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

ABSTRACT:

Summary

Complex human diseases can show significant heterogeneity between patients with the same phenotypic disorder. An outlier detection strategy was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance. Here we describe a graphical software package (z-score outlier detection (ZODET)) that enables identification and visualisation of gross abnormalities in gene expression (outliers) in individuals, using whole genome microarray data. Mean and standard deviation of expression in a healthy control cohort is used to detect both over and under-expressed probes in individual test subjects. We compared the potential of ZODET to detect outlier genes in gene expression datasets with a previously described statistical

SUBMITTER: Roden DL 

PROVIDER: S-EPMC3885386 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature