Methods and approaches in the analysis of gene expression data.
The application of high-density DNA array technology to monitor gene transcription has been responsible for a real paradigm shift in biology. The majority of research groups now have the ability to measure the expression of a significant proportion of the human genome in a single experiment, resulting in an unprecedented volume of data being made available to the scientific community. As a consequence of this, the storage, analysis and interpretation of this information present a major challenge. In the field of immunology the analysis of gene expression profiles has opened new areas of investigation. The study of cellular responses has revealed that cells respond to an activation signal with waves of co-ordinated gene expression profiles and that the components of these responses are the key to understanding the specific mechanisms which lead to phenotypic differentiation. The discovery of 'cell type specific' gene expression signatures have also helped the interpretation of the mechanisms leading to disease progression. Here we review the principles behind the most commonly used data analysis methods and discuss the approaches that have been employed in immunological research.
Dopazo
J
J
Bioinformatica, Centro Nacional de Investigaciones Oncológicas Carlos III, Ctra. Majadahonda-Pozuelo, Km. 2 Majadahonda 28220, Madrid, Spain.
Zanders
E
E
Dragoni
I
I
Amphlett
G
G
Falciani
F
F
D002965:Classification; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D015894:Genome, Human; D006801:Humans; D007158:Immunologic Techniques; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D015203:Reproducibility of Results