MOTIVATION: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. RESULTS: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to < 70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT-PCR (qRT-PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. AVAILABILITY: The data presented can be viewed at http://www.ncbi.nlm.nih.gov/geo/ under GEO accession no. GSE3017.
MOTIVATION: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. RESULTS: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to < 70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT-PCR (qRT-PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. AVAILABILITY: The data presented can be viewed at http://www.ncbi.nlm.nih.gov/geo/ under GEO accession no. GSE3017.
Authors: Kazuhiro Ohtsu; Marianne B Smith; Scott J Emrich; Lisa A Borsuk; Ruilian Zhou; Tianle Chen; Xiaolan Zhang; Marja C P Timmermans; Jon Beck; Brent Buckner; Diane Janick-Buckner; Dan Nettleton; Michael J Scanlon; Patrick S Schnable Journal: Plant J Date: 2007-08-23 Impact factor: 6.417