Semiparametric approach to characterize unique gene expression trajectories across time

Sandra L Rodriguez-Zas¹^,2^,3 , Bruce R Southey¹^,4^,5 , Charles W Whitfield³^,6 and Gene E Robinson³^,6

¹Department of Animal Sciences, University of Illinois at Urbana-Champaign, IL 61801, USA

²Department of Statistics, University of Illinois at Urbana-Champaign, IL 61801, USA

³Institute for Genomic Biology, University of Illinois at Urbana-Champaign, IL 61801, USA

⁴Department of Chemistry, University of Illinois at Urbana-Champaign, IL 61801, USA

⁵Department of Computer Science, University of Illinois at Urbana-Champaign, IL 61801, USA

⁶Department of Entomology, University of Illinois at Urbana-Champaign, IL 61801, USA

author email corresponding author email

BMC Genomics 2006, 7:233doi:10.1186/1471-2164-7-233


Published:	13 September 2006

Abstract

Background:

A semiparametric approach was used to identify groups of cDNAs and genes with distinct expression profiles across time and overcome the limitations of clustering to identify groups. The semiparametric approach allows the generalization of mixtures of distributions while making no specific parametric assumptions about the distribution of the hidden heterogeneity of the cDNAs. The semiparametric approach was applied to study gene expression in the brains of Apis mellifera ligustica honey bees raised in two colonies (A. m. mellifera and ligustica) with consistent patterns across five maturation ages.

Results:

The semiparametric approach provided unambiguous criteria to detect groups of genes, trajectories and probability of gene membership to groups. The semiparametric results were cross-validated in both colony data sets. Gene Ontology analysis enhanced by genome annotation helped to confirm the semiparametric results and revealed that most genes with similar or related neurobiological function were assigned to the same group or groups with similar trajectories. Ten groups of genes were identified and nine groups had highly similar trajectories in both data sets. Differences in the trajectory of the reminder group were consistent with reports of accelerated maturation in ligustica colonies compared to mellifera colonies.

Conclusion:

The combination of microarray technology, genomic information and semiparametric analysis provided insights into the genomic plasticity and gene networks linked to behavioral maturation in the honey bee.