Data Analysis

The RH Microarray Center is the chosen core facility for many national and international Institues and Centers and quality control (QC) and standard operating procedures (SOP) play an important part of the labeling and analysis process. For more information, go to the QC/SOP page.

There are several methods for the processing and analysis of the raw data obtained by microarray experiments. The data analysis can be subdivided into two distinct and equally important steps, namely low-level and high-level analysis. The low-level analysis consist of preprocessing, background evaluation and normalization, with the aim of detecting and removing systemic and technical variation and hence enable the direct comparison of microarray samples, which is carried out as part of the high-level analysis. Low-level analysis is completed by transforming the normalized intensity measures into one expression value/index per gene or transcript.

The high-level analysis deals with the identification of differentially expressed genes and expression patterns of distinct functional groups, as well of the developement of classification and prediction models for prognostic analysis. Much attention is futhermore directed to the process of annotating and categorizing gene function by the gene ontology.