RH generated and analysed microarray data and performed GO analyses.
Auxin-responsive genes from A. thaliana and O. sativa were identified by analysing the microarray data from auxin treatment experiments.
However, by analysing the microarray data without correcting for multiple comparisons we were able to identify by Partek and Ingenuity software and to verify by qRT-PCR and ELISA a number of other gene changes between sexes.
By analysing the microarray data, more than 5,000 genes, which had statistically significant changes in their expression, were identified in the WT plants under drought versus WT plants under irrigation (WTd-WTw), T2 plants under drought versus T2 plants under irrigation (T2d-T2w), and T2 plants under drought versus WT plants under drought (T2d-WTd) comparisons.
DH and DT analysed microarray data and selected candidate genes.
We approached this problem by analysing global, temporal microarray data from ex vivo CD3+, CD4+ and CD8+ T-cell cultures, and leveraging Gene Ontology associations and prior knowledge.
While one would expect that genes involved in male gametogenesis would exhibit differential expression in male-sterile and male-fertile individuals, and that this difference would be detectable by analysing the microarray expression data, it is possible that the difference may be statistically insignificant owing to the limitations of the methodology.
AEL constructed the microarray, performed microarray experiments and analysed the microarray data, assisted by CSJ, and analysed real-time PCR data and wrote the manuscript.
For example, Hvidsten et al. (2003) proposed a systematic supervised learning model to predicting bioprocess by analysing microarray data.
Additional information on the microarray experiments and the statistical methods used to analyse the microarray data is provided in the Additional files 1, 2 and 3. Raw microarray data of the entire data set is available in GEO (GSE54514).
RTT statistically analysed the microarray data.
