Abstract
Background
The identification of genes and uncovering the role they play in
diseases is an important and complex challenge. Genome-wide linkage and
association studies have made advancements in identifying genetic
variants that underpin human disease. An important challenge now is to
identify meaningful disease-associated genes from a long list of
candidate genes implicated by these analyses. The application of gene
prioritization can enhance our understanding of disease mechanisms and
aid in the discovery of drug targets. The integration of
protein-protein interaction networks along with disease datasets and
contextual information is an important tool in unraveling the molecular
basis of diseases.
Results
In this paper we propose a computational pipeline for the
prioritization of disease-gene candidates. Diverse heterogeneous data
including: gene-expression, protein-protein interaction network,
ontology-based similarity and topological measures and tissue-specific
are integrated. The pipeline was applied to prioritize Alzheimer's
Disease (AD) genes, whereby a list of 32 prioritized genes was
generated. This approach correctly identified key AD susceptible genes:
PSEN1 and TRAF1. Biological process enrichment analysis revealed the
prioritized genes are modulated in AD pathogenesis including:
regulation of neurogenesis and generation of neurons. Relatively high
predictive performance (AUC: 0.70) was observed when classifying AD and
normal gene expression profiles from individuals using leave-one-out
cross validation.
Conclusions
This work provides a foundation for future investigation of diverse
heterogeneous data integration for disease-gene prioritization.
Background
The rapid accumulation of high-throughput data along with advances in
network biology have been fundamental in improving our knowledge of
biological systems and complex disease. The emergence of network
medicine [[29]1] has explored disease complexity through the systematic
identification of disease pathways and modules. Via the analysis of
network topology and dynamics, key discoveries have been made including
identification of novel disease genes and pathways, biomarkers and drug
targets for disease [[30]2]. Network theory is making important
contributions to the topological study of biological networks, such as
Protein-Protein Interaction Networks (PPIN) [[31]3]. The study by Xu et
al. [[32]4] analyzed topological features of a PPIN and observed that
hereditary disease-genes from the Online Mendelian Inheritance in Man
(OMIM) database [[33]5] have a larger degree and tendency to interact
with other disease-genes in literature curated networks. Both Chuang et
al. [[34]6] and Taylor et al. [[35]7] have indicated that the
alterations in the physical interaction network may be a indicator of
breast cancer prognosis. Goh et al. [[36]8] demonstrated that the
majority of disease genes are nonessential and located in the periphery
of functional networks. Research by [[37]9] discovered that genes
connected to diseases with similar phenotypes are more likely to
interact directly with each other.
Identification of candidate genes associated with physiological
disorders are a fundamental task in the analysis of complex diseases
[[38]10]. Genome-wide association studies and linkage analysis have
been pivotal in the identification of candidate genes, however, the
large list of resultant genes returned are time-consuming and expensive
to analyze [[39]11]. The availability of high-throughput molecular
interaction network provides and the application of network analysis
tools such as clustering or graph partitioning have proved valuable in
disease gene prioritization [[40]12]. For instance, PPIN data
integrated with genome-wide expression profiles using DNA arrays and/or
next generation sequencing enabling the modeling of networks have aided
our understanding of how biological networks operate. A number of
computational approaches to prioritize candidate genes have been
proposed including: ToppGene [[41]13] and GeneWanderer [[42]14] which
rank candidate genes based on known associations with disease genes
using diverse data sources and methodology. The study by Vanunu et al.
[[43]15] applied a diffusion- based method named PRINCE to prioritize
genes in prostate cancer, AD and type 2 diabetes. Wu et al. proposed
the resource AlignPI [[44]16] which applied a network alignment
approach predict disease genes. The algorithm VAVIEN [[45]17] was also
developed to prioritize disease genes based on topological features of
PPINs.
These diverse studies confirm the importance and need of improving
methods to integrate diverse 'omic' sources to uncover candidate
disease genes in biological systems. To address this need, we have
developed a prioritization pipeline, which integrates diverse
heterogeneous information. We illustrate the implementation of this
framework using Alzheimer's Disease (AD) as a Case Study. AD is the
most common neurodegenerative disease which is both genetically complex
and heterogeneous. Pathological characteristics of AD include presence
of amyloid peptide plaques, mature senile plaques and neurofibrillary
tangles and loss of neurons in conjunction with the presence of
oxidative stress [[46]18]. AD can be divided into two categories early
onset AD (EOAD) (patients < 65) and late onset AD (LOAD) (patients >
65). A set of gene mutations including APP, PSEN1 and PSEN2 involved in
the amyloid beta and tau pathways have been associated with hereditary
AD. Using genome-wide association studies, Lambert et al. [[47]19]
identified the gene encoding APOE in LOAD as a risk factor along with
11 new loci. Furthermore, studies have suggested that AD is a
multifactorial disease in which many pathways are involved. This
highlights the progress, which has been made in determining the genetic
underpinnings of AD. However, there is further need for an
understanding of AD mechanisms to develop more specific diagnostic
tests and novel drug therapies to target this disease.
Our proposed pipeline integrates AD gene-expression and network data
along with ontology-based semantic similarity, topological information
and tissue information. The integration of biological data such as
semantic similarity which is independent of the gene expression
profiles and PPIN used to obtain the significant hubs is advantageous
in providing objective prioritization criteria. To evaluate the
perfromance of our proposed method we (1) train an Random Forest
classifier using features generated using the prioritized gene
candidates to predict AD sample outcome using leave-one out
cross-validation (LOOCV); (2) perform enrichment analysis and (3)
compare the candidate gene list to a manually curated reference dataset
of verified known and susceptible AD disease genes. Furthermore, we
investigate the tissues in which AD candidate disease-genes are
expressed through incorporation of tissue-specific expression data.
The remainder of the paper is organized as follows, in Section 2 the
integrative framework is described along with details on datasets and
PPINs used in the analysis. Section 3 provides a summary of the results
obtained and conclusions along with future work is presented in Section
4.
Methods
Prioritization Workflow
The schematic workflow of our computational integrative approach for
disease gene prioritization is shown in Figure [48]1. Firstly, PPIN and
gene-expression are integrated to provide an initial list of
prioritized genes. Secondly, we incorporate additional data in the form
of network topology and ontology-based semantic similarity to provide
further prioritization. This list of genes is then evaluated using
enrichment analysis and measurement of tissue specificity.
Figure 1.
Figure 1
[49]Open in a new tab
Overview of Computational Framework to Prioritize Disease Gene
Candidates.
AD gene expression data
Human AD gene expression data was obtained from the Gene Expression
Omnibus (GEO) database. ([50]http://www.ncbi.nlm.nih.gov/geo/). The
selected profile [51]GSE4757 was generated using the platform
[52]GPL570: Affymetrix Human Genome U133 Plus 2.0 Array. The study by
Dunckley et al. [[53]20] examined the transcriptome of entorhinal
neurons from six cortical areas with or without neurofibrillary tangles
(a histopathology feature of AD) using Laser capture microdissection.
The dataset consists of gene expression profiles of NFT-bearing
entorhinal cortex neurons from 10 mid-stage AD patients (Disease)
compared with 10 histopathologically normal neurons (Control) from the
same patients and brain region. These represent the different stages of
AD according to the pattern of disease spread. Using the MAS5.0
function in R the CEL files were firstly normalized. Probes in
expression profile were then mapped to corresponding NCBI Gene IDs. The
average expression value was calculated in cases where the Gene ID
related to more than one probe resulting in 20,539 unique Gene IDs.
A total of 10,106 significant genes were obtained using the
significance analysis of microarrays (SAM) [[54]21] technique, a
regularized t-test approach, using the false discovery rate (FDR =
0.98). Differentially expressed (DE) genes are genes whose expression
levels are significantly different between two groups of experiments.
Human protein-protein interaction network
The PPIN was constucted using data from literature curated sources
along with the recent Y2H screening by Rolland et al. [[55]22]. The
literature based dataset consists of 11,045 binary human protein pairs
extracted from seven publically available databases including BioGRID
[[56]23], DIP [[57]24], Biomolecular Interaction Network Database
(BIND) [[58]25], HPRD [[59]26], InACT [[60]27], Protein Data Bank (PDB)
[[61]28] and Molecular INTeraction database (MINT) [[62]29]. The
protein pairs were filtered on evidence where pairs that are only
supported by two or more pieces of evidence are included. Protein pairs
were also obtained from Y2H experimentation whereby 15,517 opening read
frames (ORFs) were systematically screened using the platform hORFeome
v5.1 (Space II) resulting in 13,944 pairwise interactions [[63]22].
Both these data are integrated to produce a PPIN consisting of 7,783
nodes and 22,621 protein pairs.
Toplogical analysis
AD gene expression data was mapped to the PPIN via NCBI geneIDs using
Cytoscape version 3.2.1 [[64]30]. This resulted in an AD disease
specific network consisting of 5,457 nodes and 10,852 protein pairs.
Hub genes were defined based on the measurement of network topological
features (1) degree and (2) betweenness centrality using Cytoscape
version 3.2.1 [[65]30].
Degree connectivity
Degree is a measure of the number of edges that connects a node. Genes
with a high degree of connectivity within a network have large numbers
of interacting partners. In PPINs it has been observed that genes with
high degrees of connectivity are more likely to be essential as genes.
Furthermore, many interacting partners in a network tend to be involved
in important cellular processes [[66]1]. Using this assumption, the top
genes with the highest degree distribution were selected as hub
proteins. This approach has previously been applied by Taylor et al.
[[67]7]. The degree cut-off threshold for selecting hubs is defined as:
[MATH: AVG+2*(Std)
:MATH]
(1)
where AVG is the average degree across all DE genes in the PPINs and
Std, the standard deviation [[68]31].
Betweenness centrality
Betweenness is a topological feature of a network measuring information
flow through the network. In biological networks, betweenness measures
the paths through which signals can pass through the interaction
network. Yu et. al. [[69]32]. identified betweenness as an important
topological property of a network where nodes with high betweenness
control most of the information flow. The betweenness centrality
C[b](n) of a node n is computed as follows:
[MATH:
Cbn=
∑s≠n≠t(θ<
mi>st(n)/θst) :MATH]
(2)
where s and t are nodes in the network different from n, θ[st ]denoting
the number of shortest paths from s to t, and θ[st](n) is the number of
shortest paths from s to t that n lies on. Betweenness centraility is
calculated in Cytoscape. Using the node betweenness distribution, genes
located in the top 50% are selected as hub genes.
Network variation of hub genes
For each hub protein in the PPIN the average of Pearson correlation
coefficients (PCC) between the hub and each of its respective partners
was calculated for both disease and control groups. This method has
previously been applied by Taylor et al. [[70]7] to measure network
variations among candidate genes and their interacting genes. To
determine if interactions are varied, the difference of AD gene
expression correlations of PPIs in disease and control samples is
calculated. The average hub difference (AvgPCC) off correlation
(Pearson's correlation co-efficient (PCC)) values between the disease
and control groups was calculated as follows:
[MATH: AvgPCC=∑i=1n(D<
mi>i-Ci)n :MATH]
(3)
where D[i ]and C[i ]represent the correlations of a hub and its
interactors for the disease and control groups respectively and n the
number of i interactors for a given hub.
Genes that are significantly different between the disease and control
groups were selected as follows: (1) labels from the AD expression data
were randomly assigned to either the disease or control group. (2) The
AvgPCC was recalculated as RandomPCC by repeating the analysis defined
in equation × 1000 times in order to calculate random distribution
values. (3) P values for each hub was calculated by:
[MATH: ∑AvgPCC≥RandomPCC1000 :MATH]
(4)
A network of significant hub genes was generated using significant
cut-off threshold of P <= 0.05. P values are adjusted using Bonferroni
correction. Random reassignment of the expression data was taken by
randomly shuffling the expression data gene labels. This method of
random reassignment retains the topological network structure of the
interactome during the randomization.
Ontology based semantic similarity
Genes involved in phenotypically similar diseases are often
functionally related on the molecular level [[71]33]. Based on this
observation, the semantic similarity between hub genes and their
interactors has been selected to analyze hub genes based on the Gene
Ontology (GO) [[72]34]. The GO is a controlled vocabulary describing
the charateristics of gene products. Semantic similarity measures
evaluate information two genes share. The functional similarity between
two proteins is estimated using encoded information in the GO
hierarchies. In this study Wang's measure of similarity [[73]35] is
applied to the Biological Process hierarchy. This measure determines
the semantic similarity of two GO terms based on the locations of terms
in the GO graph and their semantic relations with their ancestor terms.
Given a GO term A, T[A ]denotes the set of all its ancestor terms
including term A itself. S[A](t) can be defined as the contribution of
a term
[MATH: t∈TA :MATH]
to the semantics of A based on the relative locations of t and A in the
graph. Given GO terms A and B respectively, the semantic similarity
between these two terms, S[GO](A,B), is defined as:
[MATH: SGO
(A,B)=∑<
mrow>t∈TA∩TB(S<
mi>A(t)+SB(t))∑t∈TASA<
/mi>(t)+
∑t∈TBSB<
/mi>(t) :MATH]
(5)
As one gene may be annotated by many GO terms, similarity between two
genes Sim(G[1],G[2]), is then calculated by taking the average semantic
similarity scores for all pairs of their associated terms. The
similarity score can range between (0,1), whereby a value closer to 1
indicates close relatedness of the two genes in biological process.
Wang's measure was implemented using the GOSemSim package in R
[[74]36], taking the median semantic similarity between a hub protein
and its interactors.
Construction of AD reference dataset
A reference dataset containing known and susceptible AD genes was
constructed using the OMIM 'morbid map' table [[75]5]. Known and
recently discovered AD susceptibility genes in detailed in the study by
Lamberet et al. [[76]19] were also included. This resulted in a list of
52 AD related genes.
Tissue specific gene expression data
Candidate genes were filtered using tissue-specific gene expression
data retrieved from BioGPS [[77]37] to determine if these genes are
expressed in tissues where AD is observed including the tissue
locations: whole brain and prefrontal cortex. This dataset contains the
transcription levels of 84 human tissues and cell lines and was
processed using the method described by Lopes et al. [[78]38]. A list
of 570 housekeeping genes were also included, obtained from [[79]39].
Classification of disease outcome
Construction of feature sets for individual samples were generated
using the prioritized hub genes in order to classify sample outcomes
applying the approach proposed by Taylor et al. [[80]17]. Using the
gene co-expression values for each sample in the AD dataset, the
absolute difference in gene co-expression CoeDiff values of the
prioritized genes and their interactors was calculated by:
[MATH:
CoeDiff<
mo class="MathClass-rel">=
∑i=1n(I<
mi>mi-Gi)i=1 :MATH]
(6)
where the difference between the gene expression values of each
prioritized gene G and its interactors Im is calculated. This was
evaluated for all prioritized proteins across each sample in the AD
dataset.
Results and discussion
Using an integrative approach defined in the Method section, diverse
heteregenous data obtained from AD gene expression, network topological
analysis, along with semantic similarity and tissue-specific
information are combined to to generate and analyze candidate AD genes.
Selecting differentially expressed genes
The microarray [81]GSE4757 was analyzed using Significance Analysis of
Microarrays (SAM) to identify significantly expressed AD related genes
in R using the SAM 5.0 package from [[82]40]. A total of 10,107
significantly positive differentially expressed (DE) genes were
observed from 20,539 genes in the AD microarray dataset based on the
two class (disease and control) unpaired t-test, using the false
discovery rate (FDR = 0.98). These DE genes were selected for the
construction of the AD specific PPIN. An overview of the top 10 DE
genes is presented in Table [83]1 along with the SAM score based on the
t-statistic value.
Table 1.
Overview of the top 10 differentially expressed genes obtained from the
Alzheimer's microarray dataset using SAM analysis.
Gene Symbol Gene Description Score
Scd stearoyl-CoA desaturase (delta-9-desaturase) 3.41
CCDC79 coiled-coil domain containing 79 3.10
STAU2 staufen, RNA binding protein, homolog 2 (Drosophila) 2.78
Hist1h2bc histone cluster 1, H2bi; histone cluster 1, H2bg; histone
cluster 1, H2be; histone cluster 1, H2bf; histone cluster 1, H2bc 2.72
HIST1H2BE histone cluster 1, H2bi; histone cluster 1, H2bg; histone
cluster 1, H2be; histone cluster 1, H2bf; histone cluster 1, H2bc 2.69
HIST1H2BF histone cluster 1, H2bi; histone cluster 1, H2bg; histone
cluster 1, H2be; histone cluster 1, H2bf; histone cluster 1, H2bc 2.56
__________________________________________________________________
HIST1H2BI histone cluster 1, H2bi; histone cluster 1, H2bg; histone
cluster 1, H2be; histone cluster 1, H2bf; histone cluster 1, H2bc 2.53
HIST1H2BG histone cluster 1, H2bi; histone cluster 1, H2bg; histone
cluster 1, H2be; histone cluster 1, H2bf; histone cluster 1, H2bc 2.52
DSTYK dual serine/threonine and tyrosine protein kinase 2.50
Lrrc16a leucine rich repeat containing 16A 2.47
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Construction of AD PPIN
The protein pairs in the human PPIN applied in this study have been
derived from both small-scale studies described in literature and
large-scale high-throughput Y2H experimentation. We integrate both
these data in the construction of the PPIN to improve interactome
coverage. Protein pairs obtained from small-scale studies are
considered high quality, however, it has been observed by Rolland et
al. [[85]22] that their coverage of the interactome is limited to a
narrow dense zone. Protein pairs obtained via high-throughput Y2H
experimentation have demonstrated distributed homogeneously across the
interactome [[86]22].
AD gene expression data was firstly mapped to the PPIN via NCBI geneIDs
using Cytoscape version 3.2.1 [[87]30]. This network was further
filtered to include only the 10,107 DE genes identified from the SAM
analysis. The mapping of DE genes with the PPIN resulted in an AD
disease specific network consisting of 3,795 nodes and 5,410 protein
pairs.
Identification of hubs through network topology analysis
Hub genes were firstly defined based on network topological features
using Cytoscape version 3.2.1 [[88]30]. The AD PPIN is represented as
an undirected graphs, G = (V,E), whereby V represents a set of nodes
(proteins) and
[MATH: E={(u,v)|u,v∈V} :MATH]
, the set of edges connecting the nodes. An overview of the global
properties of the network is presented in Table [89]2. Using Cytoscape,
a total of 174 hub genes were selecting based upon the topological
analysis measures degree distribution and betweenness centrality. Genes
with a high degree of connectivity and genes with low connectivity but
high betweenness were selected using the cut-off thresholds defined in
the Methods section. Table [90]3 presents the number of hubs obtained
using these measures. These two measures have been selected as studies
of model organisms have observed that proteins with high degree of
connectivity tend to be encoded by essential genes [[91]41].
Furthermore, detection of these genes leads to larger numbers of
phenotypic outcomes compared to genes with lower connectivity [[92]42].
However, not all disease genes in humans are essential genes. Goh et
al. [[93]8] found that non-essential disease genes tend to be tissue
specific located at the functional periphery of the interactome and do
not necessarily encode hubs. Both Yu et al. [[94]32] and Joy et al.
[[95]43] demonstrated how nodes with a low degree of centrality but
high betweenness are important in a network. Taking this into
consideration, we also include betweenness as an indicator of
centrality.
Table 2.
Overview of the global properties of the AD specific PPIN.
Nodes Edges Clustering Co-Efficient Avgerage Degree Average Betweeness
Centraility
3,795 5410 0.026 2.85 0.009
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Table 3.
Analysis of AD PPIN Topological Features to Identify Hub Genes.
Number of Nodes
High Betweenness
(Bottlenecks) 128
Selected Hub Genes 175
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Prioritization of hubs
Step 1: Integration of co-expression and PPIN
To further filter the list of 174 identified hub genes and select
significant hubs we integrated the AD gene expression data with the
PPIN. The network variation of the hub genes and their interactors was
calculated using the AvgPCC equation defined in Methods. PCC values
between the hubs and their interactors were calculated for both the
disease and control groups. Significant hub genes were selecting using
the Bonferroni corrected cut-off threshold of P < 0:05. A total of 22
genes were identified.
Step 2: Gene ontology semantic similarity analysis
The semantic similarity between hub genes and their interactors has
also been selected to prioritize hub genes based on the Gene Ontology
(GO). It has been observed that genes involved in phenotypically
similar diseases are often functionally related on the molecular level
[[98]33]. In this study, the semantic similarity between a gene hub and
it's interacting partners was performed using Wang's [[99]35] measure
of similarity detailed in equation 4. This was applied to the GO
Biological Process hierarchy as a quantitative measure of functionality
similarity between gene pairs using the R package GOSemSim [[100]36].
To obtain the similarity value for the hub and all its interactors, the
median similarity was taken across all protein pairs. The semantic
similarity values obtained ranged between 0 and 1. The hub gene
semantic similarity distribution was sorted in ascending order and hubs
with GO semantic similarity greater than 0.5 selected. This resulted in
a total of 10 prioritized hubs. Combining candidate genes output from
analysis in Step 1 and Step 2 resulted in a list of 32 prioritized hub
genes summarized in Table [101]4.
Table 4.
List of significant hubs obtained from gene co-expression network
analysis.
Approach Number of Prioritized Hubs
Network Variation using PPIN and Gene Expression 22
Gene Ontology 10
[102]Open in a new tab
Evaluation
Functional annotation enrichment
GO and pathway enrichment analysis using the DAVID resource [[103]44]
was applied to investigate the biological implications of the
prioritized hub gene list. Functional annotation was obtained by
extracting the most over-representative GO terms (Biological Process,
Cellular Component and Molecular Function) for the groups of genes
under observation with respect to the whole genome taken as the
reference background set (p-value <0.05). We applied this approach to
measure if the prioritized hub genes are more enriched in GO terms or
involved in pathways than what would be expected by chance. The number
of GO terms and KEGG pathways are summarized in Table [104]5.
Table 5.
Number of Enriched Gene Ontology Terms and KEGG Pathways.
Number of Terms
GO Biological Process 135
GO Molecular Function 19
GO Cellular Component 17
KEGG Pathways 5
[105]Open in a new tab
Enrichment analysis of the prioritized hub genes identified significant
biological processes modulated in AD pathogenesis including: neuron
differentiation, neuron projection morphogenesis, neuron projection
development and regulation of neuron apoptosis. Furthermore,
significant KEGG pathways including: the Wnt signaling pathway and the
TGF-beta signaling pathway both of which have been implicated in
neurodegenerative diseases [[106]45,[107]46] These results highlight
the potential of this approach in using prioritized hubs for the
prediction of AD biomarkers.
Reference dataset comparison
The list of prioritized hub genes were compared to the reference
dataset consisting of 52 AD related genes. A total of 9 AD susceptible
genes from the list of hub genes identified were identified summarized
in Table [108]6. Mutations in PSEN1 are the most common cause of early
onset of AD. TRAF1, is a critical regulator of cerebral
ischemia-reperfusion injury and neuronal death [[109]47]. LZTS2 has
shown associated with late onset AD [[110]48].
Table 6.
List of AD Susceptible Genes Idenfied from the Prioritized List of Gene
Hubs.
Gene Symbol Gene Name
LZTS2 leucine zipper, putative tumor suppressor 2
MTUS2 KIAA0774
TRAF1 TNF receptor-associated factor 1
FHL3 four and a half LIM domains 3
REL v-rel reticuloendotheliosis viral oncogene homolog (avian)
CARD9 caspase recruitment domain family, member 9
TFCP2 transcription factor CP2
MID2 midline 2
KRT38 keratin 38
[111]Open in a new tab
Tissue analysis
The prioritized gene hubs were analyzed to determine if gene hubs are
expressed in tissues in whereby by AD is observed including the
prefrontal cortex and whole brain. Tissue specificity is an important
component of network analysis as genetic diseases often target specific
tissue(s). Therefore, perturbations of pathways or proteins may have
differential effects among diverse tissues [[112]49]. Using tissue
specific expression data from BioGPS [[113]37] along with housekeeping
genes obtained from Lopes et al. [[114]38], we identified that 4 of the
prioritized gene hubs are located in the whole brain and/or the
prefrontal cortex tissues.
Disease outcome classification
Using the prioritized gene-candidate list along with gene expression
values from the GEO dataset [115]GSE4757, we constructed a feature
vector applying the approach described in Methods using Equation 6.
These features measure the differences in absolute co-expression values
between the prioritized genes and their interactors across each sample
in the dataset. The aim of this approach is to determine if measured
differences in co-expression values of prioritized genes and their
interactors can predict sample outcome. A total of 32 feature values
(obtained using each of the genes in the prioritized list) were
generated for each instance in the AD dataset. The AD dataset has a
total of 20 individual samples, 10 AD and 10 normal. We use these as
labels when measuring the classification performance. Classification of
sample outcome using this vector was performed using the Random Forest
classifier [[116]50] in the WEKA toolbox [[117]51]. Leave one out cross
validation (LOOCV) was implemented, whereby, for a dataset with n
samples, n experiments are performed. Each experiment uses n-1 samples
for training and the remaining sample for testing. The performance was
assessed by a receiver operating characteristics (ROC) curve which
plots the true positive rate against the false positive rate at various
threshold settings. The area under the ROC curve (AUC) was estimated
for each of the prioritization approaches described in Table [118]7.
Reasonable AUC values were observed for the various prioritization
approaches ranging from 0.73-0.70 as presented in Table [119]7 and
illustrated using ROC curves in Figure [120]2. This analysis suggests
that the integration of diverse data in the prioritization of protein
hubs are indeed useful for sample outcome prediction. Interestingly, we
can see a slight decrease in classification performance when gene hubs
prioritized using topological features are integrated with gene hubs
prioritized using GO semantic similarity. This is compared to using
only the topological or GO semantic similarity prioritization approach
only.
Table 7.
Summary of Disease Outcome Classification using Prioritized Genes.
Priortization Approach Description Hubs AUC Value
Topology Hubs prioirtized using network topology methods defined in
Step 1 22 0.74
GOSim Hubs prioirtized using GO Semantic Similarity defined in Step 2
10 0.72
Toplogy_GoSim Union of priortized hubs from Step 1 and Step 2 32 0.7
[121]Open in a new tab
Figure 2.
Figure 2
[122]Open in a new tab
ROC Curves illustrating the predictive performance of the Gene Hub
Prioritization Approches.
Conclusions
In this study, we have proposed a novel computational framework that
integrates diverse heterogeneous including gene expression, network
topological features: degree and betweenness along with GO semantic
similarity and tissue specificity information to prioritize and analyze
disease-gene candidates. Our proposed pipeline provides the flexibility
to integrate other heterogeneous data sources. To illustrate our
approach, AD was applied as a Case Study. AD is a chronic
neurodegenerative disorder characterized by a progressive decline in
memory and cognitive abilities. It is estimated that 4-8% of the
population over 65 years of age suffers from this disease with the rate
of incidence increasing with age. Currently, only the symptoms of AD
are treated with no effective therapeutic strategy available. Using our
pipeline, 22 prioritized candidate AD disease genes were generated.
Enrichment analysis revealed GO Biological Process terms were enriched
by the prioritized gene-list such as regulation of neurogenesis and
generation of neurons which are linked to AD pathogenesis. Analysis of
KEGG pathways identified prioritized genes involved in pathways
including Wnt signaling pathway and the TGF-beta signaling pathway. AD
susceptible genes: PSEN1 and TRAF1 extracted from OMIN [[123]5] and a
recent study by Lambert et al. [[124]19] were identified using the
prioritization approach. A reasonable predictive performance (AUC:
0.70) was achieved when classifying AD and normal gene expression
profiles from individuals using a feature set generated from the
prioritized gene-list along with supervised classification using Random
forest and LOOCV. These results demonstrate that the integration of
PPINs along with disease datasets and contextual information is an
important tool in unraveling the molecular basis of diseases. It is
important to note that network based approaches for candidate disease
gene priortization need to be viewed with care as the map of the binary
human PPIN is still incomplete. However, coverage of the human
interactome is increasing through systematic Y2H and transcriptional
interaction screens. This increased coverage, quality, and diversity of
human PPIN data will provide further opportunities for the molecular
characterization and understanding of human disease [[125]1].
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
FB, HW and HZ conceived and designed the framework. FB performed the
experiments and analyzed the data. FB, HW and HZ wrote the paper.
Contributor Information
Fiona Browne, Email: f.browne@ulster.ac.uk.
Haiying Wang, Email: hy.wang@ulster.ac.uk.
Huiru Zheng, Email: h.zheng@ulster.ac.uk.
Acknowledgements