We present two novel web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA. These bioinformatics tools use integrative analysis methods, including ensemble and consensus machine learning techniques, as well as modular combinations of different analysis types, to extract new biological insights from experimental transcriptomics and proteomics data. They enable researchers to combine related algorithms and datasets to increase the robustness and accuracy of statistical analyses and exploit synergies of different computational methods, ranging from statistical learning to optimization and topological network analysis

Enrico Glaab

Jonathan M. Garibaldi

Natalio Krasnogor

English

AbstractWe present two novel web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA. These bioinformatics tools use integrative analysis methods, including ensemble and consensus machine learning techniques, as well as modular combinations of different analysis types, to extract new biological insights from experimental transcriptomics and proteomics data. They enable researchers to combine related algorithms and datasets to increase the robustness and accuracy of statistical analyses and exploit synergies of different computational methods, ranging from statistical learning to optimization and topological network analysis.</jats:p

Jonathan Garibaldi

Crossref

Nature Precedings

Integrative analysis of large-scale biological data sets

January 2011 Page 1 of 282
Integrative analysis of large-scale 
biological data sets
Enrico Glaab, Jonathan M. Garibaldi, Natalio Krasnogor January 2011
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Overview:
• Introduction: main goals and data sets
• ArrayMining.net: tool set for integrative microarray analysis
• TopoGSA: network topological analysis of genes/proteins
• Conclusions
Outline
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Introduction
Research questions and goals behind the thesis
• Typical problem in biosciences: How to make effective use of multiple, 
large-scale data sources?
• Typical problem in computer science: How to exploit the strengths of 
different algorithms for the same/related purpose?
 GOAL: Develop new methods combining diverse data
sources and algorithms
Machine learning Network analysis Statistics
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January 2011 Page 4 of 282
Introduction (2): Our data
Biological data sources used:
• unweighted binary
interactions (MIPS, 
DIP, BIND, HPRD,   
IntAct - only human) 
• 9392 nodes,
38857 edges
• mutated genes in 
different human   
cancer types 
(Breast, Liver,...)
• 30 gene sets of 
size > 10 genes
• obtained from GO, 
BioCarta, Reactome, 
KEGG and InterPro  
• total: approx. 3000 
pathways (size > 10)
• public microarray 
data sets: Huang 
et al., Veer et al.
• pre-processing:
GC-RMA
Breast cancer microarray data: Protein interaction data:
Cellular pathway data: Cancer gene sets:
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Methods overview
Methods overview: ArrayMining & TopoGSA
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January 2011 Page 6 of 282
Web-tool: ArrayMining.net
What is ArrayMining.net? 
ArrayMinining.net is an online microarray 
analysis tool set integrating multiple data 
sources and algorithms.
6 analysis modules:
1. Gene selection
2. Sample clustering
3. Sample classification
4. Gene Set Analysis
5. Gene Network Analysis
6. Cross-Study Normalization
Goal: A “swiss knife“ for
microarray analysis tasks
classical
new
www.arraymining.net
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Methods overview
Methods overview: ArrayMining & TopoGSA
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January 2011 Page 8 of 282
ArrayMining.net: QMC dataset
Gene name PC (gene vs. outcome): Fold Change
Q-value 
(Rank)
ESTROGEN RECEPTOR 1 -0.75 0.16 1.6e-20 (1.)
RAS-LIKE, ESTROGEN-REGULATED, 
GROWTH INHIBITOR -0.66 0.46 5.3e-14 (2.)
WD REPEAT DOMAIN 19 -0.66 0.73 1.2e-13 (3.)
CARBONIC ANHYDRASE XII -0.65 0.28 2.7e-13 (4.)
ARP3 ACTIN-RELATED PROTEIN 3 
HOMOLOG (YEAST) 0.64 1.37 9.6e-13 (5.)
TETRATRICOPEPTIDE REPEAT DOMAIN 
8 -0.63 0.82 2.2e-12 (6.)
BREAST CANCER MEMBRANE PROTEIN 
11 -0.62 0.24 7.1e-12 (7.)
Gene selection: QMC Breast cancer data set
• all top-ranked genes are known or likely to be involved in breast cancer
• the selection is robust with regard to cross-validation cycles and algorithms
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January 2011 Page 9 of 282
ArrayMining.net: In-house data
Heat map: 50 most significant genes         Box plot: 4 most significant genes
Visualization of results: QMC Breast cancer data
Expression levels across 3 tumour grades:
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January 2011 Page 10 of 282
Methods overview
Methods overview: ArrayMining & TopoGSA
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January 2011 Page 11 of 282
ArrayMining.net: Example
ArrayMining - Class Discovery Analysis module:
• Motiviation:
Exploiting the synergies between partition-based and hierarchical clustering 
algorithms
• Approach:
Consensus clustering based on the agreement of clustering results for pairs 
of objects (details on next slide). 
- equivalent to median partition problem (NP-complete)
- Simulated Annealing (SA) has been shown to provide good solutions
• Our solution:
- Compare SA (Aarts et al. cooling scheme) with thermodynamic SA (TSA) 
and fast SA (FSA)  FSA provides fastest convergence
- Initialization: Input clustering with highest agreement to other inputs
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Consensus clustering: example
Example application: QMC breast cancer dataset
• Separate sub-classes in 84 luminal samples with consensus clustering
• Input algorithms: k-Means, SOM, SOTA, PAM, HCL, DIANA, HYBRID-HCL
low confidence
(silhouette width:
compare within-
cluster- vs. between-
cluster-distances)best separation
for two clusters
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January 2011 Page 13 of 282
Methods overview
Methods overview: ArrayMining & TopoGSA
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January 2011 Page 14 of 282
ArrayMining.net: Gene set analysis
samples pathways
Gene set analysis – Motivation:
• Measurements for a single gene are often unreliable
• Similar genes might contain complementary information
• We want to integrate functional annotation data
 Gene Set Analysis (GSA):
1) Identify sets of functionally
similar genes (GO, KEGG, etc.)
2) Summarize gene sets to „Meta“-
genes (PCA, MDS, etc.)
3) Apply statistical analysis
(example: Van Andel institute cancer gene sets)
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January 2011 Page 15 of 282
Consensus clustering: example (2)
Combine consensus clustering with gene set analysis
• Map genes onto Gene Ontology (GO), reduce dimensionality (MDS)
• Apply same consensus clustering as before on GO-based „meta-genes“
~3 times higher 
confidence
better separation
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January 2011 Page 16 of 282
Methods overview
Methods overview: ArrayMining & TopoGSA
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January 2011 Page 17 of 282
TopoGSA
TopoGSA: Network topological analysis of gene sets
What is TopoGSA? 
TopoGSA is a web-application mapping
gene sets onto a comprehensive human
protein interaction network and analysing
their network topological properties. 
Two types of analysis:
1. Compare genes within a gene set:
e.g. up- vs. down-regulated genes
2. Compare a gene set against a
database of known gene sets
(e.g. KEGG, BioCarta, GO)
www.infobiotics.net/TopoGSA
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January 2011 Page 18 of 282
TopoGSA - Methods
• the degree of each node in the gene set
• the local clustering coefficient Ci for each node vi in the gene set:
where ki is the degree of vi and ejk is the edge between vj and vk
• the shortest path length between pairs of nodes vi and vj in the gene set
• the node betweenness B(v) for each node v in the gene set:
here σst(v) is the number of shortest paths from s to t passing through v
• the eigenvector centrality for each node in the gene set
TopoGSA computes the following topological properties for an uploaded 
geneset and matched-size random gene sets:
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January 2011 Page 19 of 282
KEGG-BRITE pathway colouring
LEGEND:
• Cellular processes
• Environmental information processing
• Genetic information processing
• Human diseases
• Metabolism
• Cancer genes
General results:
• Metabolic pathways 
have high shortest path 
lenghts and low bet-
weenness
• Disease pathways and 
cancer gene sets tend to 
have high betweenness 
and small shortest path 
lenghts
Mean node
betweenness
Mean clusterin
g
coefficient
Mean
 shortest
path
 lengthNa
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January 2011 Page 20 of 282
ArrayMining  TopoGSA
Send selected genes from ArrayMining to TopoGSA:
• Results of within-gene-set comparison:
Estrogen receptor 1 gene and apoptosis regulator Bcl2, both up-regulated 
in luminal samples, have outstanding network topological properties (higher 
betweenness, higher degree, higher centrality) in comparison to other genes.
• Results of comparison against reference databases:
- Metabolic KEGG pathways are most similar to the uploaded gene set in 
terms of network topological properties.
- Most similar BioCarta pathways: Cytokine, differentiation and inflammatory
pathways. 
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January 2011 Page 21 of 282
Conclusions / Outlook
• Combining algorithms in a sequential and/or parallel fashion can
provide performance improvements and new biological insights
• Microarray and gene set analysis tasks can be interlinked flexibly
in an (almost) completely automated process
• New analysis types like network-based topology analysis and co-
expression analysis complement existing tools 
• For further details: See our publications in BMC Bioinformatics
(Glaab et al., 2009) and Bioinformatics (Glaab et al., 2010)
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References
References
1. E. Glaab, A. Baudot, N. Krasnogor, A. Valencia. TopoGSA: network topological gene set analysis, 
Bioinformatics, 26(9):1271-1272, 2010
2. E. Glaab, A. Baudot, N. Krasnogor, A. Valencia. Extending pathways and processes using molecular interaction 
networks to analyse cancer genome data, BMC Bioinformatics, 11(1), 597, 2010
3. E. Glaab, J. M. Garibaldi and N. Krasnogor. ArrayMining: a modular web-application for microarray 
analysis combining ensemble and consensus methods with cross-study normalization, BMC 
Bioinformatics,10:358, 2009
4. E. Glaab, J. M. Garibaldi, N. Krasnogor. Learning pathway-based decision rules to classify microarray 
cancer samples, German Conference on Bioinformatics 2010, Lecture Notes in Informatics (LNI), 173, 
123-134
5. E. Glaab, J. M. Garibaldi and N. Krasnogor. VRMLGen: An R-package for 3D Data Visualization on the 
Web, Journal of Statistical Software, 36(8),1-18, 2010
6. H. O. Habashy, D. G. Powe, E. Glaab, N. Krasnogor, J. M. Garibaldi, E. A. Rakha, G. Ball, A. R. Green, 
C. Caldas, I. O. Ellis. RERG (Ras-related and oestrogen-regulated growth-inhibitor) expression in 
breast cancer: A marker of ER-positive luminal-like subtype, Breast Cancer Research and Treatment, 
2010 (Epub ahead of print)
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TopoGSA: network topological gene set analysis,

Integrative analysis of large-scale  biological data sets

http://precedings.nature.com/documents/5598/version/1/files/npre20115598-1.pdf

Integrative analysis of large-scale biological data sets

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