ICGE: an R package for detecting relevant clusters and atypical units in gene expression

Background: Gene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample ... ) belongs to one of these previously identified clusters or to a new group. Results: ICGE is a user-friendly R package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use. Conclusions: We demonstrate the utility of ICGE by analyzing simulated and real data sets. The results show that ICGE could be very useful to a broad research community.

Natural Variation In Recovery From A Short Period Of Anoxia Due To A cGMP-Dependent Protein Kinase in Drosophila melanogaster

[EN] Protein Kinase G (PKG) or cGMP-dependent protein kinases (PKG) have been shown to play an important role in resistance to abiotic stressors such as high temperatures or oxygen deprivation in Drosophila melanogaster. In Drosophila, the foraging gene encodes a PKG; natural variants for this gene exist, which differ in the level of expression of PKG: rovers (forR allele) which express high PKG levels, and sitters (forS allele) which express lower PKG levels. This project explores the differences in recovery from short periods of anoxia between natural variants (focusing on forS2, flies with a sitter gene in a rover background), as well as mutants with insertions in the foraging gene and RNAi recombinants that show a reduced PKG expression. The parameters measured were time to recovery and level of activity after anoxia. The results showed lower activity after anoxia in sitters than in rovers, reflecting a worse recovery from the anoxic coma in flies with lower PKG levels.