Extended analysis of benchmark datasets for Agilent two-color microarrays. Export

@article{citeulike:2073154, abstract = {ABSTRACT: BACKGROUND: As part of its broad and ambitious mission, the MicroArray Quality Control (MAQC) project reported the results of experiments using External RNA Controls (ERCs) on five microarray platforms. For most platforms, several different methods of data processing were considered. However, there was no similar consideration of different methods for processing the data from the Agilent two-color platform. While this omission is understandable given the scale of the project, it can create the false impression that there is consensus about the best way to process Agilent two-color data. It is also important to consider whether ERCs are representative of all the probes on a microarray. RESULTS: A comparison of different methods of processing Agilent two-color data shows substantial differences among methods for low-intensity genes. The sensitivity and specificity for detecting differentially expressed genes varies substantially for different methods. Analysis also reveals that the ERCs in the MAQC data only span the upper half of the intensity range, and therefore cannot be representative of all genes on the microarray. CONCLUSIONS: Although ERCs demonstrate good agreement between observed and expected log-ratios on the Agilent two-color platform, such an analysis is incomplete. Simple loess normalization outperformed data processing with Agilent's Feature Extraction software for accurate identification of differentially expressed genes. Results from studies using ERCs should not be over-generalized when ERCs are not representative of all probes on a microarray.}, author = {Kerr, Kathleen F.}, citeulike-article-id = {2073154}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-8-371}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17915030}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17915030}, day = {3}, doi = {10.1186/1471-2105-8-371}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {microarray, normalization}, month = {October}, number = {1}, posted-at = {2008-12-24 09:07:46}, priority = {0}, title = {Extended analysis of benchmark datasets for Agilent two-color microarrays.}, url = {http://dx.doi.org/10.1186/1471-2105-8-371}, volume = {8}, year = {2007} }

TY - JOUR ID - citeulike:2073154 L3 - citeulike-article-id:2073154 N2 - ABSTRACT: BACKGROUND: As part of its broad and ambitious mission, the MicroArray Quality Control (MAQC) project reported the results of experiments using External RNA Controls (ERCs) on five microarray platforms. For most platforms, several different methods of data processing were considered. However, there was no similar consideration of different methods for processing the data from the Agilent two-color platform. While this omission is understandable given the scale of the project, it can create the false impression that there is consensus about the best way to process Agilent two-color data. It is also important to consider whether ERCs are representative of all the probes on a microarray. RESULTS: A comparison of different methods of processing Agilent two-color data shows substantial differences among methods for low-intensity genes. The sensitivity and specificity for detecting differentially expressed genes varies substantially for different methods. Analysis also reveals that the ERCs in the MAQC data only span the upper half of the intensity range, and therefore cannot be representative of all genes on the microarray. CONCLUSIONS: Although ERCs demonstrate good agreement between observed and expected log-ratios on the Agilent two-color platform, such an analysis is incomplete. Simple loess normalization outperformed data processing with Agilent's Feature Extraction software for accurate identification of differentially expressed genes. Results from studies using ERCs should not be over-generalized when ERCs are not representative of all probes on a microarray. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - Extended analysis of benchmark datasets for Agilent two-color microarrays. VL - 8 KW - microarray KW - normalization AU - Kerr, Kathleen PY - 2007/10/03/ UR - http://dx.doi.org/10.1186/1471-2105-8-371 ER -