GeWare: A data warehouse for gene expression analysis

Transkript

1 GeWare: A data warehouse for gene expression analysis T. Kirsten, H.-H. Do, E. Rahm WG 1, IZBI, University of Leipzig dbs.uni-leipzig.de

2 Outline Motivation GeWare Architecture Annotation Integration Expression Analysis Conclusion and Future Work 2

3 Motivation Microarrays to measure expression of thousands of genes at the same time Various kinds of data with different characteristics and requirements Central data management and analysis platform Data Warehouse approach Expression data import, e.g. from Affymetrix system Fact tables to store both raw and derived data Uniform specification of experiment annotations Integration of gene annotations from public sources Integration of analysis and data mining algorithms/tools 3

4 System Architecture Sources Data Warehouse Data Analysis Experiments Flat Files & MicroDB RDBMS Probe and gene Intensities intensities File-based exchange Data ex-/import to/from tools Uniform web-based GUI Descriptive statistics Canned / Ad-hoc queries (Data mining, OLAP) MIAME Submission Website Manual User Input Sample & Experiment annotations Multidimensional data model Transparent integration (Data access using database API) Public Data Sources LocusLink GO RDBMS UniGene GenMapper Gene annotations Data Integration Tight integration (Direct operation on database Tool Integration 4

5 System Workflows Import workflow Expression analysis workflow Experiment creation / selection Browse / search in annotations Import of raw data (CEL files) Import of preprocessed data (MicroDB or others) Experiment annotation Gene groups Management of analysis objects Experiment groups Gene expression matrices Preprocessing (Normalization/aggregation) Expression analysis Visualization Internal / integrated analysis Statistics / reporting Export OLAP External analysis (Functional profiling, clustering) 5

6 Experiment Annotation Uniform and consistent annotation Controlled annotation vocabularies Annotation templates as collections of annotation categories for which the annotation values has to be captured Hierarchical arrangement of categories Definition of MIAME compliant templates by biologists MAGE-ML export (data exchange) 6

7 Experiment and Gene Groups Collections of objects with common patterns Input for reporting and further analysis Definition by User selection Search in annotation Result of reporting and advanced analysis Example: p53 tumor group 7

8 Preprocessing Preprocessing Generation of expression values on gene level Several predefined algorithms (Mas5, Rma, Li/Wong) P. by user defined methods for normalization, etc. 8

9 Expression Analysis Several statistical reports used for analysis entry and outlier detection Descriptive statistics (mean, sd, se ) Gene/experiment correlation Concentration ratios Advanced analysis Multiple Resampling Procedures: Analysis and test of gene groups Multivariate tests on probe basis 9

10 Visualization Different kinds of visualization of gene expression values (line, bar charts, heat maps) 10

11 Conclusions / Future Work GeWare Management of a high volume of expression data Flexible and uniform experiment annotation Storing experiment and gene groups, expression matrices Different kinds of analysis, export Web-Interface: Future work Coupling with advanced analysis/ data mining routines Visualization extension 11