The capacity to detect RNA isoform changes in archival microarray data using SplicerAV allowed us to carry out the first analysis of isoform specific mRNA changes directly associated with cancer survival.
Cheadle, C., Vawter, M. P., Freed, W. J. & Becker, K. G. Analysis of microarray data using Z score transformation.
We first normalize the microarray data using a rank invariant method.
Du, D., Rawat, N., Deng, Z. & Gmitter, F. G. Jr. Construction of citrus gene coexpression networks from microarray data using random matrix theory.
Comander J, Natarajan S, Gimbrone MA, García-Cardeña G. Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation.
Analysis of the microarray data using GeneGo pathway analysis software demonstrated that the chemokines and adhesion signaling pathway was significantly involved in the mechanism of both ECP and TI, suggesting a crucial role for cell adhesion in these therapies.
The locus IDs of induced ESTs in the SSH library were converted into corresponding Probe IDs using http://www.ricearray.org to compare the SSH library data with microarray data using MapMan software.
Next, we systematically analyzed the microarray data using various approaches depicted in Figure 4A.
We also validated our microarray data using information from the literature, by screening databases and by performing RT-PCR for selected genes.
The microarray data using BioMaps are shown in Table S4, Table S5, Table S6, Table S7, Table S8, Table S9, Table S10 and Table S11.
For example, querying whole public microarray data using a single gene identifier is an ambiguous procedure to extract relevant co-regulated genes.
