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Identification of differentially expressed genes and false discovery rate in microarray studies
Arief Gusnanto1, Stefano Calza, Yudi Pawitan
1Medical Research Council - Biostatistics Unit, Institute of Public Health, Cambridge, UK. Arief.Gusnanto@mrc.cam.ac.uk
Current Opinion in Lipidology
|March 14, 2007
Summary
The false discovery rate (FDR) is now the standard for analyzing microarray data, replacing P values for identifying differentially expressed genes. Further research is needed in data preprocessing and sensitive testing methods.
Area of Science:
- Bioinformatics
- Statistical Genetics
- Genomics
Background:
- Microarray technology enables high-throughput gene expression analysis.
- Identifying differentially expressed genes presents statistical challenges due to multiplicity and sensitivity issues.
Purpose of the Study:
- To review advancements in microarray data analysis.
- To emphasize the role of false discovery rate (FDR) control in identifying differentially expressed genes.
Main Methods:
- Focus on statistical methodologies for analyzing high-throughput data.
- Review of the false discovery rate (FDR) concept and its application.
- Discussion of methods to enhance FDR control using additional microarray information.
Main Results:
- The P value is often inadequate for analyzing large-scale hypothesis testing in microarrays.
- The false discovery rate (FDR) is emerging as a more appropriate statistical tool.
- FDR control offers a more sensible approach for interpreting microarray data.
Conclusions:
- Growing agreement exists on using the FDR framework for microarray analysis.
- Further research is required for optimizing data preprocessing steps like normalization and filtering.
- Developing more sensitive testing procedures remains an active area of investigation.
