Vol. 23 no. 15 2007, pages 2024–2027
BIOINFORMATICS APPLICATIONS NOTE
Data and text mining
VISDA: an open-source caBIGTManalytical tool
for data clustering and beyond
Jiajing Wang1, Huai Li2, Yitan Zhu1, Malik Yousef3, Michael Nebozhyn3, Michael Showe3,
Louise Showe3, Jianhua Xuan1, Robert Clarke4and Yue Wang1,*
1Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington,
VA 22203,2Bioinformatics Unit, RRB, National Institute on Aging, NIH, Baltimore, MD 21224,3Systems Biology
Division, The Wistar Institute, Philadelphia, PA 19104 and4Lombardi Comprehensive Cancer Center and
Department of Oncology, Georgetown University, Washington, DC 20057, USA
Received on November 8, 2006; revised on May 22, 2007; accepted on May 22, 2007
Advance Access publication May 31, 2007
Associate Editor: John Quackenbush
Summary: VISDA (Visual Statistical Data Analyzer) is a caBIGTM
analytical tool for cluster modeling, visualization and discovery that
has met silver-level compatibility under the caBIG initiative. Being
statistically principled and visually interfaced, VISDA exploits both
hierarchical statistics modeling and human gift for pattern recogni-
tion to allow a progressive yet interactive discovery of hidden
clusters within high dimensional and complex biomedical datasets.
The distinctive features of VISDA are particularly useful for users
across the cancer research and broader research communities to
analyze complex biological data.
Supplementary information: Supplementary data are available at
Many biomedical hypothesis-driven studies can be formulated
as a clustering problem, such as classifying cancer subtypes,
detecting data outliers and identifying functional gene modules
(de Hoon et al., 2004). Clustering a complex dataset within
high dimensions is a challenging task. Several tools are
currently available to cluster and display complex biological
data (Dudoit et al., 2003; Gentleman et al., 2004; Reich et al.,
2006). Naturally, more capable methods are continuously being
developed for the analysis and interpretation of complex
datasets that may include multiple subclasses.
VISDA is an open-source clustering tool developed to
target the silver-level requirements of the cancer biomedical
informatics grid (caBIGTM) architecture and compatibility
guidelines (https://cabig.nci.nih.gov/tools/VISDA). caBIGTM
is a major initiative of the National Cancer Institute to create
an open-source, open-access information network enabling
cancer researchers to share tools, data, applications and
technologies according to agreed-upon standards and identified
needs. Compared to existing methods such as hierarchical
clustering (HC) and self-organizing map (SOM) as provided
by many popular analytical tools (Dudoit et al., 2003;
Eisen et al., 1998; Gentleman et al., 2004; Tamayo et al.,
1999), VISDA produces a coarse-to-fine cluster structure
supported by a statistical hierarchical mixture model and
supervised/unsupervised feature selection to ease the curse of
dimensionality. The embedded hierarchical data exploration
scheme helps discover and visualize the hidden tree of clusters.
Interactive user participation directs the clustering process; the
clustering solution is validated by a minimum description
length (MDL) based model selection. Finally, VISDA uses soft
data decomposition to model overlapped clusters (Wang et al.,
2000;Wang et al., 2003). Thus, VISDA offers both an adjunct
and an alternative to existing methods.
As one of the adopted data analytical tools in caBIGTM,
VISDA offers users across the cancer research and broader
research communities a unique yet effective data clustering tool
for cluster modeling, discovery and visualization. VISDA is an
open-source software. The Java and C source code and
documents of application program interfaces (APIs) are
provided at http://gforge.nci.nih.gov/projects/visda/ enabling
users to modify the program and add new functions or
The core algorithms of VISDA are implemented in Cþþ
and the visualization functions and user interface are imple-
mented in Java. To improve the design performance and speed
up the design time, we used the Linear Algebra Package
(LAPACK) for basic vector and matrix operations and two
open Java packages, jmathplot and epsgraphics for plotting
graphics. Importing data from the caArray data portal to
VISDA is facilitated by caArray MAGE-OM APIs. Importing
data from local MAGE-ML file to VISDA uses MAGEstk
(The MAGE Software Toolkit). VISDA has been tested
on Microsoft Windows XP, Linux and UNIX platforms.
Users can install VISDA directly on a computer and launch
*To whom correspondence should be addressed.
? The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: firstname.lastname@example.org
the program from batch files provided in the deployment
2.2 Architectural design
VISDA contains four major components: (1) edu.vt.cbil.visda,
(2) edu.vt.cbil.visda.data, (3) edu.vt.cbil.visda.comp and (4)
edu.vt.cbil.visda.view. The class objects and their relations are
designed using Unified Modeling Language (UML). edu.vt.
cbil.visda package provides a main entry to perform the initial
setup, data input and output, data analysis and data/results
visualization by calling well-defined APIs. edu.vt.cbil.visda.
data can interact with microarray data sources such as caArray
database, local MAGE-ML files and tab-delimited text files
and then make the data available to edu.vt.cbil.visda.comp.
In edu.vt.cbil.visda.comp, we implemented the following
modules: (1) cluster modeling module (CMM), (2) dimension
reduction module (DRM), (3) cluster formation module
(CFM), and (4) cluster validation module (CVM). These
modules comprise the essential cores of the VISDA toolkit
(Wang et al., 2000). edu.vt.cbil.visda.view can display the data
profile, and both the intermediate and final output results
including the ‘soft-clustering’ probabilities of the samples/genes
in each cluster and a graphical representation of the estimated
hierarchical ‘tree of phenotype’ and/or ‘tree of gene module’.
tab-delimited text file including multiple annotations of genes
and conditions; (ii) any local data file in MAGE-ML format
and (iii) data retrieved from caArray. All gene/sample
annotation fields can be automatically extracted and used for
subsequent cluster discovery. The uploaded data can also be
visualized as a heatmap before the scheduled analysis,
providing the user a global view of the entire data set. The
configuration step gives a user the freedom to choose among
different analysis tasks, such as gene/phenotype clustering,
supervised/unsupervised feature selection, various projection
methods and other advanced features including cluster valida-
tion. VISDA core algorithms are then activated to perform the
targeted clustering on the uploaded gene expression data.
Data preprocessing The input data can be (i) any
ing functions for sample/gene clustering: (i) supervised and
unsupervised feature selections; (ii) discriminatory data projec-
tions for exploratory cluster visualization, including principal
component analysis (PCA) and projection pursuit method
(PPM); (iii) hierarchical statistical modeling and parameter
estimation by the expectation-maximization (EM) algorithm
and (iv) advanced functional options including Fisher dis-
criminatory component analysis (DCA) projection, MDL
cluster validation and hybrid clustering initialization using
Analytical algorithms VISDA implements the follow-
played as a heatmap. Annotations of the conditions are
shown at the top; annotations of the genes are listed on the
right. During the clustering process, clusters at each hierarchical
level can be visualized by three individual 2D projections: PCA,
Information visualization Expression data are dis-
PPM and DCA. The user can then select the best projection
view for further classification at the deeper–levels. One of
VISDA’s distinctive features is the integration of human
intelligence into the automation of the core algorithms.
To leverage a user’s prior knowledge and visual cues about
data patterns, VISDA allows each user to initialize the number
of clusters and their centers at each exploration level. The
iterative user-algorithm interactions exploit the power of
the human gift for pattern recognition and statistical machine
learning, assuring robust and globally converged clustering
sentative screen shots from VISDA. All the sub-level results are
stored into a hierarchical structure, and a pie chart diagram
(Fig. 1D) shows the growth of the HC tree. All the pictures can
be viewed, zoomed and saved in either PNG or EPS format.
At each hierarchical level, the clustering posterior probabilities
of all samples/genes belonging to each cluster can be saved as
a text table with multiple sample/gene annotations. The table of
the most informative genes/features selected for array cluster-
ing, ranked by their respective signal-to-noise ratio (SNR;
supervised) or variance (unsupervised) criteria, can also be
viewed and saved.
Graphic user interface—GUIFigure 1 shows repre-
several ongoing projects for cancer diagnostic and muscular
dystrophy studies and shown its effectiveness for subclass
discovery and novel cluster detection (Bakay et al., 2006; Zhu
et al., 2006). The Showe’s lab at the Wistar Institute
(Philadelphia, PA, USA) applied VISDA to a dataset of two
head and neck cancer subtypes. VISDA reveals two novel
subclusters in one of the subtypes, providing new biological
insights into cancer development. Guided by a pathologically
plausible diagnostic Tree of Phenotype (TOP), we conducted
gene clustering by VISDA on a microarray gene expression
dataset of 12 different muscle dystrophies and normal skeletal
muscle. We then superimposed prior knowledge of gene
regulation to analyze the clustering results and generate novel
hypotheses for further research on muscular dystrophies. We
obtained several condition-specific gene bi-clusters at different
nodes/levels of the VISDA derived TOP, and shown their
potential association with specific pathway gene regulatory
networks (Zhu et al., 2006).
Case study VISDA has been tested and used in
VISDA was developed to be consistent with the caBIGTMsilver
compatibility guidelines that highlight the use of controlled
vocabularies, common data elements (CDEs), well-documented
APIs and UML models. As a caBIGTManalytical tool, VISDA
is capable to retrieve data from caArray (caGrid node) and
locally perform computations upon these data. We implemen-
ted VISDA client APIs to consume silver-compatible MAGE-
OM APIs and caArray CDEs. Class diagrams in UML were
provided for all VISDA packages and APIs. Documentations
of the programming interfaces were derived from the imple-
mentation using JavaDocs.
VISDA: an open-source caBIGTManalytical tool
There are several workflow scenarios for using caBIGTM
VISDA. For example, end users can access the data files and
annotations through the caArray data portal and perform
clustering analysis. End users can also access the data files and
annotations from caArray, then conduct normalization by
distance weighted discrimination (DWD, another caBIGTM
analytical tool), or by other normalization tools provided in
caBioconductor (an ongoing caBIGTMproject), and finally
perform clustering analysis by VISDA. Using the current
version of VISDA, users can retrieve a BioDataCube for
DerivedBioArray of the selected experiment as well as
associated composite sequence names and sample names via
the MAGE-OM API, and then generate data matrix for all the
arrays. Composite sequence
DesignElement objects. caArray 1.3 or above system auto-
matically annotates the sample name in the biomaterial
annotation, delimited by ‘_L_’. Therefore, VISDA labels
samples by parsing biomaterial names in BioMaterialMeasure-
ment objects from the BioAssay. We noticed that some
BioAssay objects retrieved from caArray do not contain
BioMaterialMeasurement objects. In such cases, VISDA
assigns bioassay names as sample names.
Human data interaction by VISDA can easily encode domain
knowledge when used by domain experts. The statistical tree
of clusters revealed by VISDA may provide meaningful
relational biological information, and also allow cluster
analysis at multiple resolutions. Since clustering algorithms
always reflect some structural bias associated with the involved
grouping principle (Frey and Dueck, 2007), it is recommended
that for a new dataset without much prior knowledge one
namesare obtained from
should try several different clustering methods or use an
ensemble scheme that combines the results of different
We plan to extend VISDA to include additional advanced
analytical functions, such as unsupervised or semi-supervised
gene/feature selection, outlier detection and conditional cluster-
ing via iterative and combinatorial gene and sample clustering.
Other potential additions include using a stability analysis-
guided phenotype clustering and visualization method to
discovera highly resolved
Eventually, we will integrate VISDA into caGrid as a full
functioned analytical service component.
TOP from genomicdata.
The authors would like to thank members of the caBIGTM
Integrative Cancer Research WorkSpace for reviewing VISDA
documentation and/or providing helpful feedback on VISDA
development. Thanks also go to the caArray Team at the
National Cancer Institute for providing caArray testing
datasets. This work is supported by the National Cancer
Instituteof theNIH under
CA109872; CA100970; CA096483.
Conflict of Interest: none declared.
Bakay,M. et al. (2006) Nuclear envelope dystrophies show a transcriptional
fingerprint suggesting disruption of Rb-MyoD pathways in muscle regenera-
tion. Brain, 129, 996–1013.
Fig. 1. Representative screen shots from VISDA for a top–down clustering of a real data set with ground truth (color coded). A navigation panel
on the left leads users through the process of data analysis. A working view panel on the right displays the analysis results. (A) A projection view at
level 1; (B) a projection view at level 2; (C) a projection view at level 3 (D) a hierarchical visualization of the clustering and outcomes. Color version of
this figure is available as Supplementary material online.
J.Wang et al.
de Hoon,M.J. et al. (2004) Open source clustering software. Bioinformatics, 20, Download full-text
Dudoit,S. et al. (2003) Open source software for the analysis of microarray data.
Biotechniques, Suppl. 45–51.
Eisen,M.B. et al. (1998) Cluster analysis and display of genome-wide expression
patterns. Proc. Natl Acad. Sci. USA, 95, 14863–14868.
Frey,B.J. and Dueck,D. (2007) Clustering by passing messages between data
points. Science, 315, 972–976.
Gentleman,R.C. et al. (2004) Bioconductor: open software development
for computational biology and bioinformatics. Genome Biol., 5, R80.
Reich,M. et al. (2006) GenePattern 2.0. Nat. Genet., 38, 500–501.
Tamayo,P. et al. (1999) Interpreting patterns of gene expression with self-
organizing maps: methods and application to hematopoietic differentiation.
Proc. Natl Acad. Sci. USA, 96, 2907–2912.
Wang,Y. et al. (2000) Probabilistic principal component subspaces: a hierarchical
finite mixture model for data visualization. IEEE Trans. Neural Netw., 11,
Wang,Z. et al. (2003) Discriminatory mining of gene expression microarray data.
J. VLSI Signal Process., 35, 255–272.
Zhu,Y. et al. (2006) Phenotypic-specific gene clustering using diagnostic tree and
VISDA. Proceedings of the 28th IEEE EMBS Annual International
VISDA: an open-source caBIGTManalytical tool