MGV: A Generic Graph Viewer for Comparative Omics
Stephan Symons∗and Kay Nieselt
Center for Bioinformatics T¨ ubingen, Faculty of Science, University of T¨ ubingen, Sand 14, 72076
T¨ ubingen, Germany
metabolomics methods have revolutionized our knowledge of
biological systems. To gain knowledge from comparative omics
studies,strong data integration and visualization features are
required. Knowledge gained from these studies is often available in
the form of graphs, and their visualization is especially useful in a
wide range of systems biology topics, including pathway analysis,
interaction networks, or gene models. Especially, it is necessary
to compare biological models with measured data. This allows the
identification of new models and new insights into existing ones.
Results: We present MGV, a versatile generic graph viewer for
multiomics data. MGV is integrated into Mayday (Battke et al.,
2010). It extends Mayday’s visual analytics capabilites by integrating
a wide range of biological models, high throughput data and meta
information to display enriched graphs that combine data and models.
A wide range of tools is available for visualization of nodes, data-
aware graph layout as well as automatic and manual aggregation
and refinement of the data. We show the usefulness of MGV applied
to several problems, including differential expression of alternative
transcripts, transcription factor interaction, cross study clustering
comparison and integration of transcriptomics and metabolomics data
for pathway analysis.
Availability: MGV is open source software implemented in Java and
freely available as a part of Mayday at
High throughput transcriptomics,proteomics and
The current focus of life science research is to achieve a systems
based view of processes, organisms and ecosystems. To this
end, biological activity is studied using a variety of tools: Gene
expression on transcription level is measured using microarrays
and next-generation sequencing. Furthermore, gas chromatography
and mass spectrometry methods are applied to measure protein and
metabolite concentrations. This leads to large and complex datasets
containing potentially tens of thousands of measured species from
hundreds of experiments. This data is then used to build a
comprehensive view of biological processes, with exhaustive formal
models of the systems as one of the ultimate aims. Analysis and
∗to whom correspondence should be addressed
interpretation of such data call for visualization at every step to be
successful. Visualization tools like box plots and heat maps support
the choice of methods for quality control and normalization, as
well as during statistical analysis. The identification of relationships
in the data and hypothesis generation often require graph-based
visualizations. This includes biological pathways, regulatory and
interaction networks, as well as gene models.
The full extent of systems biology data is only utilizable if
analysis tools can keep up with the data. Current datasets require
classical visualization tools, a genome-based view as well as strong
network visualization features. Especially for the latter, smart
layout and data analysis tools as well as interactivity is necessary
and extensibility is important to cope with new data formats and
analysis methods. Network visualization is most useful if it is
combined with measured data, as this allows to compare predictions
with actual results. Therefore, extensible tools for visualizing
and analyzing enriched networks are desirable. Additionally, the
integrative bioinformatics paradigm calls for seamless integration
and processing of data from a multitude of sources.
Here, we introduce a generic, integrative graph viewer called
MGV (short for Mayday Graph Viewer). MGV is based on the
versatile Mayday platform (Battke et al., 2010) and offers a
comprehensive set of tools for analysis and visualization of graphs.
As an extensible, feature rich (Koschmieder et al., 2011) tool for
multiple omics data analysis, Mayday is flexible about the data
analyzed. A recently published major extension to Mayday, called
Mayday SeaSight (Battke and Nieselt, 2011) allows working with
high-throughput sequencing data. MGV is designed to work on
any kind of measured data, and it can import different graph and
pathway data formats. A large variety of different configurable
tools are available for displaying measured data at nodes, along
with layout methods that use data properties of the nodes. Several
biological and all-purpose graph file formats can be imported. MGV
also allows to visually organize and analyze biological data. To
this end, we investigated new ways of exploring, organizing, and
summarizing systems biology data in graphs.
While the extent of systems biology data expands, so does
our knowledge of the organisms, processes and molecules under
investigation. Much of that knowledge is condensed in databases
of biological pathways. As a conceptual representation they
are essential for understanding data and putting it into context:
Pathways are the building blocks of our understanding of life
at its basis. Several common sources of biological pathways
Associate Editor: Prof. Martin Bishop
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Bioinformatics Advance Access published June 11, 2011
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MetaCyc (Caspi et al., 2008), Pathway Commons (Cerami et al.,
2010), WikiPathways (Pico et al., 2008) or Reactome (Matthews
et al., 2009). One of the most useful concepts for working
with pathways is a graph representation. It allows to view the
components of the pathway that are interacting in close context.
For example, it is natural to view metabolites as nodes and
reactions as edges. A petri network representation of pathways
(see for example Pinney et al. (2003)) allows better display of
regulatory processes and is standardized in SBGN (Le Nov` ere
et al., 2009), the Systems Biology Graphical Notation. In general,
exploratory analysis of pathways can lead to more effective
identification of hypotheses (Kelder et al., 2010). Graphs are also a
natural representation of regulatory networks and protein interaction
data. Also gene models have been successfully represented as
graphs (Heber et al., 2002), and so have overlapping sets (e.g.
clusters of (co)regulated genes) of any origin.
There are several tools available for the visualization of pathways
and general biological networks. These include generic tools
like Vanted (Junker et al., 2006), Ondex (K¨ ohler et al., 2006)
and Cytoscape (Smoot et al., 2011), a popular platform for
analysis of networks with more than 100 plugins for data analysis.
However, most of these tools have few data analysis tools for the
underlying biological data and instead focus on graph visualization.
For the field of biological pathways, many customized solutions
for specific aspects and organisms exist, among others KaPPA-
View (Tokimatsu et al., 2005), MapMan (Thimm et al., 2004) and
Paintomics (Garc´ ıa-Alcalde et al., 2010). GenMapp (Salomonis
et al., 2007) and PathVisio (Van Iersel et al., 2008) provide rich
pathway visualizations for a wide range of organisms. In contrast,
a plethora of methods for all kinds of analyses is implemented
in R (R Development Core Team, 2008), mostly based on the
Bioconductor (Gentleman et al., 2004) project. While this is
immensely useful and also offers a large number of visualization
plots, R does not offer much interactivity in general.
All these tools have interesting methods, but often require
researchers to exchange data between applications, which is
time consuming and often causes unnecessary problems like
incompatible formats, loss of data during conversion and repeated
analyses. Furthermore most applications do not make full use of the
potential of the graph representation. Other applications are limited
to certain organisms, data sources or cannot integrate measured
additional information on vertices and edges, can be used to
visualize many aspects of data, also simultaneously in detail and
as overviews. A graph based view of measured data is also a helpful
tool for manual or semi-supervised structuring of the data.
With MGV, we intend to provide an integrated solution for
all graph based data visualization, with the special focus on
measured data integration and visualization. Based on Mayday,
MGV is ensured to work on many high-throughput data sets without
conversion overhead or need for third party applications.
for example KEGG (Kanehisa et al., 2008),
especially when displaying
This section introduces the Mayday Graph Viewer (MGV), and its most
important components, for node rendering, layout and data integration,
based on its architectural design.
Probe in:2 out:1
1 [0006560 ? proline metabolic process ? inferred from electronic annotation, 0006562 ? proline catabolic process ? i
2 [0006198 ? cAMP catabolic process ? inferred from electronic annotation, 0019933 ? cAMP-mediated signaling ? inf
3 [0000002 ? mitochondrial genome maintenance ? inferred from direct assay, 0006457 ? protein folding ? inferred fr
Fig. 1. Rendering options of MGV. Plots for single values and single
or multiple genes are available. Simple representation of nodes is done
with various shapes. Additional information can be displayed via renderer
decorators and auxiliary items.
In Mayday, measured species, independent of the source, are called
“probes”. In Mayday’s data model, probes can be manually or automatically
grouped to unsorted sets.Meta information, for example gene annotations,
quality values or genetic loci, can be associated with any component of the
data model. The conceptual framework for MGV is a digraph G = (V,E),
which might be unconnected or even a degenerate case with E = ∅. Each
node v ∈ V can either be one or more probes representing experimental
data or meta data, such as molecules representing reaction components in
biochemical pathways. This concept allows to visualize data from different
studies. Furthermore, both nodes and edges are associated with a name, a
set of properties and a role which describes their biological meaning. The
user is presented an embedding of G in the plane, which can be interactively
modified or changed using several algorithms. The graphical representation
of nodes and edges depends on their respective roles and is configurable.
of the data. Furthermore, selections are shared, as are data transformations.
Any set B ⊆ V ?= ∅ can be visually grouped in MGV. Groups can be
displayed either in a rectangular or elliptical shape or as the convex hull of
the nodes as embedded on the screen. Possible operations on groups are the
calculation and display of distance and correlation heat maps. Visualization
of probe values and lists of probe properties or the most frequent annotations
can be displayed for the groups (see figure 1 of the supplemental material
for examples). Groups can either be built manually, or based on node
and probe properties, for example all nodes with probes can be grouped
according to the major gene lists of the probes. Also, k-Means and Qt
clustering (Heyer et al., 1999) can be applied to induce groups, if nodes
are associated with probes. Modularity clustering (Newman, 2006) is also
available (implementation from Noack, 2007) for group creation.
Graph-based visualization of data
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New graphs in MGV can be created from nodes originating from probes
or sets of probes, between which edges can be introduced manually or
automatically. Further nodes can be added manually, or from existing
Mayday data. Interaction partners can be queried from STRING (Szklarczyk
et al., 2010). External annotations can be imported from PubMed and
UniProt.. For the creation of other graphs, MGV contains an extensible
collection of tools to produce new graphs for several purposes. These
include, among others, clustering comparison, biochemical pathways, gene
models and a probe-centric view (useful to gather information about a singe
gene), all discussed below.
Partitioning clustering, significance tests and similar methods applied
to two or more different studies usually lead to different results. MGV
provides a graphical comparison tool for clusterings from two studies.
Clusters are represented as nodes in a bipartite graph, their intersection as
edges connecting the clusters. This allows to inspect how clusters are shared
between the two studies. If the total set of probes used in two datasets are
2006). Small or insignificant overlaps can be filtered out.
MGV can import biochemical pathways from KEGG and BioPax files.
For KEGG pathways, the layout as defined in the file is used. BioPax
pathways are displayed using SBGN. Additionally, overview graphs of
BioPax files, induced by either reactions or pathways as nodes and the
metabolites they share as edges can also be created. The import of graphs
from SBML and CellML files is also possible.
The analysis of differential splicing calls for a genome based view that
shows the different transcripts of a gene. MGV can visualize differential
splicing along with expression levels of transcripts as measured by tiling
micorarrays or RNAseq. This is done either based on splicing graphs (Heber
et al., 2002) or in a verbose style representing each isoform as a linked list.
For an intermediate view, a tree-based structure can be used that aggregates
common 5’-end exons. Exon-level data can also be viewed in MGV.
For a single probe, MGV can be used to aggregate all available
information and display it in a star-like graph. Information considered
encompasses the probe selections the target probe is contained in, the
profile of the probe in other datasets, meta information, genomic neighbors,
abstracts from PubMed, interaction partners from STRING, as well as
reaction partners from BioPax files. This view can help researchers to swiftly
learn about specific genes (see supplemental figure 2 for an example).
Networks from GML, XGMML, GraphML and GraphViz dot files can be
imported. Networks can be saved in GraphML, Dot and GML formats or a
specialized format (based on GraphML) that preserves all properties of the
current graph. When importing graphs, probes are automatically mapped to
all imported nodes when possible. Export of images is possible in PDF, SVG
and several bitmap formats.
Automatic extension of the graphs is possible by adding similar probes
(with respect to one of several distance measures). If a mapping of probes to
genomic positions is available, genomic neighbors of probes can be added.
External information, such as PubMed abstracts and interaction partners (via
STRING) can be added to complement the knowledge of the researcher.
Finally, nodes carrying a dynamic average of all probes connected to the
node can be added, for example to summarize the activity of a class of
enzymes. Several ways of weighting incoming and outgoing edges are
available. To remove unnecessary nodes from the graph, filtering can be
done on all aspects of nodes. Nodes can also be merged to form nodes with
Implementation MGV can be extended via plugins, including data import,
node rendering and layout, as well as graph manipulation and filtering.
MGV, as an extension of Mayday, is implemented in Java and both are free
and open source software licensed under the GPL (version 2), available at
MGV associates every node and edge with a role, which are used to decide
how the node or edge is displayed. For each node, the rendering can be
of renderers is available (see figure 1). If a node is associated with one or
more probes, the probe values are displayed using either color gradients,
heat maps, profile plots, star plots, bar plots and (for a large number of
probes) box plots. Various shapes are available for showing the node’s class
or properties. Special renderers are available for components like exons (in
gene models) and SBGN entities. For the summary of long time series, a
time series bitmap (Kumar et al., 2005) renderer is available.
In addition to the primary renderers described above,
information can be displayed using so called decorators. Decorators are
placed below or above a node. They are used to display the origin (set
of probes or dataset) or meta information connected with this node, for
example class partition, gene ontology annotations and relevance values
derived from statistical tests. Further additional information can be added
using auxiliary items. They are displayed as symbols placed on the margin of
the node. Auxiliary items can be used to manually add node-wise additional
information and can express uncertainty, highlight important facts and mark
Edges with different roles mostly differ in source and target decorations
(i.e. arrow heads) or line style. Edges can be drawn in a tapered style (Holten
than arrow heads. For displaying the weight of the edges, the width of
the edge can be adjusted, or the edge can be displayed in a zig-zag, with
the frequency encoding the weight. Reduction of edge overdrawing can
be achieved by using Bezier curves, thus heuristically bundling edges. In
addition to several predefined roles, it is possible to define additional roles
of edges and nodes for specialized rendering. Furthermore, each node and
edge can be configured to be rendered in an individual style.
(hierarchical, force-based, etc) are available. For graphs with no edges, these
algorithms are not applicable. Instead, such graphs are laid out on grids or on
circles. If probes are associated with nodes, their properties and values can
be used to induce an embedding, for example using principal component
analysis to place similar probes near each other.
Grouping and sorting nodes according to various criteria is used to
distribute groups of similar nodes on the screen. This can be done either
in rectangular, circular or axis-wise fashion (the latter as introduced as
Hive Plot, http://mkweb.bcgsc.ca/linnet/). Criteria for grouping include node
properties, node connectivity, probe values and probe annotation. Nodes
can be sorted according to several criteria within their group in order
to reproducibly structure the nodes within a component. This kind of
procedure allows to introduce external knowledge into the layout. This is
also applicable to general graphs, where for example (strongly) connected
components could form groups. An advantage of this method is the run time,
which is O(g · nlogn) for n nodes and g groups. Pathways are drawn in
a recursive scheme, see (Symons et al., 2010) for details. In addition to the
layout of the entire graph, users may automatically rearrange parts of the
graph, by aligning them, or centering entire connected components around a
node in a hierarchical way.
Working across several datasets is an important feature in MGV. In systems
biology, for example, transcriptomic, proteomic and metabolomic data
measure different aspects of a biological system. Usually, such data cannot
be directly compared, which means they cannot be combined in the same
dataset. However, visual comparison allows important insights and can
be done using MGV. Since the graph data structure is agnostic about the
origin of the probes, probes from different datasets in Mayday can be
represented in a common graph. To ensure comparability, a probe mapping
keeps track of which probes are equivalent between datasets. The same is
done for equivalent experiments. The various data transformations available
in Mayday are communicated between datasets, as well as selections of
Cross Dataset Operations
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Fig. 2. Differential expression within and between isoforms of RPL36A in human. The 3’-most exons are manually labeled according to the Ensembl biotype:
square: retained intron; triangle: nonsense medicated decay; circle: processed transcript; star: protein coding. Expression is visualized in a heat map style, with
an inverse black body radiation gradient. (black: high; white: low). (A) shows a verbose view of the gene. The labels below the nodes indicate the genomic
position of the exon; tool tips show the complete label. (B) and (C) show the simplified and compressed view, respectively.
mapped probes. Only a subset of the plugins is applicable to nodes with
probes of different origin, which makes visualization the most important
tool for this data. For visualization of probe data on nodes, no probe or
experiment mapping is required. Therefore, MGV can be used to visually
compare any data. However, statistical analyses between datasets require a
valid probe and experiment mapping. Based on grouped nodes of different
datasets, distance and correlation (Pearson, Spearman, Kendall) measures
can be calculated. A correlation heat map allows to inspect correlation
between probes from two datasets, along with their respective values (see
supplemental figure 3 for an example).
We illustrate some of the most important features of MGV using
typical questions in systems biology research. Since there are
different data requirements for each question, we use a variety of
datasets for illustration. In all examples, we use measured values
from expression and/or metabolomics studies mapped to nodes. To
visualize the expression values, we employ the inverse black body
radiation gradient, which works well in gray scale and color. Since
demonstrating every feature of MGV exceeds the scope of this
paper, the supplemental material contains several further examples.
Differential Expression of Gene Isoforms
Using RNAseq or tiling microarrays, it is possible to measure gene
expression on the gene isoform level. MGV can visualize both
differential expression within and between isoforms of the same
gene. In figure 2 we show an example of human gene RPL36A
(ribosomal protein L36a) that has 10 isoforms. The expression
values were generated from RNAseq data (Illumina, Inc., personal
communcation). Marked with a triangle, an isoform annotated
in Ensembl to be subject to nonsense medicated decay and a
similar one, annotated as a retained intron biotype (marked with
a square), show distinct up-regulation in some experiments. In
comparison, three of the four protein coding isoforms have a much
lower expression in these experiments. The remaining transcripts
with retained introns have very low expression values across all
experiments. In addition to the expression profiles of the transcripts,
MGV can show the overall structure of the transcripts. In figure 2B,
identical exons in the 5’-end of the transcripts are aggregated, which
allows to focus on the 3’-ends of the transcripts. Only three isoforms
of RPL36A share common exons at their 5’-end. The splicing graph
in figure 2C shows that there are some shorter exons in the center
of the gene that occur in many transcripts. They are indicated by the
high number and weight of adjacent edges. In this case, expression
profiles are displayed using a heat map with multiple lines.
Transcription Factor Activity in Yeast
We applied MGV to two publicly available datasets studying
transcription in Saccharomyces cerevisiae under various conditions.
Knijnenburg et al. (2007) studied yeast under four nutrient limited
conditions in chemostat cultures, in both aerobic and anaerobic
conditions. Each condition was investigated in triplicates. In
total, the dataset consists of 24 experiments. Marks et al. (2008)
investigated yeast during wine fermentation in 7 time points in
triplicates. The entire study has 21 experiments. Both studies were
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conducted using the Affymetrix Yeast Genome S98 platform, which
has 9335 probesets. The normalized datasets provided by GEO
(accession ids GSE1723 and GSE8536) were directly imported into
The authors of the chemostat study identified several so called
modules, intersecting clusters of co-regulated genes and a set of
transcription factors that potentially control the modules. For a
subset of each module, specific transcription factor (TF) binding
sites were found. These annotations induce a directed graph of 70
nodes (of which figure 3 shows a subset), representing either TFs or
sets of genes controlled by the TFs, and 59 edges, where an edge is
drawn if a set of genes is controlled by a TF. The complete graph
has 13 connected components and can be found in supplemental
figures 4 and 5. Expression values either of the transcription factors
or the controlled genes are mapped to the nodes. We added meta
information for class labels and number of probes for each node
and adjusted rendering rules to show profile plots for all nodes.
Edge weights were calculated as the average Pearson correlation
between the probes represented by the nodes adjacent to the edge.
The graph was laid out using the grouping-based method: sources
(transcription factors, inner ring) and sinks (target genes, outer
ring) were grouped respectively. The largest component (20 nodes,
representing altogether 44 unique probes) is displayed in figure 3.
From this figure, we can infer that Met32 has a high correlation
with several modules of genes that are up-regulated in sulfur-limited
conditions. Pho4 has a similar, but weaker correlation to genes
regulated by phosphorous limitation. The other transcription factors
have much weaker correlation with their respective target genes than
Met32 and Pho4.
It can be suspected that there is an overlap of the genes regulated
by nutrient depletion or anaerobic conditions identified in the
chemostat study with genes regulated during the fermentation time
series. We wish to answer the following questions: 1) How are genes
which are differently regulated during wine fermentation regulated
under nutrient or oxygen limitation? 2) How do genes controlled by
nutrient limitation react during wine fermentation?
We performed t-tests to identify genes differently regulated by
nutrient depletion and oxygen supply. For each condition, the
expression differences against all other samples were investigated.
For the wine study, tests were performed for all time points against
the first time point. In both cases, we considered genes with
p < 0.05 (after correction to control the false discovery rate) to
be significant. For both studies, the resulting genes were clustered
using k-Means, with k = 10 and k = 12, respectively.
Using the clustering comparison tool of MGV, a visual
comparison of the resulting genes was performed (see figure 4).
To do so, a graph was created, in which nodes represented clusters
and were connected to clusters in the other dataset if they shared at
least one probe. The edge weight was set to the overlapping fraction
of the size of the smaller cluster. Nodes with no adjacent edges
were omitted from the graph. Nodes were ordered descendingly
by the weight of all adjacent edges. In figure 4, the nodes adjacent
to edges representing 10% and 20% overlap are shown separately.
Concerning the first question, we can conclude from figure 4
that especially genes up-regulated during the early phase of the
fermentation (especially during the first 48 hours) overlap with
Fig. 3. Enriched visualization of transcription factors (inner ring of nodes)
regulating sets of genes (outer ring) in yeast. Expression values are displayed
as profile plots. Edge width is proportional to the mean correlation between
transcription factor and target gene expression. The number of genes
represented by each node is shown in the upper left corner of the node. The
nutrient limitations for carbon (C), nitrogen (N), phosphorous (P) and sulfur
(S) under aerobic and anaerobic conditions are displayed as a colored bar
below the plot.
genes up-regulated in carbon-depleted conditions, regardless of
oxygen supply. They also overlap with a cluster of 62 genes
mainly up-regulated in anaerobic carbon and phosphorus limited
conditions. Furthermore, we find that genes down-regulated at
the beginning of the fermentation (1h) overlap with genes down-
regulated in aerobic conditions. To a lower extent, this is also
true for a cluster of 60 genes down-regulated towards the end of
the fermentation. Concerning question two, we see that nutrient
depletion regulated genes, except for carbon, have little overlap
with genes regulated during fermentation. Limited intersection
of genes up-regulated under sulfur and of genes up-regulated
under nitrogen limitation exists with genes down-regulated only
during the beginning of the fermentation, while nitrogen related
genes are also up-regulated after 48h in the fermentation study.
Phosphorous-regulated genes share only very small intersections
with genes regulated during fermentation. When directly comparing
the differentially regulated genes (see supplemental figure 6),
similar conclusions can be made. In addition, a set of genes down-
regulated under nitrogen limitation is up-regulated at the beginning
of the fermentation.
Cross-dataset visualization of Pathways
We demonstrate the cross-dataset visualization features of MGV
with a dataset studying anaerobic growth in Pseudomonas
aeruginosa (Alvarez-Ortega and Harwood, 2007). Microarray gene
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20% overlap 20% overlap
10% overlap 10% overlap
Fig. 4. Bipartite cluster comparison graph: a comparison of clustered
differentially expressed genes between the chemostat study (Knijnenburg
et al. (2007); left) and the wine study (Marks et al. (2008); right). Clustering
was performed with k-means with k = 10 and k = 12 clusters respectively.
Expression is visualized as a profile plot. Edge weight is proportional to the
overlapping fraction of the smaller cluster. Intersections of more than 20%
and 10%, respectively are shown separately on the right.
expression data (ArrayExpress accession id E-GEOD-6741) was
imported into Mayday. Here, we found that highly variant genes
(coefficientofvariation> 0.1)contained, amongothers, genesfrom
amino acid degradation pathways.
To complement this data, we added similar but not directly
comparable metabolomics data from the Systomonas project (Choi
et al., 2007) that studied P. aeruginosa under similar conditions
(series 6 and 8). Figure 5A shows the tyrosine degradation pathway
of P. aeruginosa in MGV as a SBGN process diagram, with enzyme
activity estimated by transcript expression from the microarray data
and metabolite abundance estimated from the metabolomics study.
Figure 5B shows an alternative view of the enzymes in the tyrosine
degradation pathway. Especially the first enzyme, branched-chain
reactions. The reduced transcription of this enzyme under aerobic
conditions correlates with the reduced amino acids concentration.
MGV has moderate memory requirements. The two datasets used
for the yeast studies, encompassing 9335 genes and 45 experiments
in total, along with extensive meta information require 280 MB.
All examples shown here (including supplements) required no
more than 50 MB additional memory. Memory consumption grows
linearly with the number of graph objects. Gene profiles shown
at a node do not contribute towards the memory consumption
significantly. Rendering speed mostly depends on the number of
probes and edges in a graph, and the renderers used.
In this article we have introduced MGV, a new versatile
and extensible graph viewer for systems biology data.
combined the successfully implemented concepts of graph based
visualization of biological knowledge with the concept of using
small multiples (Tufte, 1983) for visualization of quantitative data.
In order to demonstrate the feasibility of our implementation of
this concept, we used data from different sources and contexts and
applied MGV to investigate common questions on it.
For regulatory networks, MGV provides an integrated view of the
network and of the expression data, in different levels of detail. In
general, the levels of detail can easily be configured via the many
rendering options for nodes. Possible details range from zero values
(showing only the shape of the node) to several hundred values (via
heat maps and profile plots). The latter case can suffer from over-
plotting when many probes are displayed. In this case, summaries
of the probes, e.g. box plots can be used. This is an application of
the visual analytics concept: data is summarized (or omitted, e.g. by
filtering out nodes or edges) in order to identify interesting regions
in the graph. Then the level of details can be vastly increased, by
zooming in and adding meta information to the nodes.
Versatility is a design goal for MGV also in the context of data
integration. MGV brings together quantitative data with annotation
data and textbook knowledge. The integration of expression data
with gene models allows to simultaneously visualize two levels of
differential activity: splicing and transcription, which is especially
interesting in larger RNAseq studies, where different classes of
samples are compared. The most useful rendering strategy for exon
nodes are heat maps and profile plots. Circular plots are less useful
here, as it is necessary to keep the width of the node constant.
MGV can work on a wide variety of data and is able to join data
between different sources and studies. This encompasses several
studies at the same level, e.g. two microarray studies as well as the
comparison of transcriptomics (or proteomics) with metabolomics
studies. We have illustrated this with the P. aeruginosa data,
in the context of a metabolic pathway in the BioPax format,
giving both an overview and details of the process. A direct
comparison of the metabolomics and transcriptomics data though
is not possible, since the studies differ in several criteria. Still, these
studies are comparable enough to demonstrate the use of MGV
for this application. Furthermore, to our knowledge, there is no
comprehensive multiomics dataset publicly available with whole
genome transcription data and a large number of measured and
identified metabolites. As technology advances, such datasets will
eventually be made available and MGV is well suited to work on
them, as demonstrated.
A further cross study visualization feature is the clustering
comparison tool of MGV. It allows to investigate overlaps of probe
groupings between datasets. In this way, properties of the probes
in each partition can be compared, as in the chemostat and wine
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aromatic amino aci…
Fig. 5. Visualization of pathways in MGV. (A) shows the tyrosine degradation pathway of Pseudomonas aeruginosa in SBGN format, with profile plots
for both microarray-based transcriptomics data and metabolomics data from different studies. The colors of the labels denotes the origin of the data. (blue:
transcriptomics, green: metabolomics). (B) shows an automatically generated schematic overview of reactions catalyzed by the branched-chain amino acid
aminotransferase (PA5013). The same data as in (A) is used. Throughout the figure, class labels are displayed as colored bars below the plot (blue: anaerobic;
red: aerobic conditions).
fermentation data. This allowed us to identify overlaps between
fermentation related genes and nutrient or oxygen dependent
genes, without using external annotations. Further applications
include comparing profiles of groups in closely related studies and
investigating the stability of clusterings among studies.
MGV assists in exploratory analysis of pathways, but can also
help in formulating new and confirmed hypothesis. For this purpose,
new graphs can be easily built in MGV. The resulting graphs can be
used to illustrate such a hypothesis, which can be then combined
with the underlying data in an interactive environment.
Based on Mayday, MGV is integrated into a well-established
framework, connected with many analysis and visualization tools.
We consider this to be beneficial because the workflow of data
analysis requires several data integration and analysis steps before
the high level analyses and visualizations that MGV is designed
for. New methods emerge continuously. Therefore, we consider it
to be important that all key features of MGV, node rendering, graph
layout, data import, graph manipulation and filtering, are extensible.
We are continuously extending MGV to provide new methods for
all these purposes. While extensibility and breadth of functionality
were in the focus of development, MGV is able to process large
sparse graphs with thousands of genes.
Further research is necessary on the questions of graph drawing
and visualization of dense datasets, especially in the context of how
to use the data properties of a node. For the latter topic, a thorough
user study should evaluate assets and liabilities of the current and
future approaches. Enhancements of the rendering options and
speed are also planned. Cross study comparison methods are also of
increasing interest. A large variety of methods for data integration
has been proposed, some of which require careful review and
integration into MGV. The development of MGV will continue
in these directions. For graph drawing, methods as suggested by
Stajdohar et al. (2010) for large graphs or Adai et al. (2004) for
handling unconnected graphs can greatly enhance MGV. Further
extensions in usability, e.g. a scripting feature for automation and
performance enhancements are planned.
MGV provides a set of powerful tools to integrate and visualize
systems biology data from many sources. High-dimensional data
visualization in a graph context is a powerful method to integrate
data from all omics sources with meta information and external
knowledge. MGV provides a wide range of tools for this purpose.
We have shown examples of data from genomes, transcriptomics
and metabolomics, which were seamlessly integrated and visualized
using MGV, even across datasets. Graph layout methods that use
the data at the nodes further enhance the analysis of data. With
upcoming new multiomics studies, MGV will be a useful tool to
make the most out of this data.
We acknowledge Florian Battke for helpful discussions and his
extensive work on data integration and normalization in Mayday
and Alexander Herbig for helpful input and discussions.
Conflict of Interest: none declared.
at Huazhong Agricultural University on April 15, 2012
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