Abstract
Background
Phenotypic features associated with genes and diseases play an
important role in disease-related studies and most of the available
methods focus solely on the Online Mendelian Inheritance in Man (OMIM)
database without considering the controlled vocabulary. The Human
Phenotype Ontology (HPO) provides a standardized and controlled
vocabulary covering phenotypic abnormalities in human diseases, and
becomes a comprehensive resource for computational analysis of human
disease phenotypes. Most of the existing HPO-based software tools
cannot be used offline and provide only few similarity measures.
Therefore, there is a critical need for developing a comprehensive and
offline software for phenotypic features similarity based on HPO.
Results
HPOSim is an R package for analyzing phenotypic similarity for genes
and diseases based on HPO data. Seven commonly used semantic similarity
measures are implemented in HPOSim. Enrichment analysis of gene sets
and disease sets are also implemented, including hypergeometric
enrichment analysis and network ontology analysis (NOA).
Conclusions
HPOSim can be used to predict disease genes and explore disease-related
function of gene modules. HPOSim is open source and freely available at
SourceForge ([29]https://sourceforge.net/p/hposim/).
Introduction
Phenotypic similarity plays an important role in different biological
and biomedical applications. Previous studies prove that genes with
similar phenotypes yields biological modules in terms of diseases, thus
it can be used in predicting disease-causing genes [[30]1][[31]2].
Furthermore, it is crucial for understanding the relationships between
different diseases [[32]3].
Most current methods for measuring phenotypic similarity [[33]4][[34]5]
are based on the Online Mendelian Inheritance in Man (OMIM) database
[[35]6] that contains textual records representing genetic disorders.
However, the absence of a controlled vocabulary makes it difficult to
analyze the OMIM data using a computational approach [[36]7]. The Human
phenotype ontology (HPO) [[37]8] provides a controlled and standardized
vocabulary of phenotypic abnormalities annotating all clinical entries
in OMIM, which sheds light on the large-scale computational analysis of
the human phenome, i.e., DECIPHER [[38]9], ECARUCA [[39]10] and Bridge
[[40]11].
Several tools using HPO-based semantic similarity are currently
available. Phenomizer [[41]12] is the first tool for semantic
similarity search over HPO, in which users input the phenotypic
abnormalities of a patient as HPO IDs, and obtain a list of diagnoses
in OMIM IDs. Other tools include OwlSim [[42]13], PhenoDigm [[43]14],
PhenomeNET/PhenomeBrowser [[44]15] and OntoSIML [[45]16]. The detailed
comparison of HPOSim and other HPO-based tools is shown in [46]Table 1.
It can be seen from the table that most of the existing tools share one
drawback: the calculations of phenotypic similarity for HPO terms,
genes and diseases are not well supported. Although OntoSIML and OwlSim
provide functions for calculating semantic similarity, users are
required to manually input the mapping from entities (gene or disease)
to HPO terms, which entails additional preprocessing effort.
Table 1. Comparison of HPOSim and other HPO-based tools.
Name Release Type Open Source Term-Term Similarity Gene-Gene Similarity
Disease-Disease Similarity Gene-Disease Similarity Similarity Measures
Combine Methods
HPOSim Stand Alone (R) √ √ √ √ × Resnik, Lin, Jiang-Conrath, relevance,
information coefficient, graph IC, Wang Max, Mean, funSimMax,
funSimAvg, BMA
Phenomizer [[47]12] Web × × × × √ Resnik symmetric, unsymmetric
OWLSim [[48]13][49]^# Stand Alone (Java) √ √ √ √ √ Jaccard, Resnik,
overlap/normalized overlap, GIC Max, Mean, BMA
PhenoDigm [[50]14] Web × × × × √ Mean of Jaccard and Resnik Max, Mean
PhenomeNET [[51]15] Web × × √[52]^* √ √[53]^* simGIC Unknown
OntoSIML [[54]16][55]^# Web × √ √ √ √ Jaccard, simGIC, Resnik Unknown
[56]Open in a new tab
* PhenomeNET only supports human genes included in OMIM.
# Although OntoSIML and OwlSim provide functions for calculating
semantic similarity, users are required to manually input the mapping
from entities (gene or disease) to HPO terms, which entails additional
preprocessing effort.
“√” means the tool provides the function and “×” means the tool does
not.
In addition, there exist several tools for HPO-based enrichment
analysis. OntoFUNC [[57]17] performs functional enrichment analysis
over ontologies in OWL format. It is based on FUNC [[58]18] and users
need to manually input the mapping data, which is the same as OntoSIML.
STOP [[59]19] is an online tool and can be used as a Cytoscape plug-in.
It can be used in the enrichment analysis of gene sets, but does not
support the analysis of disease set.
Several R packages for semantic similarity and enrichment analysis are
available, including GOSim [[60]20], GOSemSim [[61]21], DOSim [[62]22],
DOSE [[63]23] and topGO [[64]24]. However, these packages mainly use
gene ontology (GO) [[65]25] and disease ontology (DO) [[66]26]. To the
best of our knowledge, there is no R package that focuses on HPO-based
semantic similarity and enrichment analysis.
Thus, we developed an R package HPOSim with an immediate purpose to
capturing phenotypic similarities between genes and diseases. The
framework of HPOSim is shown in [67]Fig. 1. HPOSim analyzes semantic
similarity for HPO terms, genes and diseases. Functional enrichment
analysis of gene set and disease set are also provided, including the
classic hypergeometric enrichment analysis and the novel network
ontology analysis (NOA) [[68]27].
Figure 1. Framework of HPOSim.
[69]Figure 1
[70]Open in a new tab
Users can use HPOSim to calculate semantic similarity for HPO terms,
genes and diseases. HPOSim can also be used to identify enriched HPO
terms for gene set and disease set.
Implementation
Data
HPO contains over 10000 terms (10686 terms in the HPO build #1042
released in September 2014) in three sub-ontologies, which are
phenotypic abnormality (PA), onset and clinical course (OC) and mode of
inheritance (MI). Approximately 99% of the HPO terms are in the PA
sub-ontology. In each sub-ontology, terms are arranged in a directed
acyclic graph (DAG) and are related to their parent terms by “is a”
relationships. The structure of the HPO allows a term to have multiple
parent terms, which enables different aspects of phenotypic
abnormalities to be explored. Diseases and genes are annotated to the
most specific terms possible, which means that if a disease or a gene
is annotated to a term then all of the ancestors of this term also
apply (see [71]Fig. 2 for an example).
Figure 2. Example of the structure of HPO.
[72]Figure 2
[73]Open in a new tab
HPO term Abnormality of the joints of the lower limbs (HP:0100491) and
all its ancestor terms are shown. Each term in the HPO describes a
phenotypic abnormality. Terms are related to parent terms by “is a”
relationships in the form of a directed acyclic graph. If a disease or
a gene is annotated to a term, it will also be annotated to all of its
ancestors.
The official ontology file provided by the HPO Consortium is in obo
format, which is plain text-based. Thus, like other widely used R
package for biomedical ontologies, e.g. GO.db, we constructed an R
package termed HPO.db. HPO.db provided programmatic interfaces to the
hierarchical structure of HPO terms. HPOSim uses HPO.db to obtain
information about terms and relationships between terms. HPO.db can be
used by other R packages that use HPO data.
HPOSim provides two kinds of pre-calculated data within the package:
the association between HPO terms, as well as association between genes
and diseases (gene-to-phenotype, phenotype-to-gene,
disease-to-phenotype and phenotype-to-disease). The associations
between HPO terms are obtained from the original ontology and
annotation data provided by the HPO Consortium, and the information
content (IC) of the HPO terms is pre-calculated based on both genes and
diseases annotated to a certain term, while semantic similarity between
genes and diseases are based on the IC of HPO terms.
The IC of a term t in HPO can be defined as follows:
[MATH:
IC(t)=
-log(p(t)) :MATH]
(1)
where p(t) is the probability of observing t and its descendants in all
genes/diseases annotated to a certain sub-ontology of HPO.
Measuring the similarity between HPO terms
Recently, several metrics that measure the semantic similarity between
ontology annotations have been proposed [[74]28]. In HPOSim, we
implement seven commonly used semantic similarity measures to measure
the similarity between HPO terms: the Resnik measure [[75]29], Lin
measure [[76]30], Jiang–Conrath measure [[77]31], relevance measure
[[78]32], information coefficient measure [[79]33], graph IC measure
[[80]34] and Wang measure [[81]35]. The first six measures are based on
IC, while the Wang measure uses both IC and graph structure.
The Resnik measure defines the similarity between terms as the IC of
their most informative common ancestor (MICA):
[MATH:
simResnik(t1,t2)=IC(t
MICA) :MATH]
(2)
where t[MICA] is the MICA of term t[1] and t[2].
The Lin and Jiang–Conrath measures consider the IC of the two terms
besides the IC of their MICA:
[MATH:
simLin(t1,t2)=2×IC(tMICA)IC(t1)IC(t2) :MATH]
(3)
[MATH:
simJC(t1,t2)=
1-(IC(t1)+IC(t2)−2×IC(t<
/mi>MICA
)) :MATH]
(4)
The relevance measure and the information coefficient measure are based
on Lin’s measure:
[MATH:
simRel(t1,t2)=simLin<
/mi>(t1,t2)×(1−p(
tMICA))
:MATH]
(5)
[MATH:
simIC(t1,t2)=simLin<
/mi>(t1,t2)×(1−11+IC(tMICA)<
/mo>) :MATH]
(6)
The graph IC measure takes all the common ancestors of the two terms
into account:
[MATH:
simGraphIC(t1,t2)=∑t∈<
mo>(A(t1)∩A(t
2))IC(t)∑t∈<
mo>(A(t1)∪A(t
2))IC(t)<
/mrow> :MATH]
(7)
where A(t) is the ancestors of term t in HPO.
The Wang measure is based on the graph structure of HPO DAG. In Wang’s
measure, a weight is given to each edge according to its type. DAG[t] =
(t,T[t],E[t]) represents the subgraph made up of term t and its
ancestors, where T[t] is the set of the ancestor terms of t and E[t] is
the set of edges in DAG[t].
In DAG[t], S[t](n) measures the semantic contribution of term n to term
t, which is defined as:
[MATH:
{St(t)=1St(n)=max{we*St(n')|n
'∈childrenof
(n)}
ift≠n :MATH]
(8)
The similarity between HPO term t[1] and termt[2] is defined as:
[MATH:
simWang(t<
/mi>1,t2)=∑t∈<
mi>Tt1∩Tt2<
/mrow>St1(t)+St2(t)SV(t1)+SV(t2) :MATH]
(9)
where SV(m) is the sum of the semantic contributions of all the terms
in DAG[m].
Combining term-term similarity into gene-gene and disease-disease similarity
In HPOSim, the similarity between two genes is calculated based on the
pairwise similarity of the two HPO term sets annotating these two
genes. HPOSim provides five methods to combine multiple term-term
similarities into one gene-gene similarity, which are “Max” [[82]36],
“Mean” [[83]36], “funSimMax” [[84]32], “funSimAvg” [[85]32], and “BMA”
[[86]35].
Given gene g[1] annotated by HPO term set HPO[1] = {t[11],
t[12],…,t[1m]} and g[2] annotated by HPO[2] = {t[21], t[22],…,t[2n]}.
The similarity matrix S=[s[i j]][m×n] contains all pairwise similarity
scores of terms in HPO[1] and HPO[2].
The “Max” method calculates the maximum semantic similarity score over
all pairs of HPO terms in the two term sets, and is defined as follows.
[MATH:
SimMax(g1<
/mn>,g2)
=max1≤
i≤m,1≤j<
/mi>≤nsij :MATH]
(10)
The “Mean” method calculates the average semantic similarity score over
all pairs of HPO terms in the two term sets, and is defined as follows.
[MATH:
SimMean(g<
/mi>1,g2
)=1m×<
mi>n∑i=1m∑j=1nsi<
/mi>j :MATH]
(11)
The “funSimMax”, “funSimAvg” and “BMA” methods are based on the maximum
value in each row and column of similarity matrix S.
The “funSimMax” and “funSimAvg” methods [[87]32] use the arithmetic
maxima and average between similarities for two directional comparisons
of the similarity matrix S.
[MATH:
SimfunSimMax(g1
,g2)
=max{1m∑i=1mmax1≤j≤nsij,1n∑j=1nmax1≤i≤msij} :MATH]
(12)
[MATH:
SimfunSimAvg(g1
,g2)
=12×(1m∑i=1mmax1≤j≤nsij+1n∑j=1nmax1≤i≤msij) :MATH]
(13)
The “BMA” method uses the best-match average strategy, which calculates
the average of all maximum similarities on each row and column of the
similarity matrix S.
[MATH:
SimBMA(g1<
/mn>,g2)
=∑i=1mmax1≤j≤nsij+∑j=1nmax1≤i≤msijm+n :MATH]
(14)
The calculation of the similarity between diseases is the same as that
between genes. The similarity between two diseases is calculated based
on the pairwise similarity of the two term sets annotating these two
diseases.
HPO-based Enrichment Analysis
HPOSim provides HPO-based enrichment analysis to investigate the
phenotypic features of gene sets or disease sets. Two enrichment
analysis methods are provided: hypergeometric test and the NOA method
[[88]27].
Given an HPO term t and a gene set with T genes, assuming that there
are R genes/diseases annotated in the whole HPO in which G
genes/diseases are annotated to t. In addition, there are O
genes/diseases in the gene set that are annotated to t. The
hypergeometric enrichment p-value for t is calculated as follows:
[MATH:
p−value=∑x=Omin(G,T)<
mrow>(Gx)
(R−GT−x)(<
mrow>RT
)<
/mrow> :MATH]
(15)
In NOA, users input a gene or disease network. For each edge in the
network, the HPO terms annotating this edge are defined as the
intersection of the two term sets annotating the two nodes of the edge.
NOA uses HPO terms annotating the edges to perform the enrichment
analysis. Two alternative strategies, “sub-net” and “whole-net”, are
applied to choose the reference set. In the “sub-net” strategy, users
are required to provide the reference set. While in the “whole-net”
strategy, the complete graph on the nodes of the input network is used
as the reference set.
Results and Discussion
HPOSim consists of two parts: (i) the similarity measures between
phenotypes (HPO terms), between human genes (Entrez IDs) and between
diseases (OMIM IDs), and (ii) HPO-based enrichment analysis (NOA and
the hypergeometric method) for gene set and disease set.
Application on gene similarity and gene set enrichment analysis
We used the aging network [[89]37] to demonstrate the application of
gene semantic similarity provided by HPOSim. The aging network was
constructed by identifying genes related to aging and adding edges
between interacting gene pairs. After removing the genes that are not
annotated in the PA sub-ontology of HPO, 102 genes and 293 interactions
were remained (see [90]S1 Dataset for detail).
First, the semantic similarity matrix of the 102 genes was constructed
using the Resnik measure and “funSimMax” combining method (see [91]S2
Dataset for detail). A hierarchical clustering was then performed using
the R package stats, and six modules were detected using the R package
dynamicTreeCut. HPO enrichment analysis (hypergeometric test) was then
performed using HPOSim. GO enrichment analysis and pathway enrichment
analysis based on KEGG (Kyoto Encyclopedia of Genes and Genomes)
pathway database [[92]38] were performed using DAVID [[93]39]. The
results are shown in [94]Table 2.
Table 2. Gene modules of the aging network.
Module Size Genes (Entrez ID) TOP 5 Enriched GO BP Terms TOP 5 Enriched
HPO Terms TOP 5 Enriched KEGG Pathways
M1 36 25, 207, 472, 581, 596, 641, 672, 675, 701, 1029, 1050, 1499,
1956, 2064, 2308, 3265, 4193, 4292, 4609, 5159, 5422, 5728, 5781, 5925,
6794, 7015, 7157, 7486, 9184, 1385, 7153, 627, 1649 regulation of
apoptosis, cell cycle process, regulation of programmed cell death,
regulation of cell death, regulation of cell cycle Neoplasm, Neoplasm
by anatomical site, Neoplasm by histology, Sarcoma, Hematological
neoplasm Pathways in cancer, Prostate cancer, Endometrial cancer,
Glioma, Bladder cancer
M2 26 545, 1387, 2010, 2033, 2068, 2073, 2074, 2260, 3479, 3480, 4000,
4036, 4792, 4803, 5979, 7020, 7314, 7341, 7415, 7507, 5830, 1950, 1161,
847, 1490, 2067 DNA metabolic process, response to UV, response to
radiation, DNA repair, nucleotide-excision repair Intrauterine growth
retardation, Aplasia/Hypoplasia of the mandible, Micrognathia,
Defective DNA repair after ultraviolet radiation damage, Abnormality of
the mandible Nucleotide excision repair, Prostate cancer, Pathways in
cancer, Melanoma, Adherens junction
M3 17 367, 2099, 2353, 2690, 2908, 3630, 3643, 3952, 3953, 5449, 5578,
6777, 7040, 8626, 8820, 2688, 5626 response to hormone stimulus,
response to endogenous stimulus, response to organic substance,
positive regulation of macromolecule metabolic process, response to
estrogen stimulus Abnormality of the anterior pituitary, Abnormality of
the pituitary gland, Abnormality of the endocrine system, Abnormality
of the hypothalamus-pituitary axis, Anterior hypopituitarism Jak-STAT
signaling pathway, Neuroactive ligand-receptor interaction,
Cytokine-cytokine receptor interaction, Aldosterone-regulated sodium
reabsorption, Pathways in cancer
M4 11 355, 2071, 3561, 3575, 4683, 4791, 5295, 5580, 6774, 6929, 5336
cell activation, B cell activation, lymphocyte activation, leukocyte
activation, immune system development Abnormality of lymphocytes,
Abnormal immunoglobulin level, Abnormality of B cell physiology,
Abnormality of B cells, Abnormality of humoral immunity Pathways in
cancer, Jak-STAT signaling pathway, Fc epsilon RI signaling pathway, Fc
gamma R-mediated phagocytosis, Neurotrophin signaling pathway
M5 9 3064, 4001, 4137, 5155, 6872, 6908, 5663, 6647, 1938 negative
regulation of neuron apoptosis, regulation of neuron apoptosis,
positive regulation of MAP kinase activity, behavior, regulation of
membrane potential Abnormality of extrapyramidal motor function,
Personality changes, Adult onset, Dysarthria, Parkinsonism Huntington’s
disease, Basal transcription factors
M6 5 348, 351, 3717, 2876, 5328 regulation of response to external
stimulus, induction of apoptosis, induction of programmed cell death,
positive regulation of apoptosis, positive regulation of programmed
cell death Long-tract signs, Abnormal bleeding, Abnormalities of the
peripheral arteries, Arterial stenosis, Cerebral inclusion bodies
N/A[95]^*
[96]Open in a new tab
* N/A indicates that there are no enriched KEGG pathway (p-value<0.05)
for this module.
Module M5 only have two enriched KEGG pathway (p-value<0.05).
Gene FOXO4 (Entrez ID: 4303) could not be grouped into a certain
module.
It can be seen that the enriched GO and HPO annotations are largely
different among these modules. For example, the enriched GO annotations
of module M2 implied that aging is associated with radiation including
ultraviolet (UV), which has been verified by previous study in skin
aging [[97]40]. While the enriched GO annotations of module M3 implied
that aging is associated with hormone stimulus, and literature mining
showed that older women require a greater parathyroid hormone stimulus
than younger women [[98]41]. The enriched HPO annotations of the module
M3 implied that aging are associated with abnormality of the pituitary,
which has been verified by Sano et al. [[99]42]. Disease enrichment
analysis based on OMIM was then performed on genes in M3 using DAVID
[[100]39] and showed that term “Pituitary hormone deficiency, combined”
was representative (p-value = 8.2E-3).
The enriched pathways of different modules are closely related to
cancer, however various among different modules. Jak-STAT signaling
pathway was found to be representative in modules M3 and M4. In a
previous study by Fulop et al. [[101]43], it was found that the
signalling of IL-2 receptors is altered in T cells and macrophages with
aging, mainly in relation to the Jak-STAT pathway.
These results above indicate that HPO-based semantic similarity can
provide a different aspect in disease-related studies other than GO.
NOA and hypergeometric gene set enrichment analysis were then performed
on the aging network. The “whole-net” strategy [[102]27] was used to
choose the reference set in NOA. The top 10 enriched HPO terms in the
two enrichment methods are shown in [103]Table 3. It can be seen that
both enrichment methods identify neoplasm-related HPO terms as the top
hits. However, these two methods give different terms and different
ranks of terms. When dealing with gene/disease sets from biological
networks, users are suggested to use the NOA method. If the gene sets
are not from network data, users can use either hypergeometric or NOA
enrichment method.
Table 3. Top 10 enriched HPO terms by the NOA method and hypergeometric
enrichment.
Rank NOA(whole-net) Hypergeometric Enrichment
HPO ID Description q-value HPO ID Description q-value
1 HP:0011793 Neoplasm by anatomical site <1E-14 HP:0002664 Neoplasm
<1E-14
2 HP:0002664 Neoplasm 4.8E-14 HP:0011792 Neoplasm by histology 1.2E-13
3 HP:0007379 Neoplasm of the genitourinary tract 1.6E-5 HP:0011793
Neoplasm by anatomical site 1.1E-12
4 HP:0001156 Brachydactyly syndrome 4E-5 HP:0100242 Sarcoma 3.1E-10
5 HP:0010787 Genital neoplasm 5.1E-5 HP:0004377 Hematological neoplasm
6.9E-8
6 HP:0008069 Neoplasm of the skin 5.7E-4 HP:0000008 Abnormality of
female internal genitalia 7.7E-7
7 HP:0001909 Leukemia 3.6E-3 HP:0004375 Neoplasm of the nervous system
7.7E-7
8 HP:0000006 Autosomal dominant inheritance 4.2E-3 HP:0002665 Lymphoma
7.7E-7
9 HP:0000008 Abnormality of female internal genitalia 4.2E-3 HP:0000812
Abnormal internal genitalia 8.2E-7
10 HP:0000812 Abnormal internal genitalia 4.4E-3 HP:0010460 Abnormality
of the female genitalia 8.6E-7
[104]Open in a new tab
Both enrichment methods identify HPO terms related to neoplasm as the
top hits. However, these two methods give different enriched terms and
different ranks of terms.
Application on disease similarity and disease set enrichment analysis
HPOSim can also be used to investigate the phenotypic relationships
between diseases. First, 115 cancer related entries were obtained by
searching the OMIM database [[105]6] using “cancer” or “carcinoma” as
the key word. After removing the diseases that are not annotated in the
PA sub-ontology of HPO and all the genes, 55 disease entries were
remained (see [106]S3 Dataset for detail).
The semantic similarity matrix of the 55 disease entries was
constructed using the Resnik measure and “funSimMax” combining method
(see [107]S4 Dataset for detail). A hierarchical clustering was then
performed and four modules were detected using the same routine as used
in the previous case study. HPO enrichment analysis (hypergeometric
test) was also performed using HPOSim. The results are shown in
[108]Table 4.
Table 4. Disease modules of the cancer entries in OMIM.
Module Size Diseases (OMIM ID) TOP 5 Enriched HPO Terms
M1 22 OMIM:246470, OMIM:114550, OMIM:120435, OMIM:133239, OMIM:137215,
OMIM:148500, OMIM:260350, OMIM:276300, OMIM:601228, OMIM:606719,
OMIM:608615, OMIM:609310, OMIM:612229, OMIM:612591, OMIM:613244,
OMIM:613347, OMIM:613659, OMIM:614331, OMIM:614337, OMIM:614350,
OMIM:614385, OMIM:615083 Neoplasm by anatomical site, Neoplasm,
Abnormality of the large intestine, Neoplasm of the large intestine,
Neoplasm of the gastrointestinal tract
M2 13 OMIM:109400, OMIM:109800, OMIM:114500, OMIM:144700, OMIM:150800,
OMIM:176807, OMIM:273300, OMIM:300854, OMIM:312300, OMIM:601518,
OMIM:603688, OMIM:605074, OMIM:608089 Neoplasm of the genitourinary
tract, Neoplasm, Neoplasm by anatomical site, Genital neoplasm, Urinary
tract neoplasm
M3 12 OMIM:603641, OMIM:114480, OMIM:158320, OMIM:167000, OMIM:211980,
OMIM:260500, OMIM:275355, OMIM:603956, OMIM:604370, OMIM:612555,
OMIM:614456, OMIM:614564 Breast carcinoma, Neoplasm, Neoplasm of the
breast, Neoplasm by anatomical site, Abnormality of the breast
M4 6 OMIM:155240, OMIM:171400, OMIM:188470, OMIM:188550, OMIM:202300,
OMIM:608266 Neoplasm of the endocrine system, Thyroid carcinoma,
Neoplasm of the thyroid gland, Abnormality of thyroid morphology,
Neoplasm by anatomical site
[109]Open in a new tab
OMIM:191600 (URETER, CANCER OF) and OMIM:610644 (PALMOPLANTAR
HYPERKERATOSIS WITH SQUAMOUS CELL CARCINOMA OF SKIN AND 46,XX SEX
REVERSAL) could not be grouped into a certain module.
The results showed that these four disease modules had different
phenotypic features. For example, module M3 included several types of
women-only cancer, including breast cancer (OMIM:114480),
breast-ovarian cancer (OMIM:604370, OMIM:612555), ovarian cancer
(OMIM:167000) and cervical cancer(OMIM:603956). And lung cancer
(OMIM:211980) in M3 was the second most commonly diagnosed types of
cancer among women in 2013[[110]44].
The result above indicated that HPO-based semantic similarity had
potential ability to play an important role in disease classification
and other disease-related studies.
Conclusions
HPOSim is an open source R package that contains seven semantic
similarity measures and two enrichment analysis based on HPO data.
Also, it provides useful functions for disease-related research and can
be integrated with other R packages. In future work, we will integrate
more similarity measures and other functions, such as visualization of
the HPO data.
Supporting Information
S1 Dataset. Aging network after removing the genes that are not
annotated in PA sub-ontology of HPO.
(CSV)
[111]Click here for additional data file.^ (3.1KB, csv)
S2 Dataset. Semantic similarity matrix of the 102 genes in the aging
network.
(CSV)
[112]Click here for additional data file.^ (112KB, csv)
S3 Dataset. Cancer entries in OMIM.
(XLSX)
[113]Click here for additional data file.^ (21.8KB, xlsx)
S4 Dataset. Semantic similarity matrix of the 55 cancer entries.
(CSV)
[114]Click here for additional data file.^ (33.7KB, csv)
Acknowledgments