Classification of genes and putative biomarker identification using distribution metrics on expression profiles.
BACKGROUND
Identification 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.


RESULTS
In 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
The methodology employed here may be used to facilitate disease-specific biomarker discovery.
Huang
Hung-Chung
HC
Department of Systems Biology and Translational Medicine, Texas A&M Health Science Center College of Medicine, Temple, Texas, United States of America.
Jupiter
Daniel
D
VanBuren
Vincent
V
D000465:Algorithms; D000818:Animals; D015415:Biomarkers / Q000032:analysis*; D016000:Cluster Analysis; D019295:Computational Biology; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D018507:Gene Expression Regulation, Developmental; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease / Q000145:classification / Q000235:genetics; D006321:Heart / Q000196:embryology; D051379:Mice; D008957:Models, Genetic; D009206:Myocardium / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011506:Proteins / Q000145:classification / Q000235:genetics* / Q000378:metabolism; D012984:Software
D015415:Biomarkers; D011506:Proteins