BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 1 of 1544
Integrative analysis of transcriptomics and proteomics data: implications to cancer biology
Integrative analysis of transcriptomics and proteomics data (ArrayMining and TopoGSA)
ASAP – Interdisciplinary Optimisation LaboratorySchool of Computer Science
Centre for Integrative Plant Biology
Centre for Healthcare Associated InfectionsInstitute of Infection, Immunity and Inflammation
University of Nottingham
Enrico Glaab & Natalio Krasnogor
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 2 of 1544
Overview:
• Introduction: goals and data sets
• ArrayMining.net: tool set for microarray analysis– @ www.infobiotics.org
• TopoGSA: network topological analysis of
genes/proteins– @ www.infobiotics.org
• (time permitting) Network-based pathway
extension
Outline
Gibson G (2003) Microarray Analysis. PLoS Biol 1(1): e15. doi:10.1371/journal.pbio.0000015
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 3 of 1544
Introduction
• 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?
GOAL: Develop new (& existing) methods combining diverse data sources and algorithms
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 4 of 1544
Reference data set
Armstrong et al. Leukemia data set
• Platform: Affymetrix UV95A oligonucleotide array
• Normalisation: Variance Stabilizing Normalisation (Huber et al., 2002)
• 72 samples and 12,626 genes
• 3 leukemia sub-types: ALL (24), AML (28), MLL (20)
• Thresholding/Filtering steps: see Armstrong et al. (2001, Nat. Genet.)
• Public access to data set:http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
samples
Heat map: 30 most differentially expressed genes vs. samples
gene
s
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 5 of 1544
Main data set
QMC breast cancer microarray data set
• Platform: Illumina Sentrix Human-6 BeadChips
• Pre-normalized data (log-scale, min: 4.9, max: 13.3)
• 128 samples and 47,293 genes
• 3 tumour grades: 1 (33), 2 (52), 3 (43)
• Probe level data analysis: Bioconductor beadarray package
• Public access to data set:http://www.ebi.ac.uk/microarray-as/aeaccession number: E-TABM-576
grade1 grade 3
Heat map: 30 most differentially expressed genes vs. samples (grade 1 and grade 3)
gene
s
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 6 of 1544
Breast cancer data - difficulties
Breast cancer outcome is hard to predict:
Large degree of class-overlap in Breast cancer microarray data, whereasLeukemia decision boundaries are easy to find (Blazadonakis, 2009).
Van‘t Veer et al. Alon et al. Golub et al.
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 7 of 1544
Data Fusion
Other 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)
additional public 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:
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 8 of 1544
Methods overview
Methods overview: ArrayMining & TopoGSA
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 9 of 1544
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“ formicroarray analysis tasks
classical
new
www.arraymining.org
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 10 of 1544
Methods overview
Methods overview: ArrayMining & TopoGSA
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 11 of 1544
ArrayMining.net: Gene selection
Gene selection module• Applies supervised feature selection algorithms (CFS, eBayes, SAM, etc.)
• Compares multiple algorithms or combines them into an ensemble
• Example: ENSEMBLE feature selection for Armstrong et al. (2001) dataset:
Affymetrix ID Gene symbol Gene descriptions – source: F-statistic
32847_at MYLK myosin, light polypeptide kinase 159.59
1389_at MME membrane metallo-endopeptidase (neutral endopeptidase, enkephalinase) 137.53
35164_at WFS1 wolfram syndrome 1 (wolframin) 128
36239_at POU2AF1 pou domain, class 2, associating factor 1 116.75
1325_at SMAD1 smad, mothers against dpp homolog 1 (drosophila) 110.37
963_at LIG4 ligase iv, dna, atp-dependent 89.77
34168_at DNTT deoxynucleotidyltransferase, terminal 89.31
40570_at FOXO1 forkhead box o1a (rhabdomyosarcoma) 86.89
33412_at LGALS1 lectin, galactoside-binding, soluble, 1 (galectin 1) 81.31
previously identified by Armstrong et al. newly identified
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 12 of 1544
ArrayMining.net: Gene selection
Gene selection module (2): Armstrong et al. dataset
• Automatic generation of box plots with gene and sample class annotations
• The first row shows the box plots for the two best-ranked newly identified genes in the Armstrong et al. dataset ()
• The second row shows two top-ranked previously iden- tified genes ()
• The user can easily compare and combine the results from different selection methods
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 13 of 1544
ArrayMining.net: Examples
Further examples: Gene selection and Clustering module
Automatic generation of heatmaps and PCA Cluster plots (Armstrong et al. dataset)
samples
gen
es
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 14 of 1544
ArrayMining.net: Examples
Further examples: 3D-ICA and Co-Expression analysis
3D Independent Component Analysis plot (left) and the largest connected components from a gene co-expression network (right) for the Armstrong et al. dataset
Sample space: Gene space:
ALL
AML
MLL
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 15 of 1544
ArrayMining.net: In-house data
Heat map: 50 most significant genes Box plot: 4 most significant genes
Apply the tools on new data: QMC Breast cancer data
Expression levels across 3 tumour grades:
STK6 MYBL2
KIF2C AURKb
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 16 of 1544
ArrayMining.net: QMC dataset
Gene name PC (gene vs. outcome):Fold
ChangeQ-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.)
QMC Breast cancer data set – selected genes
• 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
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 17 of 1544
Methods overview
Methods overview: ArrayMining & TopoGSA
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 18 of 1544
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
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 19 of 1544
Sam
ple
1
Sample 2
ArrayMining.net: Consensus clustering
ArrayMining‘s consensus clustering approach:
Clustering Agreement:= No. of times pairs of samples are assigned to the same cluster across all input clusterings
Idea: Reward objects in the same cluster, if they have a high agreement.
Agreement matrix:
Aij := #agreements
across all clusterings for samples i and jFitness function:
:= (max(A)+min(A))/2
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 20 of 1544
• FSA (Fast SA; Szu, Hartley; 1987)Uses Cauchy-distributed random numbers anda sligthly modified cooling scheme:
• ASA (Adaptive SA; Ingber; 1993)Temperature-dependent pseudo-random numbers, quenching, „Re-Annealing“
• TSA (Thermodynamic SA; Vicente et al.; 2003)Automatically adjusts temperature based on laws of thermodynamics
Simulated Annealing - variants
Cauchy vs. Gaussian distribution
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 21 of 1544
Clustering methods and validity indices
• Sample clustering methods: 8 different methods considered:
- partition-based: k-Means, PAM, CLARA, SOM, SOTA
- hierarchical: AL-HCL, DIANA, AGNES
• Scoring & number of clusters selection: 5 validity indices / splitting rules used:
- Silhouette width, Calinski-Harabasz, Dunn, C-index, knn-Connectivity.
- good validity indices should have: no multivariate normality assumptions (Gower, 1981), small or no bias (Milligan & Cooper, 1985)
• Standardization: classical (mean 0, stddev. 1) or median absolute deviation
Example: Silhouette widtha(i) = avg. distance of obj(i) to all others
in the same clusterb(i) = avg. distance of obj(i) to all
others in closest distinct cluster
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 22 of 1544
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 widths)
best separationfor two clusters
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 23 of 1544
External validation
Random model Single clustering Consensus
Measure similarity of clusterings with the rand index R:
a, b, c and d are the #pairs of objects assigned to:
- the same cluster in both clusterings (a)
- different clusters in both clusterings (b)
- the same cluster in clustering 1/2 and different clusters in clustering 2/1 (c/d)
- Corrected for chance: adjusted rand index
Reference clustering: 3 tumour grades (low, medium, high)
Clustering results – external validation (tumour grades)
10000 random clusterings
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 24 of 1544
Methods overview
Methods overview: ArrayMining & TopoGSA
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 25 of 1544
ArrayMining.net: Gene set analysis
samples
pathways
Extension: Gene set analysis
• Expression levels 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)
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Subramanian et al. PNAS October 25, 2005 vol. 102 no. 43 15545–15550
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 26 of 1544
ArrayMining.net: Examples
Gene Set Analysis module – example analysis
Heat map for the Armstrong et al. dataset based on pathway meta-genes
• we apply the Gene Set Analysis module to the Armstrong et al. dataset• with known cancer gene sets the class separation is better than for single genes
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 27 of 1544
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
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 28 of 1544
External validation
Single clustering Consensus clustering Consensus (PAM+SOTA)
10000 random clusterings
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 29 of 1544
Interim Summary
• Consensus clustering (CC) results tend to be similar to or slightly better than the best single clusterings in terms of adj. rand index and validity indices (but longer runtime)
• The input clusterings should include diverse methods and exclude similar methods
• Using gene sets (GS) representing cellular pathways instead of single genes results in better cluster separation, adj. rand indices and validity indices (annotation data required)
GS & CC provide improved results, but: longer runtimes + annotation data required
ArrayMining Integrative Clustering - Summary
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 30 of 1544
Methods overview
Methods overview: ArrayMining & TopoGSA
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 31 of 1544
TopoGSA
TopoGSA: Network topological analysis of gene sets
What is TopoGSA? TopoGSA is a web-application mappinggene sets onto a comprehensive humanprotein interaction network and analysingtheir 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
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 32 of 1544
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:
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 33 of 1544
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 nodebetweenness
Mean clustering
coefficient Mean shortest
path length
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 34 of 1544
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.
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 35 of 1544
Real-world application of tools sets
ArrayMining identifies RERG as a tumour marker
• RERG (Ras-related and oestrogen-regulated growth-inhibitor) was identified as a new candidate marker of ER-positive luminal-like breast cancer subtype
• Validation using immunohistochemistry on Tissue Microarrays containing 1,140 invasive breast cancers confirmed RERG‘s utility as a marker gene
TMAs of invasive breast cancer show strong RERG expression
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 36 of 1544
RERG Protein Expression VS BCSS & DMFI
Kaplan Meier plot of RERG protein expression with respect to BCSS in ER+ U ER- cohort
BCSS in months250200150100500
Cum
ulat
ive
Surv
ival
1.0
0.8
0.6
0.4
Positive RERG expression
Negative RERG expression
p=0.002
DMFI in months250200150100500
Cum
ulat
ive
Surv
ival
1.0
0.8
0.6
0.4
Positive RERG expression
Negative RERG expression
p= 0.007
Kaplan Meier plot of RERG protein expression with respect
to BCSS in ER+ only
BCSS in months250200150100500
Cum
ulat
ive
Surv
ival
1.0
0.8
0.6
BCSS in months250200150100500
Cum
ulat
ive
Surv
ival
1.0
0.8
0.6
Positive RERG expression
Negative RERG expression
p=0.027
With
out a
djuv
ant t
reat
men
tW
ithou
t Tam
oxife
n tr
eatm
ent
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 37 of 1544
Conclusions(I): Feature comparison with similar tools
ArrayMining & TopoGSA
GEPAS (Tarraga et al.)
Expression Profiler(Kapushesky et al.)
Pre-processing:Image analysis, single- and dimensionality reduction, gene name normalization,cross-study normalization, covariance-based filtering
Pre-processing:Image analysis, missing value imputation, multiple single study normalization methods, dimensionality reduction, ID converter
Pre-processing:Image analysis, single study normalization, missing value imputation, dimensionality reduction,advanced data selection
Analysis:
Classification, Clustering, Gene selection, GSEA, PCA, ICA, Co-expression analysis, PPI-topology analysis, Ensembles/Cons.
Analysis:
Classification, Clustering, Gene selection, GSEA, PCA, CGH arrays, Tissue mining,Text mining, TF-binding site prediction
Analysis:
Clustering, Gene selection, PCA, Co-expression analysis (different from ArrayMining), COA, Similarity search
Usability/features:
PDF-reports, sortable ranking tables, data anno-tation, 2D/3D plots, e-mail notification, video tutorials
Usability/features:
special tree visualization (Caat, SotaTree, Newick Trees), 2D plots, data annotation (Babelomics),
Usability/features:
Excel export, XML queries, 2D plots, data annotation (GO, chromosome location)
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 38 of 1544
Conclusions (2)
• 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 flexiblyin an (almost) completely automated process
• New analysis types like network-based topology analysis and co-expression analysis complement existing tools
• In the case of BC it allowed us to identify candidate genes to characterise ER+ luminal-like BC.
– RERG gene is a key marker of the luminal BC class
– It can be used to separate distinct prognostic subgroups
• Accessible through www.infobiotics.org
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 39 of 1544
Outlook : PPI-based pathway-enlargement
Idea:
Enlarge pathways by adding genes that are „strongly connected“ to the
pathway-nodes or increase the pathway-“compactness“
Pathway extension criteria:
• degree(v) > 1; and
• #pathway-links(v,p) / #outside-links(v,p) > T1; or
• #triangle-links(v,p) / #possible_triangles(v,p) > T2; or
• #pathway-links(v,p) / #pathway-nodes(p) > T3
black = pathway-nodes;red blue green = nodes added based on different criteria
...
...
...
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 40 of 1544
Pathway enlargment – added genes
Example case: BioCarta BTG family proteins and cell cycle regulation
Black: Original pathway nodes – Green: Nodes added based on connectivity
Added cancer gene
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 41 of 1544
Pathway enlargment – Example 1
More than 20 proteins
annotated in our
PPIN
5 added proteins by the
extension process
3 known disease
associated
2 candidates: METTL2B,
TMED10
Example: Alzheimer disease pathway
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 42 of 1544
Pathway enlargment – Example 2
Example: Interleukin signaling pathways
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 43 of 1544
Pathway enlargment - conclusionThe method integrates two sources of information, extending
canonical pathways using large-scale protein interaction data
Identifies new regulators, new candidates for disease pathways
Future: investigate extended pathways as input for enrichment/classification methods
This work is based on the following papers:Arraymining: a modular web-application for microarray analysis combining ensemble and
consensus methods with cross-study normalization. E. Glaab, J. Garibaldi, and N. Krasnogor. BMC Bioinformatics, 10(1):358, 2009.
TopoGSA: network topological gene set analysis. E. Glaab, A. Baudot, N. Krasnogor and A. Valencia. Bioinformatics.http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btq131v1?ct=ct
RERG (Ras—related and oestrogen-regulated growth-inhibitor) expression in breast cancer as a marker of ER-positive luminal-like subtype. H.O. Habashy, D.G. Powe, E. Glaab, N. Krasnogor, J.M. Garibaldi, E.A. Rakha, G. Ball, A.R. Green and I.O. Ellis (to be submitted)
Extending biological pathway definitions using molecular interaction networks. E. Glaab, A. Baudot, N.Krasnogor and A. Valencia (to be submitted)
BigRoc, Weizmann Institute of Science, Israel, March 2010 Page 44 of 1544
Acknowledgements
• QMC: Hany Onsy Habashy, Desmond G Powe, Emad A Rakha, Graham Ball, Andrew R Green, Ian O Ellis.
• CS: Jon M. Garibaldi• CNIO: A. Valencia, A. Baudot
• BBSRC for grants BB/F01855X/1, BB/D0196131• EPSRC for grant EP/E017215/1• The EC for grant Marie-Curie Early-Stage-Training programme
(grant MEST-CT-2004-007597)