Transcript How to look for the needle in a haystack

Jianguo (Jeff) Xia
Wishart Research Group
University of Alberta, Canada
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Metabolomics in the University of Alberta
DI/GC/LCMS,
HPLC, NMR
Software
Tools
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Compound
Databases
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Outline
 Introduction
 Omics data overview
 Lessons from other omics research
 Project goals
 Web-based metabolomics tools
I.
General data processing & statistical analysis
I.
MetaboAnalyst (http://www.metaboanalyst.ca)
Identify functionally interesting patterns
II.
I.
III.
I.
MSEA (http://www.msea.ca)
Metabolic Pathway Analysis
MetPA (http://metpa.metabolomics.ca)
 Public databases
 Summary
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The ‘-omics’ data overview
Genomics
DNA sequence
100,000 - 1,000,000
Transcriptomics
Gene expression
10,000 - 100,000
Proteomics
Protein expression/
interaction
1,000 – 10,000
Metabolomics
Compound
concentration
100 – 1,000
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Common questions
1.
2.
3.
4.
5.
6.
Are there some interesting patterns present in my
data?
What are the most important features associated
with different phenotypes?
Is there a real difference between the groups?
Can I use this data to predict a phenotype?
How to interpret these features / patterns?
How does my result compared with published
data?
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Common approaches
1st
Classical statistics
T-tests, ANOVA
Since 1950s
2nd
High-dimensional
feature selection;
Machine learning
SAM, Limma;
SVM, Neural
networks
Since 1990s
3rd
Group-based
enrichment analysis
GSEA, GSA,
Globaltest
Since 2003
4th
Pathway Analysis
SPIA, TopoGSA
Since 2007
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Project Goals
 Provide well-established methods proven highly
successful in other ‘omics’ studies;
 Do not re-invent the wheel!
 Support traditional approaches
 Cheminformatics approaches
 Data processing & normalization procedures
 Easy-t0-use
 Not command-line
 Target users – bench biologists
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Indentify influential algorithms
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Identify the best practices
Nature Genetics - 38, 500 - 501 (2006)
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Metabolomics web applications
• General data processing & analysis
– MetaboAnalyst
– http://www.metaboanalyst.ca
• Metabolite Set Enrichment Analysis
– MSEA
– http://www.msea.ca
• Metabolomic Pathway Analysis
– MetPA
– http://metpa.metabolomics.ca
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MetaboAnalyst
 http://www.metaboanalyst.ca
 General metabolomics data processing, normalization,
and statistical analysis
 Support two-group and multi-group analysis
 20+ well-established methods
 Dynamic graphical presentation
 Automatic report generation
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What MetaboAnalyst can:
 Basic data processing:
 Peak picking, Peak alignment, Baseline filtering, etc.
 Data normalization
 probabilistic quotient normalization, scaling, etc.
 Data overview
 PCA, Heatmaps, etc.
 Identify important features
 t-tests, ANOVA, SAM, etc.
 Classification
 PLS-DA, random Forest, SVM, etc.
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GC/LC-MS
raw spectra
MS / NMR
peak lists
• Peak detection
• Retention time correction
MS / NMR
spectra bins
Metabolite
concentrations
Baseline filtering
Peak alignment
• Data integrity check
• Missing value imputation
Data normalization
• Row-wise normalization (4)
• Column-wise normalization (4)
Data analysis
• Univariate analysis (4)
• Dimension reduction (2)
• Feature selection (2)
• Cluster analysis (4)
• Classification (2)
Data annotation
• Peak searching (3)
• Pathway mapping
Download
• Processed data
• PDF report
• Images
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MetaboAnalyst
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Data Upload
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Data processing and integrity check
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Data normalization (1)
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Data normalization (2)
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Data Analysis
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Clustering with PCA
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Hierarchical clustering
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Supervised approach – PLS-DA
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Feature selection - ANOVA
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Feature selection - SAM
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Data Download
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Updates & Forecast
 Recently upgraded
 Support for multiple group analysis
 One-way ANOVA & post-hoc analysis
 To be added
 To add some advanced methods for

Association analysis / ROC / OPLS
 To enhance web interfacing with XCMS
 Allow local installation

To be released by the end of this summer
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Data Interpretation
 Manual approach
 Background knowledge plus literature search
 Basic & Intuitive
 Can be very accurate
 Issues
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

Time-consuming
Subjective
Lack of statistical strength
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Introducing MSEA
 http://www.msea.ca
 Metabolite Set Enrichment Analysis
 Identify biological meaningful patterns from
quantitative metabolomics data
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Biologically meaningful
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Associated to the same biological process (i.e. pathway)
Associated with the same genetic traits (i.e. SNP)
Co-regulated in the same pathological conditions (i.e. disease)
Located in the same cellular compartment
Enriched in certain tissues/ organs
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The MSEA approach
Over-representation
Analysis
Single Sample
Profiling
Compound
concentrations
Compound
concentrations
Compound selection
(t-tests, ANOVA, PLS-DA)
Important compound lists
Quantitative
Enrichment Analysis
Compound
concentrations
Compare to normal
references
Abnormal compounds
Assess metabolite set
directly (GlobalTest)
Find enriched
biological themes
Metabolite set libraries
Biological interpretation
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MSEA @ www.msea.ca
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Over-representation analysis
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Compound label standardization
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Identify abnormal concentration
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Library selection
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Result
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Download
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MSEA summary
 More biologically–motivated
 Simultaneously biomarker identification and
interpretation
 Automatic comparison with published data
 Important patterns
 Reference concentrations
 Potential issues
 Limited by the size and quality of the knowledge database
 For pathway-based metabolite sets

Does not consider the pathway topology
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Topology matters
Glycine, serine and theonine metabolism
Galactose metabolism
p = 1e-5
p = 1e-7
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Introducing MetPA
 http://metpa.metabolomics.ca
 Pathway Analysis Tool
 884 pathways covering 11 model organisms
 Enrichment Analysis
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Global Test
Global ANCOVA
 Topology Analysis
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Degree Centrality
Betweenness Centrality
 Google-map style visualization
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MetPA
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11 pathway libraries (KEGG)
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Combining Enrichment analysis & Topology analysis
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Node importance measure: centrality
 Degree Centrality
 Local structure;
 Highly connected (hub)
 The Red nodes
 Betweeness Centrality
 Global structures;
 Sits on many shortest
paths between other
nodes
 The Blue node
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Junker et al. BMC Bioinformatics 2006
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Point and Click
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Lossless zooming
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Result table
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Downloads
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MetPA summary
 Combine statistical analysis and topological analysis
 Results are more close to manual identification
 Highly interactive visualization system
 allows easy hierarchical navigation within a large
amount of information
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Public Databases
 HMDB
 DrugBank
 SMPDB
 T3DB
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Summary
1st & 2nd
generation
3rd generation
• MetaboAnalyst: general data
processing & analysis
• MSEA: Metabolite set enrichment
analysis
4th
generation
• MetPA: Metabolomics Pathway
Analysis
5th
generation
• Integrate with other omics data
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Acknowledgement
• Dr. David Wishart
 Alberta Ingenuity Fund (AIF)
 The Human Metabolome Project (HMP)
 University of Alberta, Canada
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