Of these, elements that are key to functional genomics studies are the protein protein interaction data and meta-expression data based on microarray data analyses.
We thank Dr. Jeff X. Zhou for contributions to the microarray data analyses.
The procedures for microarray data analyses were described previously by Swanson et al. [11].
We next validated the microarray data analyses in an independent experiment.
This analysis method was originally developed for identifying co-expressed gene clusters in many DNA microarray data analyses [33], [34].
Published microarray data analyses usually used the combination of SAM and Limma methods with popular Quantile and Lowess normalizations.
The starting gene lists for these intersections were represented by the reliable signal and not the statistical analysis and reliable signal, as used before in other microarray data analyses from our laboratory [21], [22].
Expression microarray data analyses from Affymetrix arrays generate lists of significantly-changed probe sets, and when one or more of a gene's matching probe sets appear in the list, then the target gene is typically considered to be differentially expressed.
Although the functions of these SCRiPs require further investigation, our gene expression microarray data analyses provide evidence that SCRiPs are responsive to thermal stress, and that different SCRiPs may have acquired distinct functions during their evolution.
KH: Performed the microarray data analyses.
EAM performed statistical and microarray data analyses.
