MOTIVATION: The defining feature of oligonucleotide expression arrays is the use of several probes to assay each targeted transcript. This is a bonanza for the statistical geneticist, who can create probeset summaries with specific characteristics. There are now several methods available for summarizing probe level data from the popular Affymetrix GeneChips, but it is difficult to identify the best method for a given inquiry. RESULTS: We have developed a graphical tool to evaluate summaries of Affymetrix probe level data. Plots and summary statistics offer a picture of how an expression measure performs in several important areas. This picture facilitates the comparison of competing expression measures and the selection of methods suitable for a specific investigation. The key is a benchmark data set consisting of a dilution study and a spike-in study. Because the truth is known for these data, we can identify statistical features of the data for which the expected outcome is known in advance. Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data.
MOTIVATION: The defining feature of oligonucleotide expression arrays is the use of several probes to assay each targeted transcript. This is a bonanza for the statistical geneticist, who can create probeset summaries with specific characteristics. There are now several methods available for summarizing probe level data from the popular Affymetrix GeneChips, but it is difficult to identify the best method for a given inquiry. RESULTS: We have developed a graphical tool to evaluate summaries of Affymetrix probe level data. Plots and summary statistics offer a picture of how an expression measure performs in several important areas. This picture facilitates the comparison of competing expression measures and the selection of methods suitable for a specific investigation. The key is a benchmark data set consisting of a dilution study and a spike-in study. Because the truth is known for these data, we can identify statistical features of the data for which the expected outcome is known in advance. Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data.
Authors: Jay O Boyle; Zeynep H Gümüs; Ashutosh Kacker; Vishal L Choksi; Jennifer M Bocker; Xi Kathy Zhou; Rhonda K Yantiss; Duncan B Hughes; Baoheng Du; Benjamin L Judson; Kotha Subbaramaiah; Andrew J Dannenberg Journal: Cancer Prev Res (Phila) Date: 2010-02-23
Authors: Kimberly A Hughes; Julien F Ayroles; Melissa M Reedy; Jenny M Drnevich; Kevin C Rowe; Elizabeth A Ruedi; Carla E Cáceres; Ken N Paige Journal: Genetics Date: 2006-04-19 Impact factor: 4.562
Authors: Richard Shippy; Stephanie Fulmer-Smentek; Roderick V Jensen; Wendell D Jones; Paul K Wolber; Charles D Johnson; P Scott Pine; Cecilie Boysen; Xu Guo; Eugene Chudin; Yongming Andrew Sun; James C Willey; Jean Thierry-Mieg; Danielle Thierry-Mieg; Robert A Setterquist; Mike Wilson; Anne Bergstrom Lucas; Natalia Novoradovskaya; Adam Papallo; Yaron Turpaz; Shawn C Baker; Janet A Warrington; Leming Shi; Damir Herman Journal: Nat Biotechnol Date: 2006-09 Impact factor: 54.908
Authors: Shucha Zhang; Cheng Zheng; Ian R Lanza; K Sreekumaran Nair; Daniel Raftery; Olga Vitek Journal: Anal Chem Date: 2009-08-01 Impact factor: 6.986