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Unlocking the potential of public available gene expression data for large-scale analysis. Jonatan Taminau PhD defense, November 2012. Introduction. In this thesis: Focus on data to information step. Focus on microarrays technology. Data. Information. Knowledge. Introduction. Data. - PowerPoint PPT Presentation
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Unlocking the potential of public available gene expression data for large-scale analysis
Jonatan TaminauPhD defense, November 2012
22
Introduction
• In this thesis:•Focus on data to information step.•Focus on microarrays technology.
Data KnowledgeInformation
33
Introduction
Data Information
Data Repositories: + Massive amounts + Examples: GEO, ArrayExpress + Publicly available!
Analysis Software: + Commercial: CLC Bio, Spotfire, etc. + Free: Bioconductor, Genepattern, Galaxy, etc. + A lot of existing research
44
Introduction
“Although hundreds of thousands of samples are publicly available, and several powerful analysis software solutions exist, the research community is facing a chasm between these two resources.” (Coletta et al, 2012)
“One of the challenges for the future is how to integrate all the DNA microarray data that have been generated and deposited in public databases.” (Larsson et al, 2006)
?
55
Introduction
• We identified two hurdles for large-scale microarray analysis:
① Consistent retrieval of individual datasets.
② Integrative analysis of multiple data sets.
66
Outline
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6 Chapter 7
Chapter 8
Chapter 9
77
Outline
Retrievalof data
IntegrativeAnalysis
Problem Statement
inSilico DB
Problem Statement
Meta-Analysis Merging
Application
88
Outline
Retrievalof data
Problem Statement
inSilico DB
Problem Statement
Meta-Analysis Merging
Application
IntegrativeAnalysis
99
Retrieval of genomic data
•Data is online, freely available•But: difficult to consistently retrieve
the data (Example: Baggerly & Combes, 2011)
•What does it mean?•Data retrieval is reproducible and
tractable•No manual intervention needed•All data is preprocessed the same
1010
Retrieval of genomic data
•Typical microarray workflow:
Image
CELfileScanner Prepro-
cessing
DNAmicroarray
ImageAnalysis
numerical(‘raw’) data
Gene expressionmatrix
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Retrieval of genomic data
CELfile Prepro-
cessingnumerical(‘raw’) data
Gene expressionmatrix
Complex + normalization/background correction + probe-to-gene mapping + versioning issues + etc. Not Documented!
“only 48% of all data in GEO and ArrayExpress was submitted with raw data” (Larsson et al. 2006)
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Retrieval of genomic data
+ Features+ Genes or probes+ range: 20k-30k
+ Instances+ Patients, tissues, etc.+ range: 10-100
Gene Expression Value: + Expression of gene i in sample j + range between 2-14 + log2 scaled
xij
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Retrieval of genomic data
•What about phenotypical data or meta-data ? •Extra information about the samples
(age, gender, disease, etc.)•No standard way of formatting this
information•MIAME / Ontologies / Free text / etc.•Also still an open problem
1414
Retrieval of genomic data
•Why is consistent retrieval from public repositories so important?•Reproducibility of results•Comparison of new results with
existing studies•Combining different studies
1515
Outline
Retrievalof data
Problem Statement
inSilico DB
Problem Statement
Meta-Analysis Merging
Application
IntegrativeAnalysis
1616
The inSilico Database
•Result of InSilico project• Innoviris (2007-2012)•8 persons from VUB & ULB
•Provides consistently preprocessed and expert-curated genomic data
•Being commercialized
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The inSilico Database
•What makes the inSilico Database so valuable ?•Not the fact that all data is
precomputed•But how it is precomputed
•What is the underlying engine ?•Genomic Pipelines•Backbone
1818
The inSilico DB | Genomic Pipelines
•For every data type there is a different pipeline
•Microarray pipeline:
• Jobs• Dependencies• Backbone
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The inSilico DB | Backbone
•Automatic Workflow System•Barely manual intervention needed•Control of intermediate results•Pre-computation saves time (for the
user)•Streamlined Error management•Automatic Monitoring
2020
The inSilico DB | Backbone
•How does it works?• Java daemon (recently replaced by
application server)•Configuration Files
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inSilicoDb package
•One thing missing for large-scale analysis...•Programmatic access via scripting
•Contains the basic functionality of InSilico DB
•Makes automatic retrieval of data possible!
•Seamlessly integrates with other bioconductor analysis tools
•Published in Bioinformatics, download > 2000 times
2222
Outline
Retrievalof data
Problem Statement
inSilico DB
Problem Statement
Meta-Analysis Merging
Application
IntegrativeAnalysis
2323
Integrative Analysis
•“Combining the information of multiple, independent but related studies in order to extract more general and more reliable results”
•Problem: •How to do it ?
•Two approaches:•Meta-Analysis•Merging
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Integrative AnalysisMergingMeta-Analysis
2525
Outline
Retrievalof data
Problem Statement
inSilico DB
Problem Statement
Meta-Analysis Merging
Application
IntegrativeAnalysis
2626
Meta-Analysis
+ Combining p-values + Combining effect sizes + Combining Ranks + Vote Counting + etc.
+ Depends on goal + Much focus on finding DEGs + Defines what the results look like
+ Consistent Retrieval is essential ! + inSilicoDb package
2727
Meta-Analysis | Stable Genes
•365 studies were screened for stable genes
•Motivation:• Interested in reference genes•Currently used genes (housekeeping
genes) are not ideal•Need a compact and diverse list of
genes that are stable under most conditions
• In collaboration with Dr Bram de Craene (VIB-UGent)
2828
Meta-Analysis | Stable Genes
(1) Retrieve Data + inSilicoDb package + All 365 datasets downloaded in less than 100 min
(2) Calculate Stability Scores + For each gene: + Coefficient of Variation (CV) sd / mean + avoid lowly expressed genes
(3) Combine Stability Scores + For each gene take median of CVs + Rank and take top 100
(4) Semantic Similarity Filtering + Exclude genes that are related + Uses gene annotation from GO + Innovative Step! + From 100 to 10 genes
2929
Meta-Analysis | Stable Genes
•Status:
•August 2012 | waiting for results…•September 2012 | first positive
results!•November 2012 | second test case,
positive feedback from NAR, manuscript in preparation…
3030
Outline
Retrievalof data
Problem Statement
inSilico DB
Problem Statement
Meta-Analysis Merging
Application
IntegrativeAnalysis
3131
Merging
+ Consistent Retrieval is essential ! + inSilicoDb package
+ Batch effects + Methods to remove - Location-scale - Matrix Factorization - Discretization+ Makes data compatible+ Preprocessing not
sufficient
+ Same as with single studies + Increased sample size !
3232
Merging | Batch Effects
• Illustrative Example what batch effects can cause:•We merged 4 different studies with
thyroid samples•All studies contained normal and
tumor samples• In collaboration with Wilma Van
Staveren (IRIBHM, ULB)
•Samples are plotted in MDS space•We expect two clusters
3333
Merging | Batch Effects
Merging without batch effect removal Merging with batch effect removal
Legend: + symbol for study + color for normal/tumor
3434
inSilicoMerging package
•R/Bioconductor package combining:•6 different merging methods•5 visual inspection tools•6 quantitative measures
•Only resource so far combining all this functionality !
•Seamlessly integrates with inSilicoDb package
3535
Outline
Retrievalof data
Problem Statement
inSilico DB
Problem Statement
Meta-Analysis Merging
Application
IntegrativeAnalysis
3636
Identification of DEGs in Lung Cancer
• Idea: compare meta-analysis and merging approaches for integrative analysis
•We used lung cancer as case based on the content of inSilico DB.
• Ignore subtypes: DEGs can be seen as playing a role in the basic mechanisms of lung cancer
3737
Identification of DEGs in Lung Cancer
•What is our hypothesis ?
•Due to the small sample sizes of individual studies there are a lot or False Negatives when using meta-analysis
•Can we avoid this by using merging as an alternative approach?
3838
Identification of DEGs in Lung CancerMergingMeta-Analysis
Constraints: + fRMA preprocessed + > 30 samples + both normal and tumor + GPL96 or GPL570 Methodology: + apply limma - p-value < 0.05 - FC > 2+ robustness test - 100 iterations with 90% of data - resampling
+ inSilicoMerging package
+ take intersection
3939
Identification of DEGs in Lung Cancer
• Meta-Analysis:
4040
Identification of DEGs in Lung Cancer
• Merging:
4141
Identification of DEGs in Lung Cancer
• Findings:• Resampling helps to remove false
positives• Relatively low impact of batch effect
removal methods• More DEGs identified through merging
(102) than via meta-analysis (25)“Deriving separate statistics and then averaging is often less powerful than directly computing statistics from aggregated data.” (Xu et al, 2008)
no False Positives? + checked literature + initial pathway analysis
4242
Outline
Retrievalof data
Problem Statement
inSilico DB
Problem Statement
Meta-Analysis Merging
Application
IntegrativeAnalysis
+ Contributions+ Conclusions
4343
Contributions
•Genomic pipelines / backbone (Ch 4)•Release of 2 publicly available
R/Bioconductor packages (Ch 4 & 7)•Survey of batch effect removal methods
(Ch 7)•Two applications• Identification of stable genes via meta-
analysis (Ch 6)•Screening of potential biomarkers via
integrative analysis (Ch 8)
4444
Conclusions
• We identified two hurdles for large-scale microarray analysis:
① Consistent retrieval of individual datasets.
② Integration of multiple data sets for integrative analysis.
4545
Conclusions
① Consistent retrieval of individual datasets. inSilicoDb package
② Integration of multiple data sets for integrative analysis. inSilicoMerging package
Paving the road towards unlocking the potential of public available gene expression studies
4646
Thanks!
+ InSilico Team!+ Jury!
+ Audience!
+ Yann-Michaël!