Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles

Abstract

BackgroundIdentification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers.Methodology/Principal FindingsIn a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as ‘brain group’ and ‘non-brain group’; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes.Conclusions/SignificanceThe methodology employed here may be used to facilitate disease-specific biomarker discovery.

Dynamic gene expression patterns in animal models of early and late heart failure reveal biphasic-bidirectional transcriptional activation of signaling pathways

“…A surprising and striking observation from our time-course data was that many of the major signaling pathways (e.g., MAPK, calcium, and p53 signaling, as well as apoptosis) altered in late-stage HF displayed a biphasic course with changes in the completely opposite direction earlier in the course of disease. Results obtained from human and mouse data affirm that genes with a switch-like or bimodal expression play a large role in cell communication and are enriched for functions in extracellular and intercellular signal transduction (9,13). To our knowledge, extensive bidirectional transcriptional regulation in HF has not been previously appreciated.…”

MMP1 bimodal expression and differential response to inflammatory mediators is linked to promoter polymorphisms

“…This study suggests a complementary strategy, whereby we first identified those transcripts that showed bimodal expression levels, and then identified the polymorphism responsible for this differential expression. Clustering methods [ 6 ] and statistical methods [ 7 , 10 , 33 ] have previously been used to identify bimodal expression in large datasets. These methods have the advantage of being motivated by strong theoretical statistical considerations.…”

“…These methods have the advantage of being motivated by strong theoretical statistical considerations. However, they also require a moderate level of statistical understanding, and in addition some of these methods can only be easily applied to large-scale meta-analysis of several data sets [ 10 ] and may be less suitable for small expression data sets generated in a single laboratory. The approach we suggest is able to be used alongside more complex approaches by laboratory scientists with only basic statistical training, who are in a good position to immediately follow up their results experimentally.…”

“…To this end, statistical approaches using mixture-model based clustering combined with either Akaike information criterion (AIC) or the Bayesian informatics criterion (BIC) have frequently been applied [ 6 - 9 ]. One method based on systematic classification of gene expression profiles has been applied to over 2,000 microarray samples [ 10 ]. These methods have a strong theoretical base and have proven successful in identifying bimodality.…”