Download Computer Laboratory University of Cam bridge CytoCom: a

Computer Laboratory
University of Cambridge
Cambridge, UK, CB3 0FD
User Manual
CytoCom: a Cytoscape plugin to visualise, query
and analyse disease-disease dynamic networks
CytoCom, Version 1.0.0
Haoming Xu, Mohammad Ali Moni and Pietro Liò
October 2014
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Computer Laboratory
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User Manual
CytoCom: a Cytoscape plugin to visualise,
query and analyse disease-disease dynamic
networks
CytoCom, Version 1.0.0
Haoming Xu, Mohammad Ali Moni and Pietro Liò
Computer Laboratory
University of Cambridge
Cambridge
UK, CB3 0FD
October 2014
ii
Table of Contents
System Requirements ....................................................................................... iii
Documentation ............................................................................................... iv
Data set .................................................................................................. iv
Control Panel ........................................................................................... iv
Parameter setting ..................................................................................... vii
Network construction ................................................................................ vii
Network Visualisation............................................................................... viii
Customise the network ............................................................................. viii
References...................................................................................................... xi
iii
System Requirements
In order to use CytoCom, your computer must be equipped with the following software package:
ˆ
Java 1.6+ is needed, Java 1.7 is recommended.
ˆ
Cytoscape 3.1.0 + installed
To download CytoCom, please visit: http://www.cl.cam.ac.uk/~mam211/.
In Cytoscape 3.1.0 or later version, the App Manager allows users to quickly
install and uninstall the apps. After downloading CytoCom, please go to
Apps→ app Manager and then click the "Install from File" button at the
bottom of the "Install Apps" tab. Select the downloaded CytoCom.jar to
install. After installing CytoCom, it could be found under the "Control
Panel".
iv
Documentation
Understanding the comorbidity of human diseases is one of the most challenging issues in bioinformatics today. CytoCom is a plug-in for Cytoscape
to visualise, query and analyse disease-disease networks.
Data set
We collected statistically signicant pairwise comorbidity associations reconstructed from over 32 million medical records in the US Medicare claims
database recorded in the ICD-9 format (http://www.icd9data.com),
which are frequently used for epidemiological and demographic studies and
collected from Hidalgo et al. (2009). We used MedPAR records from 1990
to 1993, where the dates and reasons for all hospitalisations were reported
in ICD-9-CM format and it contains the diagnoses of 13,039,018 elderly patients. In total, the ICD-9 classication consists of 657 dierent categories
at the 3 digit level Hidalgo et al. (2009). Yet, the data set is large enough to
predicate race and gender specic comorbidity patterns.
Control Panel
After launching Cytoscape, CytoCom will appear in the control panel and
will present itself as shown in Figure 2.
In order to build a disease-disease association network, we need to load the
data according to the following steps:
1. Gender selection. Select the "Male", "Female" or "Both" options from
the database.
2. Race selection. Select the "White", "Black" or "Both" options from the
database.
3. Click the "Load" button, the CytoCom will be connected to the inner
database from which the data will be loaded.
After loading the data, we can explore disease-disease association (comorbidity) network by means of the following steps:
v
Figure 1. CytoCom control panel
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1. Enter the phi values in the text elds. It could be either integer or decimal number. Hence, values greater than input value will be selected to
explore the network.
2. Enter the relative risk values in the text elds.It could be either integer
or decimal number. Hence, values greater than input value will be selected to explore the network.
3. Click the "Apply" button. The CytoCom will then construct the diseasedisease network diagram. The name of the network starts with "Comorbidity network" + "ICD-codes" + "Phi" + "RR". See Figure 2
Figure 2. The name of the network is "Comorbidity network 340 0.01 0.6"
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In order to explore more diseases that are associated with the disease nodes
as seed input in an existing network, users need to search the network by
double-clicking the selected nodes within it. A search is then performed
based on these nodes using the pre-specied Phi and RR values and the
additional comorbid diseases are then added to the existing network. This
function works with one or more selected nodes and supports the creation of
dynamic network diagrams interactively with CytoCom. Figure 4a shows the
network generated with CytoCom using disease 340 and Figure 4b shows the
extended disease disease association network starting from the input disease
code 350.
Parameter setting
For a pair of diseases i and j , we used two statistical measures to quantify
the relationship between two diseases: Relative Risk (RRij ) and φ-correlation
(φij ), which are calculated based on the Hidalgo et al. (2009). The correlation of RRij = 1 implies no comorbidity, RRij > 1 implies positive comorbidity, and 0 < RRij < 1 implies negative comorbidity. Similarly,
φij = 0 implies no co-morbidity, 0 < φij < 1 implies positive comorbidity and −1 < φij < 0 implies negative comorbidity. The two comorbidity
measures are not completely independent of each other, and both measures
have their intrinsic biases Hidalgo et al. (2009). They increase with the number of patients aected by both diseases. For example, RR overestimates
relationships involving among rare diseases and underestimates the comorbidity between highly prevalent illnesses, whereas φ accurately discriminates
comorbidities between pairs of diseases of similar prevalence but underestimates the comorbidity between rare and common diseases. Hidalgo et al.
(2009) suggested that two diseases are strongly associated if Rij > 20 and
φij > 0.06. Therefore, user may consider these values for relative risk and φ
to estimate the comorbity among diseases. However, user may observer the
disease associations by putting their desired parameters values.
Network construction
The comorbidity disease network is constructed using the two comorbidity
measures (relative risk and phi-correlation), and using the patient medical
records. All informations that are provided as a data source le is used for
the identication and calculation of the comorbidity association between diseases according to the parameter setting by the users. The table panel in
Figure 3 provides the representation of the comorbidity output network information. Table contains 8 columns. disease 1 and disease 2 headed columns
indicate ICD-9 code of disease 1 and disease 2 respectively. Column name 1
viii
and name 2 represent names of disease 1 and disease 2. Column prevalence
1 and prevalence 2 mean the prevalence of diseases. Both of the prevalence
values are the absolute number of aected patients from the specic population. Columns rr and phi are two statistical measures. CytoCom built
disease comorbidity network based on these information.
Figure 3. Cytoscape screenshot of CytoCom
Network Visualisation
Each node in the network represents a unique disease. Two diseases are connected if there is an association between them. We colour each node according to the category of diseases based on the rst 3 digits of the given ICD9
codes. The node size increases with the increasing the disease prevalence.
More common diseases are represented as larger nodes as shown in Figure
4a. In addition, the user can retrieve a display of the legend by clicking the
"Show legend" button. It opens the Legend window, which provides colour
information for each node, as shown in Figure 5. All nodes can be labelled
with either the ICD-9-CM code or the disease name and the "Show names"
or "Show codes" tabs can be clicked alternately to switch between labelling
the nodes by code or by name.
Customise the network
Cytoscape provides several layout algorithms for organising its network visualisation system. For example, by selecting "Layouts" in the Cytoscape
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Figure 4. Panels (a) and (b) show the disease-disease network. Panel (a) shows the
network generated with CytoCom using disease 340 and Panel (b) shows the diseases
associated with 344 in the existing network that is expended.
Figure 5. Legend window
x
menu, it is possible to apply a specic layout algorithm to generate a particular view according to the user's preferred choice. In addition, the user can
change the size of the existing network nodes and the width of the existing
network edges by using the Style properties of the Cytoscape .
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References
Hidalgo, C. A., Blumm, N., Barabási, A.-L., and Christakis, N. A. (2009). A dynamic network approach for the study of human phenotypes. PLoS computational biology,
5(4), e1000353.