Three-way analysis of variance (ANOVA) confirmed that the source of variation was not due to different batches of RNA.
Furthermore, the concept of random genetic mutation as the source of variation was conceived at a time in which our understanding of a genome was simplistic.
Since practical success requires the design of robust networks with repeatable, reliable responses understanding the sources of variation is important.
The testers used were all highly related, being B73 types, and therefore the importance of this source of variation is likely less than if unrelated testers were used.
The sources of variation are related to the processes used to manufacture a product and the materials used in the processes.
Measures of orientation with respect to the shoreline direction can thus be made as a tool to assess beach stability versus changeability, once the sources of variation are correctly interpreted.
While the sources of variation are, I mention, fascinating and many—I mention copying errors and recombination, for example, in the production of mutations for the moment, these mechanisms are beside the point.
The sources of variation are many [5], [8] [10].
In this paper, the sources of variation are analyzed by decomposing the variance into parts attributable to specific experimental factors: technical replicates, sites, peptides, transitions within each peptide, and higher-order interaction terms based on carefully built mixed effects models.
Our previous studies using this array platform determined that the sources of variation were similar in hybridization experiments with both Brassica and Arabidopsis species [29], [30], and that verifiable changes in candidate gene expression could be detected in natural B. napus polyploids following infection with Sclerotinia [31].
