1. Microarray data repositories
Mark Lambrecht
The Arabidopsis Information Resource

2. Bioinformatics : process
Bioinformatics is involved in
storing
managing
integrating
retrieving
analysing
biological data; genome data, gene expression, proteomic data

3. Part I : challenges in constructing MA data repositories
Part II : Existing microarray data repositories
case study : integrating microarray information in Arabidopsis Information Resource

4. Goal To store gene expression info to allow for Dealing with
generation and interpretation of experimental data
integration of data in data repository
Dealing with
integrity
testing quality of data, QA (log(R/G) vs log(RG)
high-throughput
exchange of data

5. A trivial problem ?
Organisation of data determines scope of analysis and results generated by analyses !
Organisation for a few experiments not necessary, but in case of 20, 300, 1000 MA experiments ?
Organisation of data has to reflect as good as possible biological reality and thus expert bioinformaticians with core biological background needed.
Most complex: integrating MA data in existing biological repository.

6. Problem 1 : generation and interpretation of experimental data
Raw data as generated by a technology platform :
oligo microarrays
cDNA microarrays
Affymetrix microarrays
Data dependent on platform (expression scales, linearity, absence of ‘interconversion’ standard)

7. Interpretation Data is Integrated (secondary) data redundant
non-redundant
annotation of data : manually, automatic (data cleaning step)
Example : annotation of Affymetrix probe sets.
Integrated (secondary) data
composition of related data from different sources

8. Key challenge Keeping track of data processing, refinement, revision
Effect of data modelling, data collection, data management infrastructure

9. Example : Affymetrix data platform
12-20 probe pairs (PM, MM)/probe set
DAT file : binary image
CEL file : quantitative representation
CHP file : gene expression measurements

10. Example : Affymetrix platform
Raw data : probe array images (.DAT files)
Interpreted data
probe intensity data files (.CEL files)
probe set summarized data (.CHP files)
replicate summarized data
normalized data across experiments
filtered data
annotation information

11. Example: cDNA microarrays
EST/cDNAs are PCR amplified and attached to glass slides by automation
Example : normalization by ‘tip’ groups
Raw data generated by image analysis programs (2 channel laser scanning device)
Processed data : preclustering files (.pcl files)
NCBI Locuslink : problem of annotation of ESTs (fully-sequenced organism).
Problems : biochemical manipulations, PCR product concentration, hybr efficiency, cross-hybridization

12. Example: cDNA microarrays

13. Data review Review, possibly correct experimental results
data : quality assessment (quality metrics : work in progress…)
technology
LIMS = laboratory information management system, validation (QC) tools, tracking systems : complex!
analyze and correct process steps, failures
interpret analysis results

14. Data comparability
Each MA experiment is different ; scanner used, probes, glass type, tips, hybridization conditions, etc… has to be tracked in database
Computational factors : hypothesis testing, analytic factors

15. Data analysis and interpretation

16. Data collection : warehouse
Goals: single source expression data
Rationale : support periodic or continuous accumulation of data for effective exploration and analysis of massive amounts of data
Data structure :
central object characterized by measurement attribute
dimension object ; organized in classification hierarchies
Key challenge : applying warehouse concept to the gene expression data domain

17. Problem 2 : MA data integration in MA repository
Alternatives
light ; data items in autonomous repositories cross-referenced
loose : autonomous repositories accessed through common view
tight : integrated in a common data repository => resolution of semantic problems required.

18. Associations Shallow : data collection and sorting
deep : determine semantic relationships

19. Complexity Determined by number of data types involved in integration
Quality of data in different repositories :
example: integrate MA database with poorly described experiments in model organism database.
Key challenge: understanding the analysis and performance requirements and finding the best alternative addressing them

20. Problem 3 : Integrating MA data in biological data warehouse
New challenges: several sources and platforms have to be integrated (Affymetrix, cDNA spotted arrays, oligo).
Providing enhanced expression data context for analysis

21. Methodology Independent data collection
different image analysis software suites
different ‘treatment’ of raw data
different analysis of treated data by software suites (Spotfire, GeneSpring, Partek, GXExplorer, Genesis, Stanford software Xcluster Treeview,…)
Statistical packages : S+, R , SAS

22. Methodology
Employ common formats for exporting or extracting gene expression data / sample data / gene annotation data
MAGE group : a common language to describe and exchange microarray data information, describes :
microarray design
microarray manufacturing information
microarray experiment setup
gene expression data and analysis results

23. Methodology : MAGE
MAGE-ML has been automatically derived from Microarray Gene Expression Object Model (MAGE-OM), which is developed and described using the Unified Modelling Language (UML) – a standard language for describing object models.
MIAME : minimum information about a microarray experiment
Use of ontologies or controlled vocabularies (too many <-> not enough), microarray ontology
Classification : e.g. aging : death to natural causes, obesitas/diabetes example

24. Example sample description
This is an example of the information requested for a sample (biomaterial) description for a microarray experiment (kindly provided by M. Hofmann and S. Schmidtke, LION bioscience AG) with notes added by C. Stoeckert, U. Penn.
Organism: mus musculus [ NCBI taxonomy browser ]
Note that the source of the term "NCBI taxonomy browser" is provided. We will need to come up with a controlled vocabulary of sources and/or IDs.
Cell source: in-house bred mice (contact:
Cell type: thymocytes (complex mixture of cells isolated from thymus. mostly T-lymphocytes and epithelial cells of different differentiation stage) [no matching for this cell type found in GXD "Mouse Anatomical Dictionary"]
Note: what we are going for here is not really the type of cell but rather the type of cell source as in paraffin sample, biopsy, etc. This will be clarified in the concepts.
Sex: female [ MGED ]
Age: weeks after birth [ MGED ]
Growth conditions: normal
Note the need for further structuring of this type of information.
controlled environment oC average temperature housed in cages according to German and EU legislation specified pathogen free conditions (SPF) 14 hours light cycle 10 hours dark cycle

25. Example sample description
Developmental stage: stage 28 (juvenile (young) mice) [ GXD "Mouse Anatomical Dictionary" ]
Organism part: thymus [ GXD "Mouse Anatomical Dictionary" ]
Strain or line: C57BL/6 [International Committee on Standardized Genetic Nomenclature for Mice]
Genetic Variation: Inbr (J) 150. Origin: substrains 6 and 10 were separated prior to This substrain is now probably the most widely used of all inbred strains. Substrain 6 and 10 differ at the H9, Igh2 and Lv loci. Maint. by J,N, Ola. [International Committee on Standardized Genetic Nomenclature for Mice ]
Individual: -
Individual genetic characteristics: the same as in "Genetic Variation"
Disease state: normal
Targeted cell type: primary thymocytes (complex mixture of cells isolated from thymus. mostly T-lymphocytes and epithelial cells of different differentiation stage [ no matching for this cell type found in GXD "Mouse Anatomical Dictionary"]
Cell line: no cell line; primary cells
Treatment: in vivo [MGED] intraperitoneal injection of Dexamethasone into mice, 10 microgram per 25 g bodyweight of the mouse
Compound: drug [MGED] synthetic glucocorticoid Dexamethasone, dissolved in PBS
Clinical Information: -
Separation technique: mice were killed 4 hours post injection and the thymus was taken out. Thymozytes were prepared by meshing the thymus through sterile gauze several times until a suspension of individual cells was obtained.
RNA was prepared from thymozytes using a Qiagen RNA preparation kit.

26. Data analysis in warehouse environment
Goal : focus rapidly on data set of interest (single experiment, aggregate of several experiments/replicates,…)
Mechanisms: free text search, key word search,
Exploration :
identify data of biological interest
clustering, classification
Consistent analysis and exploration
Data analysis evolves and is of secondary importance to storing data.

27. Outlook
Data management technology will provide continuously enhanced tools
Complexity will rise as new types of data generated appear
Expertise needed in application ;
image analysis
data modelling
data analysis : classification/statistics
and data management and software
DBA, software engineering, software QA + prod.

28. Part II : existing microarray data repositories
NCBI Geo :
different types of expression data
Submitter
Platform
Sample
Series

29. Existing microarray data repositories
ArrayDB
NCGR’s GeneX
Stanford Microarray Database (SMD) (see later)
They all have
clone viewers
histogram
scatter plots
analysis software as plug-ins

30. Attached to other data repositories
Attached to biological pathway databases
KEGG
GenMAPP

31. Case study Arabidopsis microarray data
AFGC : Arabidopsis functional genomics consortium
online demonstration
Describing goals of MA data repository :
use cases scenario

32. Usecases for an expression database
Remember : these are all functions to be provided by the MA database and interfaces ! (complex queries with DBMS could be simple for individual analysis).
General functionalities
1. Retrieve/download all the genes that have been arrayed.
2. Retrieve/download the genes or array elements that have been arrayed using a specific technology or manufacturer or version
3. Return array elements/genes by gene name, locus name, nucleotide accession, Protein accession, and sequence (nucleotide and aa).
4. Download the raw( or processed) data for one ExprHybridization.
5. Download/retrieve the summary data for one ExpressionSet.
6. Browse ExpressionSets (experiments).

33. Usecases for an expression database
Simple Queries
7. Select an ExprHybridization and retrieve processed data for a gene by name, accession or sequence
8. Select an ExpressionSet and retrieve summary data for a gene by name, accession or sequence.
9. Select an experiment (ExpressionSet) and retrieve the genes that were up-/down-regulated, or varied X-fold (up or down), or uncertain, etc.
10. Retrieve experiments (ExpressionSets) by researcher's name, keyword, germplasm, classification, experiment type (time course, treated/untreated, dose response),and/or treatment (or only experimental variable treatment)
11. Retrieve detailed information about an experiment.

34. Usecases for an expression database
More complex queries
12. Retrieve all the experiments in which gene X was up/down/expressed/etc.
13. Retrieve only the experiments that satisfy a given condition/s in which gene X was up/down/expressed/etc
14. Retrieve summary data for Gene X in all the experiments that satisfy a set of experimental conditions (germplasm, treatment, anatomical part, etc).
15. A user is browsing through experiments and wants a list of genes that are distinctive for a pairwise comparison, e.g. a search for auxin–upregulated or –downregulated genes.
Advanced queries
16. Taking different samples and different genes, can the genes be divided in certain clusters.
17. Given the expression values of gene X in an experiment, return other genes with similar expression values in the same experiment or across experiments

35. Data model of Arabidopsis MA repository

36. Practical implementation of MA repository
DBMS : Sybase
Mixture of build/buy
transact SQL ( Oracle’s procedural SQL)
analysis Spotfire /GeneSpring
Building own analysis software in Perl, Python, C
Compliant with MAGE standards / MAML