DL carried out qRT-PCR amplifications and analyses, performed statistical analysis on microarray data, designed functional analyses tools and participated in the draft and edition of the manuscript.
The authors consider the effect of random signal aggregation on correlation and network inference, two of the most popular analyses tools for microarray analyses.
Microarray analyses have become an important tool in animal genomics.
Gene expression and regulation was determined by microarray analyses and further evaluated by bioinformatics tools including pathway and cluster analyses.
Novel genomic tools such as microarray analyses have brought an overview on the instant changes in the expression levels of hundreds, thousands or even more genes and at the same time, providing more information about the organism's response to changes in the environment [ 14, 17, 23– 26].
To investigate possible biological interactions of differently regulated genes, datasets representing genes with altered expression profile derived from microarray analyses were imported into the Ingenuity Pathway Analysis Tool (IPA Tool; Ingenuity®Systems, Redwood City, CA, USA; ).
We like to assess "health" in whole animals rather than in vitro but we use a large range of tools ranging from genetics, to microarray analyses to flow cytometry.
Currently, one major drawback when performing microarray analyses on data derived from dietary intervention studies is the lack of proper statistical tools.
Moreover, microarray analyses revealed that expression of genes involved in these metabolic pathways is an efficient tool for classification of pheochromocytomas in accordance with the predisposition gene mutated.
Analysis of microarray data has become a useful tool for the study of different illnesses including all types of cancer 1– 3. Microarray analyses are carried out, essentially, with the objective to detect variation patterns of genetic expression.
