NT
⎛
⎜
⎜
⎝
⎞
⎟
⎟
⎠
⋅
()
−
()
,
min,
1
0
2
(2)
CF
tstat i i
mP
,}
=
mP
=
tstat i i mP
tstat i i mP
=
m
=
ts
=
−
+
max
({
,}
) ({
,}
)
var({
mean mean
2
1
2
1
t tat i i
mP
m
m mP
−
)
var({
,}
)
()
,
2
1
2
1
0
+
+
⎛
⎜
⎜
⎜
⎜
⎜
⎜
⎝
⎞
⎟
⎠
⎟ ⎟
⎟
⎟
⎟
⎟
(3)
xi
c
xi
t
si
c
si
t
ri
+
ri
−
HERw t w t
DR
titj
obs i i
i
m
∑
i i
i
m
∑
obs
PP
=
⎛
⎜
⎜⎜
⎝
mP
∑
⎞
⎟
⎟⎟
⎠
=
+ +
+
=
−
=
max,
max
11
e eij
j
mP
∑
i
mP
∑
titjdij
j
mP
∑
i
titjeij
j
mP
∑
i
mP
∑
==
+ +
==
− −
==
11
11
11,
,
,
m mP
∑
titjdij
j
mP
∑
i
− −
==
⎛
⎜
⎜
⎜
⎜
⎜⎜
⎝
⎞
⎟
⎟
⎟
⎟
⎟⎟
⎠
11
(4)

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Genome Biology 2009, 10:R44
For the DR score computation, 0/0 is defined to be equal to
zero. The scores obtained under the null distributions are
denoted by HERrand and DRrand and are defined as in Equa-
tion 4 with ti replaced by Ti.
Test statistic and significance evaluation
For each of the three hypotheses (NT1, NT2 or NT3) the test
statistic is defined as:
where mean(HER) and std(HER) refer to the mean and
standard deviation of the HER score for the given test and
mean(DR) and std(DR) are those for the DR score.
For the NT1 and NT2 tests, multiple random samples of
arrays are taken from the common set of treated and control
data (without replacement) and randomly assigned to control
or treated groups. For each random sample, the Tstat, is are
calculated and then HERrand and DRrand are computed. The
NT1 test requires Tstat, i to be computed for all the m genes
while the NT2 test requires computation for just the mP genes
that are part of the pathway. For the NT3 test, multiple ran-
dom samples of mP Tstat, i s are drawn from the global set of m
observed tstat, i.
The estimate of the P-value for each of the tests is computed
as:
where I(Si  Sobs) is an indicator function that equals 1 when
the ith randomly estimated test statistic value, Si, equals or
exceeds the observed value and 0 otherwise. The estimation
procedure used for the special case when the data are in the
form of a list of differentially expressed genes or a list of genes
associated with a disease is provided in Additional data file 1.
The way the significance computations are performed, tests
NT1 and NT3 could be viewed as belonging to the class of
'competitive' hypotheses (as elaborated in the Background
section) while NT2 could be viewed as a 'self-contained'
hypothesis.
The method when applied to each of the three null hypotheses
NT1, NT2 and NT3 is denoted by SEPEA_NT1, SEPEA_NT2
and SEPEA_NT3, respectively.
Generation of simulated data
Data were simulated from two genetic systems (Linear (L)
and ErbbSignaling (E)) of 500 genes ( and
). Each system had two subnetworks of interest
and each subnetwork was assumed to have no interactions
with the other subnetwork. The Linear network had a set of
30 genes that were connected in a linear
fashion (Figure 3a). A set of 87 genes in
the ErbbSignaling network interacted in the same manner as
described by the Erbb signaling pathway in the KEGG path-
way database [44] (Figure 3b). Pathway enrichment analysis
was performed on these two subnetworks.
Each set  and H had a subset of genes (with indices
), , whose expressions were perfectly
correlated with each other (L had ncorr = 0 or 9 genes and E
had ncorr = 7 genes). The gene expressions in the complement
of each of the sets L and E, (L)c and (E)c, were assumed to
be independent of each other even though some of them could
be assumed to be known to have gene products that interact
with gene products of genes in L and E. This could be justi-
fied by the fact that the interaction was not at the gene expres-
sion level and involved changes in the phosphorylation/
binding states of the protein, for example. Let
denote the set of gene indices associated with the proteins cir-
cled in Figure 3b, ordered from left to right. The random var-
iable defining the gene expression of gene gn is denoted by Xn.
Let N(, ) represent the normal probability distribution with
mean  and standard deviation . Then data for all the 500
genes in each of the two systems were generated for one
experiment under control conditions in the following man-
ner:
Let  (L and E) denote the set of genes that are direct tar-
gets of the treatment. The total number of genes in the system
affected by the treatment (that includes the set ) was chosen
to be 50 and 10 for the Linear and ErbbSignaling networks,
respectively. The effect of the treatment was to increase the
mean of the expressions of the direct targets by a factor pert,
' = pert·. Results from the assignment pert = 1.2 are dis-
cussed here while those resulting from other assignments are
discussed in Table S3 in Additional data file 1. Let UL and UE
denote a uniformly random selection of ncorr genes from the
sets  and H, respectively, let VnL and VnE denote sets of n
genes drawn from the complements of the sets  and H,
respectively, and let Ø denote the empty set. The details of the
different correlation patterns considered here are given in
Table 1. Patterns 1 and 3 were the correlation patterns that
were favored by the scoring rules described in this paper.
S
HER mean HERNT
std HERNT
(
DR mean DRNT
std DRNT(
=
−
+
−
()
)
()
)
(5)
p
I Si Sobs
randomizations
#
i randomizations:
=
≥
()
∑
(6)
{}
, ,..,
gn
L
n=1 2 500
{}
, ,..,
gn
E
n=1 2500
({})
, ,..,
Λ =
=
gn
L
n 1 230
({})
, ,..,
Hgn
E
n
=
=1 287
{ }ij
j
ncorr
=1
Σ =
=
{}gi
j
n
j
corr
1
{} ierbj
j=1
7
X
XXjn
Xnk
i
ii corr
n
k
i
jj
=
=+=
=∈
−
=
Ν
Ν
( , )
,,,
( , ), { } \ {
10 1
22
10 1
1
1
500

i ij
j
ncorr
}=1
(7)

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Genome Biology 2009, 10:R44
All methods in this paper were coded using the Java program-
ming language. For each combination of correlation pattern
and pert assignment, 1,000 independent experiments were
simulated. Each experiment involved the generation of nc = 5
control samples and nt = 5 treatment samples. For the rand-
omization tests of each method, 1,000 randomizations were
chosen. The performance measures chosen were the number
of experiments out of the 1,000 performed that resulted in P-
values for the test (Equation 6) below different chosen signif-
icance levels. The methods evaluated were GSEA [35],
maxmean [10], SEPEA_NT1, SEPEA_NT2 and SEPEA_NT3.
For the SEPEA_NT1 method, the parameters a and b in
Equation 2 were empirically set to equal 2 and 5, respectively.
Parameter a = 2 provides a quadratic decrease in the impor-
tance of genes along the sorted list for high values of the CF
factor (when the mean changes in expression of the genes in
the pathway are higher than that of the rest of the genes in the
system). In the situation of low values of the CF factor, the
Schematic of networks used to generate simulated data
Figure 3
Schematic of networks used to generate simulated data. Illustrative schematic of the two pathways used to generate the simulated data. (a) The Linear
network of 30 nodes/gene products, each of which is associated with one gene. The pair of squiggly lines across some arrows is used to indicate that there
are more nodes that are not shown. (b) The Erbb signaling pathway from the KEGG pathway database [44]. The expressions of the genes associated with
the nodes circled in red are correlated with each other and are the genes that were affected by the treatment.
(b)
12
15
29
30
(a)

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Genome Biology 2009, 10:R44
value b = 5 was chosen such that the top 20% of genes in the
sorted list approximately receive importance in the interval
(0.2, 1) while the remaining genes receive weights in the
interval (0, 0.2). Results from GSEA [35], maxmean [10] and
SEPEA_NT1 are comparable because all test a similar null
hypothesis. The main difference between these methods is
that while GSEA and maxmean are blind to the structure of
the biochemical pathway, SEPEA-NT1 is not.
Assignment of network weights
The pathway network is represented by a set of nodes/gene
products and set of edges between these nodes. The nodes
represent gene products such as individual proteins or pro-
tein complexes. There is an edge from node/protein u to
node/protein v if u transfers the signal it received immedi-
ately to v (either in the form of increasing the transcription of
genes associated with v, changing the phosphorylation state
of v, causing disassociation of v from a complex that it is part
of) in the case of signaling pathways or that u and v catalyze
two successive reactions in the case of metabolic pathways.
Let denote the set of P nodes of the network and
denote the set of N genes associated with the
nodes. The number of edges entering node vi is defined as its
in-degree and the number of edges leaving vi is defined as its
out-degree. We define a node to be a terminal node if either
its in-degree or out-degree is zero.
Assume that each edge represents a unit distance between the
two nodes that it connects. So if the shortest route between
two nodes is via two edges in the pathway network, then the
two nodes are said to be 2 units of distance apart. Note the
phrase 'distance between a pair of nodes' is used to imply
'shortest distance between this pair', considering that there
may be more than one path connecting the two nodes in the
pathway network. Let j denote the shortest distance of node
vj to a terminal node of the pathway. Let G(vi, ga) denote the
indicator function, which is equal to 1 when gene ga is associ-
ated with node vi and is equal to 0 otherwise. The number of
genes associated with node vi is denoted by Ni. Let sij denote
the distance from node vi to node vj in the network. sij is
assigned a value of 0 either when i = j or when node vj is
unreachable from node vi. Define the positive indicator func-
tion, I+(x), which is equal to 1 when x is positive and equal to
0 otherwise.
The weights for gene ga, wa, and gene pair (ga, gb), dab and eab,
are given by:
The weight wa is defined such that genes associated with
nodes closer to the terminal nodes have higher weights than
those that are further away. The choice of a linear function to
capture the intuition behind the HER is arbitrary and other
functions will be experimented with as part of future work.
The non-zero weights dab for genes a and b are smaller if they
are associated with gene products that are closer together in
the pathway network than for pairs of genes whose gene prod-
ucts are further away.
Statistical test for Distance Rule justification
Let the total number of pathways (nodes) in the network in
Figure S1 in Additional data file 2 be denoted by Np. Denote
the distance between pathways i and j on this pathway net-
work by . Define to be equal to zero if pathway j is not
reachable from pathway i. Also define variable , which is
equal to 1 for all non-zero values of the corresponding
and 0 otherwise. Perturbations to one pathway are trans-
ferred across the edges of the network to multiple pathways.
Using human microarray data randomly chosen from the
GEO database [45], we considered eight comparisons
between two conditions (Table 1). For each comparison, the
DR score was computed (Equation 4) for every human path-
way on the network of pathways described above. In order to
make the comparison possible across all the pathways, the DR
scores obtained above using experimental data were normal-
ized with DR scores obtained by setting the Tstat, i values for all
the genes equal to 1. Let the normalized DR score for pathway
i be denoted by . A meta score can now be defined on the
pathway network as follows:
Higher values of meta_DR would indicate that pathways with
higher values of the normalized DR scores are closer to
{}
, ,..
vk k=1 2 P
{}
, ,..
ga amP
=1 2
w G v g(
i
kP
k
aia
i
P
=⋅−
=
+
⎛
⎜
⎜
⎜
⎝
⎞
⎟
⎟
⎟
⎠
⎛
⎜
⎜
⎜
⎝
⎞
⎟
⎟
⎟
⎠
=∑
,)
max
, ,1 2
{}
1
1
1
δ
δ

, ,
,
if the pathway is a signaling on
if it is a metabolic
∑
11
∑
11
= 1
pathway
d
G vigaG vjgbsij
NiNj
⋅
e
G vi
(
ab
j
P
i
P
=∑
P
=∑
mn
=
⋅⋅
=
=
P
(,)(,)
,
g gaG vjgb I
NiNj
sij
ji
)(,)()
⋅ ⋅+
⋅
=
(8)
dij
p
dij
p
eij
p
dij
p
DRi
meta DR
DRi
j
Np
∑
DRjeij
p
i
Np
∑
DRi
j
Np
∑
DRjdij
p
i
Np
∑
_
=
==
==
11
11
(9)
DRi

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Genome Biology 2009, Volume 10, Issue 4, Article R44 Thomas et al. R44.13
Genome Biology 2009, 10:R44
each other. The significance of the obtained meta_DR scores
are tested using random networks generated by the Markov-
chain switching algorithm [52]. The properties of these ran-
dom networks are that they have the same number of nodes
and edges as the original pathway network and the degree
sequence among all the nodes is also maintained. These net-
works differ, however, from the original network due to a
number of random edge swaps across the network.
GeneChip experiments
Cyclopamine powder (11-deoxyjervine; Toronto Research
Chemicals Inc., North York, Ontario, Canada) was dissolved
in 100% ethanol to a concentration of 5 mg/ml and this stock
solution was stored at -20°C. A similar volume of 100% etha-
nol was stored at -20°C for use in vehicle control exposures.
Approximately 200 tadpoles from each of two clutches (des-
ignated 'clutch A' and 'clutch B') of the species Xenopus laevis
were obtained from Nasco Biology (Fort Atkinson, WI, USA)
for a total of approximately 400 tadpoles. Animals were
raised at an air temperature of 25 ± 1°C in tanks of 9 liters of
tap water treated with Stress Coat (Aquarium Pharmaceuti-
cals, Chalfont, PA, USA) and aged 1 day. Each day for three
consecutive days, as animals reached stage 52 [53], the popu-
lation of stage 52 individuals from each clutch was removed
from the clutch tanks and divided in half indiscriminately,
resulting in four exposure groups per day: a control group for
clutch A; an experimental group for clutch A; a control group
for clutch B; and an experimental group for clutch B. Each
exposure tank had between 10 and 20 individuals, with 150
ml treated water per individual. After sorting into exposure
tanks, 30 l per animal of 5 mg/ml cyclopamine solution was
added to all experimental tanks, and 30 l per animal of 100%
ethanol was added to each control tank. After 24 hours of
exposure, animals were sacrificed by over-anesthesia with
MS222, dried on a paper towel, then put into vials of
RNAlater (Ambion, Austin, TX, USA). Vials were kept at 4°C
overnight, then moved to -20°C for storage. Both hindlimb
buds were dissected off each animal at the base of the limb
using surgical scissors, placed in fresh vials of RNAlater, and
returned to -20°C for continued storage. RNA extractions
were performed using the RNeasy Mini Kit and optional
RNase-Free DNase Set (QIAGEN, Valencia, CA, USA), with
the following notes: limbs were put into a 1.5 ml microcentri-
fuge tube, residual RNAlater was pipetted off, and limbs were
crushed with a homegenizer in 200 l buffer RLT, then 300
l more buffer RLT was added; and elution was carried out
with two washes of 50 l RNase-free water. Extracted total
RNA was stored at -80°C and transferred to the WM Keck
Foundation Biotechnology Resource Center, Affymetrix
Resource Center (Yale University, New Haven, CT), where
they were again run through DNase treatment. Four control-
experimental pairs of samples were chosen, from a total of 12
pairs, based on quantity and quality of RNA as determined by
analysis on an Agilent 2100 Bioanalyzer RNA Nano chip (Agi-
lent Technologies Inc., Santa Clara, CA, USA). Samples in
each pairwise comparison were extracted from the same
number of limbs, were from the same clutch, were exposed to
cyclopamine solution or ethanol on the same day, and their
total RNA was extracted in the same batch of extractions. The
eight chosen samples were each hybridized to an Affymetrix®
GeneChip® Xenopus laevis Genome Array (Affymetrix, Santa
Clara, CA, USA) using 3 g total RNA. Data have been depos-
ited in the National Center for Biotechnology Information,
NCBI GEO with series record ID [GEO:GSE8293].
Abbreviations
DR: Distance Rule; GEO: Gene Expression Omnibus; GSEA:
gene set enrichment analysis; HER: Heavy Ends Rule; KEGG:
Kyoto Encyclopedia of Genes and Genomes; MAPK: mitogen-
activated protein kinase; NT: network test; OMIM: Online
Mendelian Inheritance in Man; SEPEA: structurally
enhanced pathway enrichment analysis; SHH: Sonic hedge-
hog.
Authors' contributions
RT and JMG designed and evaluated the research with
important suggestions from CJP and FMP. RT implemented
the research and drafted the manuscript. GFS performed the
Xenopus laevis experiments. All the authors read and
approved the final manuscript.
Additional data files
The following additional data are available with the online
version of this paper: a Word document that provides a sec-
tion on a particular estimation of P-values and additional
tables of results (Additional data file 1); a figure of the net-
work of pathways in the KEGG pathway database [44] (Addi-
tional data file 2); a figure that demonstrates the receiver-
operator characteristics of the different methodologies used
(Additional data file 3); a table with the P-values for KEGG
[44] pathways after cyclopamine treatment of developing X.
laevis, designed to inhibit SHH signaling, using microarray
data from GEO [45] [GEO:GSE8293] (Additional data file 4).
Additional data file 1Estimation of P-values and additional tables of results Estimation of P-values and additional tables of results. Click here for fileAdditional data file 2 Network of pathways in the KEGG pathway database The nodes of this network are pathways while the edges indicate the transfer of signal or material between the pathways. Click here for file Additional data file 3Receiver-operator characteristics of the different methodologies used(a) Sensitivity versus '1 - specificity' of enriched pathways that are predictive of survival from lung cancer for the six methods: SEPEA_NT1, SEPEA_NT1*, SEPEA_NT2, SEPEA_NT3, GSEA and maxmean. SEPEA_NT1* is the same analysis as in SEPEA_NT1 except that the pathway network information was not used. (b) Positive predictive power (ppp) versus negative predic-tive power (npp) for the same data and using the same methods of analysis as in (a). Click here for fileAdditional data file 4P-values for KEGG [44] pathways after cyclopamine treatment of developing X. laevisP-values for KEGG [44] pathways after cyclopamine treatment of developing X. laevis, designed to inhibit SHH signaling, using microarray data from GEO [45] [GEO:GSE8293]. P-values were obtained using SEPEA_NT1, SEPEA_NT2, SEPEA_NT3, GSEA and maxmean analyses with 1,000 randomizations to compute sig-nificance.Click here for file
Acknowledgements
This research was supported by the Intramural Research Program of the
NIH, National Institute of Environmental Health Sciences (NIEHS). RT spe-
cially thanks Dr Shyamal Peddada of the Biostatistics Branch at NIEHS for
valuable suggestions regarding the methodology. The Xenopus cyclopamine
exposure experiments were performed by GFS in Yale University's Depart-
ment of Ecology and Evolutionary Biology, and were supported in part by a
grant to Gunter P Wagner by the Yale Core Center for Musculoskeletal
Disorders, which is funded by NIH grant P30 AR-46032 from the National
Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS).
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