#  GeneVis
An interactive visualization tool for combining cross-discipline datasets
within genetics

Casper van Leeuwen

Visualization group, SURFsara, Amsterdam, the Netherlands.

<EMAIL_ADDRESS>

###### Abstract

Summary: GeneVis is a web-based tool to visualize complementary data sets of
different disciplines within the field of genetics. It overlays gene-cluster
information, gene-interaction data and gene-disease association data by means
of web-based interactive graph visualizations. This allows an intuitive and
quick assessment of possible relations between the different datasets. By
starting from a high-level graph abstraction based on gene clusters, which can
be selected for detailed inspection at the gene-interaction level in a
separate window, GeneVis circumvents the common visual clutter problem when
using gene datasets with a high number of gene entries.
Availability: The source of GeneVis is available at
https://gitlab.com/caspervanleeuwen/genevis/
Contact<EMAIL_ADDRESS>

##  1 Introduction

The continuous technological advancement in the fields of genetics, genomics
and bioinformatics has led to large data sets on gene clusters, gene-gene
interactions and the association of genes with diseases. One of the challenges
is to use this wealth of information by combining the data from different
sources and experiments to get a better (intuitive) understanding of the
biological processes underpinning disease. GeneVis is a web-based
visualization tool to explore high-level gene clusters (e.g. Kyoto
Encyclopedia of Genes and Genomes111http://www.kegg.jp/, KEGG, pathway
functions), gene-to-gene interactions (e.g. Human Integrated Protein-Protein
Interaction Reference222http://cbdm-01.zdv.uni-mainz.de/ mschaefer/hippie/,
HIPPIE) and gene-disease associations (e.g. Genome-Wide Association
Studies333http://www.gwascentral.org/, GWAS). Data from these sources are
generated in a domain-specific way (i.e in their own format using different
methods), which makes it difficult to directly relate data from the different
sources to each other. GeneVis was designed to overcome this problem by
generating insightful visualizations to create a higher-level understanding of
multi-domain research results.

GeneVis uses force-directed graphs as a basis to visualize relations between
gene clusters, between clusters and individual genes and between individual
genes (figure 2). Force-directed graphs are a general way to visualize
relational data (Eades et al.,, 1984)(Tollis et al.,, 1999)(Fruchterman and
Reingold,, 1991), although their effectiveness can succumb to visual clutter
whenever the number of data items gets large. To reduce this visual clutter
the GeneVis visualization tool introduces a higher-level view in the form of a
cluster graph, called the Cluster view, where sets of genes are used as graph
nodes instead of individual genes (left panel, figure 2). This reduces the
number of nodes in the graph and reduces the number of visual items to inspect
and comprehend. This higher-level cluster graph gives the user a concise but
insightful view of the whole gene dataset, using node deformations and color
maps to display the average gene association to the cluster and cluster size
and the average association with a disease, respectively (left panel, figure
2). GeneVis also gives the user the ability to ”zoom” into a cluster of
interest and reveal the underlying gene subset and gene interactions. This
level is called the Gene View (right panel, figure 2). Both in Cluster and
Gene View gene sets can be explored by zooming, selecting and highlighting
genes or clusters. Finally, a visual-scheme (or color map) over both views
called the ”Disease Mode” allows quick visual inspection of the association of
genes or gene clusters with a specific disease or trait, using data from gene-
disease association datasets (GWAS data by default).

Figure 1: Cluster node representation. This shows that the minor radius
(radius inner circle) is defined by the number of genes and the major radius
is defined by the minor radius multiplied by the average gene association
score.

##  2 Method

###  2.1 Datasets

GeneVis allows a simultaneous visualization of three complementary types of
datasets: a gene cluster dataset, a gene-gene interaction dataset and a gene-
disease association dataset. All datasets must be provided in either tab or
comma-separated tabular file formats. For example file formats see Appendix .

####  2.1.1 Gene cluster dataset

To visualize gene clusters with GeneVis, the gene cluster dataset should
contain a list of gene names with the associated gene cluster. The cluster
data can either be based on a hard (0 or 1) or soft probability clustering
(between 0 and 1), which both will be handled appropriately by GeneVis. Each
cluster must contain a subset of genes, where each gene has an association
value ${a_{c}}$, where $c$ is the cluster to which the gene is associated.
This association value ${a_{c}}$ can either be a given $p$-value from a
dataset (for a soft-clustered dataset) or an automatically computed fraction
${a_{c}=\frac{1}{N^{c}_{g}}}$(for a hard-clustered dataset), determined by the
number of clusters ${N^{c}_{g}}$ a specific gene is associated with. Each row
within the dataset, except the first header row, represents a gene. The
columns represent the gene Entrez ID (geneEntrezId), the gene name (geneName)
and all the gene clusters. All the columns must be named accordingly in a
header row. The association values between genes and clusters are stored in
the cells of the tabular structure (see table 1).

geneEntrezId | geneName | GLYCOLYSIS … | CITRATE_CYCLE … | PENTOSE … | …
---|---|---|---|---|---
873 | CBR1 | 0.2 | 0.4 | 0.9 | …
2026 | ENO2 | 0.6 | 0.6 | 0.2 | …
2665 | GDI2 | 0.1 | 0.2 | 0.1 | …
… | … | … | … | … | …

Table 1: Gene cluster dataset format example

####  2.1.2 Gene-gene interaction dataset

GeneVis can visualize any form of gene-gene interaction that can be scored as
an interaction strength between 0 and 1. To visualize gene-gene interactions,
GeneVis requires a tabular dataset of genes containing 3 columns, the source
gene Entrez ID (SourceGeneId), the target gene Entrez ID (TargetGeneId) and
the interaction score (score)(see table 2). All the columns must be named
accordingly in a header row. Each row entry contains the interaction strength
(score, varying between 0-1) between 2 individual genes (source and target),
which will affect the representation in Gene View, see section 2.3. Different
types of gene-gene interactions can be visualized in this way, for instance
protein-protein interactions discovered with co-immunoprecipitation assays or
yeast-2-hybrid (Stelzl et al.,, 2005)(Li et al.,, 2010).

SourceGeneId | TargetGeneId | score
---|---|---
216 | 216 | 0.75
3679 | 1134 | 0.73
55607 | 71 | 0.65
… | … | …
Table 2: Gene-gene interaction dataset format example

####  2.1.3 Gene-disease association dataset

The gene-disease association dataset is a tabular dataset containing diseases
and its associated genes and scores. Each row in this dataset is a study about
a certain disease and it’s associated gene(s) and that studies $p$-value (see
table 3). This dataset must contain at least the following columns: gene names
(Genes), disease/trait (Disease/Trait) and the p-value of the association
($p$-value). This dataset will be visually represented in the Disease Mode,
see section 2.4.

Disease/Trait | Genes | p-Value | …
---|---|---|---
depressive disorder | CBX4 | 0.0000002 | …
depressive disorder | PDZD2 | 0.0000003 | …
depressive disorder | CTC-497E21.5 | 0.0000007 | …
… | … | … | …

Table 3: Gene-disease association dataset format example

###  2.2 Cluster View

GeneVis offers two levels of visualization. In Cluster View (left panel,
figure 2) all gene clusters present in the gene cluster dataset and their
overlap are shown. The layout of the graph of clusters is determined by means
of a force-directed graph layout algorithm (Tollis et al.,, 1999). Each gene
cluster is represented with an ellipse-shaped node, where the ellipse size
defined by the inner circle radius represents the number of genes present in
the cluster and the length of the major axis of the ellipse is inverse
proportionally scaled to represent the average of gene association scores
within the cluster multiplied by the minor radius(figure 1). So spherical
clusters have a higher average gene association score than more elliptical
clusters and proportionally smaller nodes have less associated genes than
proportionally bigger nodes. A random color is assigned to each node and while
the Disease Mode is active they are colored according to their disease
association, see section 2.4 and figure 2 and figure 3.

An edge between two cluster nodes describes the gene overlap between the
clusters. The size and the color intensity of the edge both scale with the
amount of overlap between the clusters, meaning the wider and brighter the
edge, the more overlap there is between the connected clusters, in terms of
numbers of genes.

Clusters can be inspected by highlighting the cluster, which will reveal the
underlying genes associated with the cluster in GeneView.

###  2.3 Gene View

The Gene View (right panel, figure 2) visualizes the individual gene
interactions within a selected cluster. Each gene is represented as a node and
each edge is an interaction between the genes. The gene nodes are represented
as pie charts, where the size represents that gene’s association with the
selected cluster and the pie pieces represent all the clusters in which that
gene is present, proportional by cluster membership. The edge width and color
represents the interaction score between genes. The Gene View has an extensive
interaction model for the user to explore the gene datasets. Individual genes
can be highlighted and queried by hovering and clicking, respectively. There
are three methods to highlight the gene interactions:

1) ”Level of connectedness highlighting” which highlights all the gene
interactions connected to the highlighted gene through different levels of
connectivity, e.g. a level of connectedness of 2 reveals all the connected
genes to the highlighted gene as level 1 and all the genes connected to the
genes in level 1 as level 2.

2) ”Link threshold highlighting” progresses through all the connected gene
interactions and highlights those interactions whose interaction score is
above the given threshold.

3) ”Top-n link highlighting” highlights the top n highest interaction links
and its genes starting from the highlighted gene.

###  2.4 Disease Mode

The Disease Mode will change the color and opacity representation of the nodes
and edges of both the Cluster View and the Gene View based on a selected
disease from the gene-disease association dataset.

The Cluster View cluster nodes and edges that do not contain any genes
associated to the selected disease will have a lower opacity in addition to
their color and size. This reveals a disease sub-cluster graph network, which
gives a sparse overview on the disease and cluster relations. Each cluster
node will be colored according to that clusters enrichment score, where highly
significant scores (${p<0.05}$) appear red, weakly significant scores
(${0.05<p<0.1}$) appear orange and not significant scores (${p>0.1}$) appear
white. This enrichment score is a modified one-tailed Fisher Exact $p$-value
called the EASE score.

The Gene View gene nodes are color-coded according to the $p$-values
associated to the study behind the selected disease obtained from the gene-
disease association dataset.

##  3 Application

In figure 2 and 3 an example of the tool is given for the gene cluster dataset
KEGG, gene-gene association dataset HIPPIE and gene-disease association
dataset GWAS. Figure 2 shows the UI of GeneVis with on the left side panel the
clusters and the right side panel the genes associated to the Cytokine
receptor cluster. Both are colored coded by their association to the Crohn’s
disease(where red is ${p<0.05}$, orange is ${0.05<p<0.1}$ and white is
${p>0.1}$). Figure 3 shows on the first row an in depth comparison of the
cluster view color mapped with 3 different diseases, Crohn’s disease, Migraine
without Aura and Prostate cancer. The following 3 rows show an in depth
comparison of the gene view of 3 different clusters (Cytokine receptor
interaction, pathways in cancer and CPI3k-Akt signaling pathway) color mapped
by the same diseases. In this example Genevis shows which of the clusters are
associated to the currently selected disease. These gene-cluster and disease
association can than be further explored on a gene-level in the Gene View. In
turn the Gene View shows which genes, which have not been associated to the
disease yet, are connected to the disease associated genes. This way the user
can exploratively find potential new studies when it comes for example gene-
disease associations.

##  4 Implementation

GeneVis is a web-based visualization tool written in HTML, CSS, JavaScript,
jQuery444http://jquery.com/ and the framework D3.js555http://d3js.org/.
Roughly ~80% of the application uses the framework D3.js, which is a versatile
data-driven HTML DOM element manipulation framework. D3.js made it possible to
provide user’s with full interactive control over their gene cluster data with
interactive frame rates.

Figure 2: GeneVis. This image is the full interface of the GeneVis. The left
panel is the Cluster View and the right panel is the Gene View. The bottom of
the interface is the interaction bar, which contains all the data set loading
and graph interaction buttons.

Figure 3: The Cluster View (first row) and the Gene View (last 3 rows). The
first row shows a comparison of 3 Cluster Views representations for 3
different diseases: Crohn’s Disease, Migraine without Aura and Prostate
Cancer. In the Cluster View row the three different pathways are annotated to
show the comparison between these pathways relative to the selected disease.
For example, the Cytokine receptor interaction pathway is an active pathway in
inflammatory diseases and as Crohn’s disease is an inflammatory bowel disease
it has a significant enrichment score and is color coded accordingly in red.
The 3 Gene View rows show 9 comparative GeneView Disease Mode representations
between the 3 different diseases and 3 different pathways. Just like the
Cluster View a clear difference can be seen between the diseases in the
different pathways.

## References

  * Eades et al., (1984) Eades, P. (1984) A heuristic for graph drawing, Congressus Numerantium, 42, 142-160.
  * Tollis et al., (1999) Tollis, I.G., Battista, G.D., Eades, P., and Tamassia, R. (1999) Graph drawing - Algorithms for the visualization of graphs, Prentice Hall, Upper Saddle River, NJ.
  * Fruchterman and Reingold, (1991) Thomas M. J. Fruchterman and Edward M. Reingold (1991) Graph Drawing by Force-directed Placement, Software-Practice and Experience, 21, 1129-1164.
  * Hosack et al., (2003) Hosack, D. A. , Dennis, Jr. G., Sherman, B. T., Clifford Lane, H., Lempicki, R. A. (2003) Identifying biological themes within lists of genes with EASE Genome Biol, Genome Biol., 4:R70.
  * Stelzl et al., (2005) Stelzl, U., Worm, U., Lalowski, M., Haenig, C., Brembeck, F.H., Goehler, H., Stroedicke, M., Zenkner, M., Schoenherr, A., Koeppen, S., Timm, J., Mintzlaff, S., Abraham, C., Bock, N., Kietzmann, S., Goedde, A., Toksöz, E., Droege, A., Krobitsch, S., Korn, B., Birchmeier, W., Lehrach, H., Wanker, E.E. (2005) A human protein-protein interaction network: a resource for annotating the proteome, Cell, 122(6), 957-68.
  * Li et al., (2010) Li, K.W., Klemmer, P., Smit, A.B. (2010) Interaction proteomics of synapse protein complexes, Anal Bioanal Chem., 397(8), 3195-202.