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Analysis of Microarray Data

Data Generated From Both mRNA
and aRNA Samples


Clustering analysis is commonly used for interpreting microarray data. It provides both a visual representation of complex data and a method for measuring similarity between experiments (gene ratios). The widely used methods for clustering microarray data are: Hierarchical, K-means and Self-organizing map. In this article, the second in our series on Ambion's MessageAmp aRNA Amplification Kit, we present data and statistical analyses from experiments conducted by Drs. Philip Moos and Brian Dalley at the University of Utah, Huntsman Cancer Institute (HCI). The microarrays used in this study were manufactured at the HCI and contained 6912 cDNA clones deposited in duplicate using a Molecular Dynamics GEN III Array Spotter. Moos and Dalley compared the data generated from the HCI microarrays hybridized with mRNA and amplified antisense RNA (aRNA) generated with the MessageAmp Kit.


Experimental Design
Total RNA was isolated from HCT116 and RKO cells and aRNA was amplified from total RNA (2 g). For non-amplified samples, mRNA was purified using Oligotex beads (Qiagen). To assess the reproducibility of RNA amplification and compare representation of messages in aRNA with those in mRNA, three samples each of RKO and HCT116 RNA were amplified independently. The quality and yields of RNA obtained from the various samples are presented in Figure 2. mRNA or aRNA (2 g) samples were fluorescently labeled by incorporating Cy3-dCTP (RKO samples) or Cy5-dCTP (HCT 116 samples) during reverse transcription with random 9-mers. Each glass slide contained duplicate arrays and each labeled RNA sample was hybridized to two slides (4 replicates). Duplicate hybridizations were pe
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