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.

Biochemical biomarkers in samples processed by different methods in Environmental Specimen Banks

In the last decades the creation of new Environmental Specimen Banks (ESB) is increasing due to the necessity of knowing the effects of pollutants in both the environment and human populations. ESBs analyze and store samples in order to understand the effects of chemicals, emerging substances and the environmental changes in biota. For a correct analysis of the effect induced by these variables, there is a need to add biological endpoints, such as biomarkers, to the endpoints based on chemical approaches which have being used until now. It is essential to adapt ESB´s sampling strategies in order to enable scientists to apply new biological methods. The present study was performed to obtain biochemical endpoints from samples stored in the BBEBB (Biscay Bay Environmental Biospecimen Bank) of the Marine Station of Plentzia (PIE - UPV/EHU). The main objective of the present work was to study the variability caused in biochemical biomarkers by different processing methods in mussels (Mytilus galloprovincialis) from two localities (Plentzia and Arriluze) with different pollution history. It can be concluded that the selected biomarkers (glutathione S-transferase and acetylcholinesterase) can be accurately measured in samples stored for years in the ESBs. The results also allowed the discrimination of both sampling sites. However, in a further step, the threshold levels and baseline values should be characterized for a correct interpretation of the results in relation to the assessment of the ecosystem health status.