Abstract
BackgroundMicroarray technology is widely utilized for monitoring the expression changes of thousands of genes simultaneously. However, the requirement of relatively large amount of RNA for labeling and hybridization makes it difficult to perform microarray experiments with limited biological materials, thus leads to the development of many methods for preparing and amplifying mRNA. It is addressed that amplification methods usually bring bias, which may strongly hamper the following interpretation of the results. A big challenge is how to correct for the bias before further analysis.ResultsIn this article, we observed the bias in rice gene expression microarray data generated with the Affymetrix one-cycle, two-cycle RNA labeling protocols, followed by validation with Real Time PCR. Based on these data, we proposed a statistical framework to model the processes of mRNA two-cycle linear amplification, and established a linear model for probe level correction. Maximum Likelihood Estimation (MLE) was applied to perform robust estimation of the Retaining Rate for each probe. After bias correction, some known pre-processing methods, such as PDNN, could be combined to finish preprocessing. Then, we evaluated our model and the results suggest that our model can effectively increase the quality of the microarray raw data: (i) Decrease the Coefficient of Variation for PM intensities of probe sets; (ii) Distinguish the microarray samples of five stages for rice stamen development more clearly; (iii) Improve the correlation coefficients among stamen microarray samples. We also discussed the necessity of model adjustment by comparing with another simple adjustment method.ConclusionWe conclude that the adjustment model is necessary and could effectively increase the quality of estimation for gene expression from the microarray raw data.
Highlights
Microarray technology is widely utilized for monitoring the expression changes of thousands of genes simultaneously
We proposed a statistical framework to model the process of mRNA two-cycle linear amplification, and established a linear model to revise the expression intensity at probe level
Several works have considered the effect of mRNA degradation in microarray during the process of two-cycle linear amplification as well as the bias it caused [12], almost no or less work has been reported to establish adjusting methods to solve this problem
Summary
Microarray technology is widely utilized for monitoring the expression changes of thousands of genes simultaneously. In order to monitor the bias associated with RNA amplification, we generated two types of rice microarray data using both One-Cycle and Two-Cycle Eukaryotic Target Labeling Assay from Affymetrix (Santa Clara, CA, USA). Both showed the same decreasing trend of probe intensity near 3’ end and 5’ end of transcripts (See Figure 1 for more details). The first one is the degradation of transcript due to RNA’s instability, especially in 16 hour’s in vitro transcription (IVT) stage during the amplification process Another reason might be the usage of random primers in the start of the second round amplification, which concludes uncompleted reverse transcription from aRNA to cDNA. It is necessary to revise the microarray data generated with two-cycle RNA amplification before using it to perform further analysis, such as detecting differentially expressed genes, constructing co-expression gene network and so forth
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