ZODET: software for the identification, analysis and visualisation of outlier genes in microarray expression data.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Complex human diseases exhibit significant patient heterogeneity.
• Identifying gene transcription variants of biological and phenotypic importance is crucial.
• Existing methods may not fully capture individual gene expression abnormalities.

Purpose of the Study:

• To develop and present a novel graphical software package, Z-score Outlier Detection (ZODET).
• To enable the identification and visualization of significant gene expression outliers in individuals using whole genome microarray data.
• To assess the utility of ZODET in detecting pathogenic variants in complex diseases.

Main Methods:

• ZODET utilizes a statistical approach based on mean and standard deviation from a healthy control cohort.
• It detects both over-expressed and under-expressed gene probes in individual subjects.
• Comparison with the Gene Tissue Index (GTI) method using simulated and real microarray datasets.

Main Results:

• ZODET effectively identifies outlier genes in gene expression datasets.
• The software demonstrated comparable or superior performance to existing methods like GTI.
• The approach is robust for analyzing both simulated and real-world microarray data.

Conclusions:

• ZODET offers a novel and effective approach for identifying outlier genes with potential pathogenic relevance in complex human diseases.
• The software facilitates the visualization of gene expression abnormalities, aiding in disease research.
• ZODET is implemented using R packages and Java and is freely available.