Visualization of Human Disorders and Genes Network
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1 Visualization of Human Disorders and Genes Network Bryan (Dung) Ta Dept. of Computer Science University of Maryland, College Park I. INTRODUCTION Goh et al. [4] has provided an useful analysis of the human disease network. The work used Gephi [1] to visualize the network and discussed insight information about the relations between human disease and human genes. The main purpose of this assigment is to use another visualization tool called NodeXL to reveal the insights obtained directly from the data. Since the topic is very field-specific and requires a good domain knowledge about genes and diseases, in order to explain reasons behind the insights, this work cited some expert explainations from [4]. II. DATA DESCRIPTION The raw data for this work is collected from [2] [4]. It contains three major data sets described as follows: - The Disorder dataset with 867 nodes and 1527 edges. Nodes represent disorders (i.e. diseases). Two disorders are connected to each other if they share at least one gene in which mutations are associated with both disorders. - The Gene dataset with 1378 nodes and 7491 edges. Nodes represent disease genes. Two genes are connected if they are associated with the same disorder. - The Disorder-Gene bipartite dataset with 3290 nodes and 2673 edges. A disorder and a gene are then connected by a link if mutations in that gene are implicated in that disorder. Contribution of this work includes visualizing the data to highlight interesting information about the connections between human disorders and genes some of which have been discovered by [4], and converting the raw data into the excel format that works with NodeXL (e.g. manually creating 22 groups, removing/modifying 2000 same IDs in the bipartite dataset etc) which took a considerable amount of time. I also made the processed data available [3] to NodeXL community. III. INSIGHT 1: LIFE WOULD BE EASY IF EACH HUMAN DISORDER DID NOT RELATE TO EACH OTHER. Sad as it might be, in the reality, many disorders closely relate to each other, which makes it hard to explain the cause of diseases and complicates treatment processes. From the figure 1, we can see that each disorder has at least one link to the other disorder, and most of them (516 out of 867) altogether form a giant component. This suggests that most diseases share the same genetic origins [4]. Not only individual disorders but classes of disorders also exhibit strong interconnections both among classes and within classes themselves. In figure 5, the concentration of grey links demonstrates significant connections between 22 disorder classes. 1
2 Fig. 1. Human disorder (disease) network. Each node corresponds to a distinct disorder, and is shaped and colored based on the disorder class to which it belongs (the names of 22 disorder classes shown on Figure 5). There is a link between 2 node if they share at least one gene. The size of each node is proportional to its number of links. Only the names of a small number of disorders are shown as examples in order to reduce the graph density. Figure 2 and Figure 3 present interesting observations on connections between disorders. In figure 2, the degree (i.e. the number of distinct disorders that a particular disorder connects to) distribution of the network indicates that the majority of the disorders are linked to only a few of the other disorders, whereas some disorders are linked to many other disorders. For example, colon cancer are linked to 50 other disorders and breast cancer are linked to 30 disorders. Figure 3, which diagrams disorders based on their degree and number of related genes, highlights a fascinating fact that colon cancer is the disorder that connects to a significant number of disorders (50 other disorders) whereas deafness connects to the highest number of genes yet to a very small number of disorders. This suggests that the degree of disorders connection with genes does not determine the degree of their connections to other diseases. This insight information has also been discovered by [4] which used gephi. In this assigment, I would like to use a different tool (NodeXL) to see if we can find out the same insight. As the result shows, we can get the same useful information using NodeXL. Fig. 2. Histogram of degree of the network. 2
3 Fig. 3. Disorder ranking based on their degree and number of related genes. IV. INSIGHT 2: WATCH OUT FOR CANCER - THE MOST CONNECTED DISORDER CLASS In addition to interconnections among different disorders and among different disorder classes in general, there are different trends of connections within a certain disorder class. In Figure 4, whereas the large cancer cluster (blue solid diamonds) is tightly interconnected, metabolic disorders (red solid triangles) only form scattered small connected components. Note that all the other clusters, which are not analyzed, are colored in grey to make cancer and metabolic clusters more visible. This interesting insight is also highlighted in cancer cluster and metabolic cluster shown Figure 5 when I clustered disorders based on their disorder classes. The reason for cancer s prominence among the most connected disorder classes comes from the fact that many cancer subtypes share common tumor genes such as TP53 and PTEN which make the connection tighter [4]. Fig. 4. Cancer cluster (blue solid diamonds) is better connected than metabolic cluster (red solid triangles). Other clusters are colored in grey to facilitate better contrast. 3
4 Fig. 5. Twenty-two disorder classes. To quantify the difference in degree of cluster interconnection, for each disorder class, based on the graph metrics for the groups, I measured the following metrics: - The ratio of single vertices to vertices (the lower the ratio is, the better the cluster connects) - The ratio of maximum vertices in a connected component to the total vertices (the higher the ratio is, the better the cluster connects) - The ratio of maximum edges in a connected component to total Edges (the higher the ratio is, the better the cluster connects) - Graph density (The higher the density value is, the better the cluster connects) I noticed that cancer and neurological disorders show better values for those metrics and also represent the most connected disease classes, in contrast with metabolic, and multiple disorders which have worse values and consequently are the least connected. V. INSIGHT 3: TOP 3 MOST ACTIVE GENES. The visualization in Figure 6 is a bipartite graph between disorders and genes. From this graph, I use NodeXL s filter function to get rid of the genes which have low degree. The Figure 7 presents the result of the above filtering process and shows the top 3 most active genes in terms of the number of related disorders including PAX6 which relates to 11 disorders, PTEN which relates to 10 disorders, FGFR2 and TP53, each of which relates to 9 disorders. Interestingly, most of these genes relate to most of the diseases in the cancer class. Note that in order to show the most active genes in terms of the number of related disorders, we can employ a similar visualization as done in Figure 3. In order to test various features of the tool, I employed filter feature of NodeXL. 4
5 Fig. 6. Bipartite graph between genes (green solid rectangle) and diseases (red solid disk). A link is placed between a disorder and a disease gene if mutations in that gene lead to the specific disorder.the size of each node is proportional to its number of links. Fig. 7. The top 3 active genes (blue solid squares). VI. TOOL CRITIQUE Overall, NodeXL is a very useful tool for network visualization. It provides users with many well-developed features to explore data sets. I am very impressed with NodeXL s ability of displaying insights almost instantly or with several quick processing steps after the data is loaded. However, there is still room for improvements which are suggested as folllows. - When visualizing data with X, Y axes (i.e. Graph Elements/Axes), the axes disappear when the visualization is saved to an image file. This is a significant weakness as these axes contain important information such as axes labels, axes unit and values which are key to interpret diagrams. The image below is the corresponding output to the visualization in Figure 3 yet without axes labels. Undeniably, users have much more difficulty in extracting information from this image due to the lack of axes labels. 5
6 Fig. 8. Diagram without axes lables. - Legends are not easy to be edited/created by users. In some contexts, the tool does not seem to have a supporting feature to create legends for group attributes. Particularly in this assignment, although I want to create legends to explain the 22 groups that are symbolized in different colors and shapes, I found no feature supporting this task. - There is no undo feature which makes it impossible to reverse any changes to the visualization. REFERENCES [1] [2] Publications/ PNAS-HumanDisease/Suppl/index.htm. [3] f12/images/f/fd/bryanta NodeXL Data.zip. [4] K.I. Goh, M.E. Cusick, D. Valle, B. Childs, M. Vidal, and A.L. Barabási. The human disease network. Proceedings of the National Academy of Sciences, 104(21): ,
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