Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of h

Background: A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions. Recently, several methods for the identification of genes with bimodal distributions have been introduced. A straightforward approach is to cluster the expression values and score the distance between the two distributions. Other scores directly measure properties of the distribution. The kurtosis, e.g., measures divergence from a normal distribution. An alternative is the outlier-sum statistic that identifies genes with extremely high or low expression values in a subset of the samples. Results: We compare and discuss scores for bimodality for expression data. For the genome-wide id...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Birte Hellwig, Jan G. Hengstler, Marcus Schmidt, Mathias C. Gehrmann, Wiebke Schormann, Jörg Rahnenführer
• Keywords: Bimodal Genes, BMCBI 2010, Low Expression, Patient Subgroups