MOTIVATION: A protocol is described to attach expression patterns to genes represented in a collection of hybridization array experiments. Discrete values are used to provide an easily interpretable description of differential expression. Binning cutoffs for each sample type are chosen automatically, depending on the desired false-positive rate for the predictions of differential expression. Confidence levels are derived for the statement that changes in observed levels represent true changes in expression. We have a novel method for calculating this confidence, which gives better results than the standard methods. Our method reflects the broader change of focus in the field from studying a few genes with many replicates to studying many (possibly thousands) of genes simultaneously, but with relatively few replicates. Our approach differs from standard methods in that it exploits the fact that there are many genes on the arrays. These are used to estimate for each sample type an appropriate distribution that is employed to control the false-positive rate of the predictions made. Satisfactory results can be obtained using this method with as few as two replicates. RESULTS: The method is illustrated through applications to macroarray and microarray datasets. The first is an erythroid development dataset that we have generated using nylon filter arrays. Clones for genes whose expression is known in these cells were assigned expression patterns which are in accordance with what was expected and which are not picked up by the standards methods. Moreover, genes differentially expressed between normal and leukemic cells were identified. These included genes whose expression was altered upon induction of the leukemic cells to differentiate. The second application is to the microarray data by Alizadeh et al. (2000). Our results are in accordance with their major findings and offer confidence measures for the predictions made. They also provide new insights for further analysis.
MOTIVATION: A protocol is described to attach expression patterns to genes represented in a collection of hybridization array experiments. Discrete values are used to provide an easily interpretable description of differential expression. Binning cutoffs for each sample type are chosen automatically, depending on the desired false-positive rate for the predictions of differential expression. Confidence levels are derived for the statement that changes in observed levels represent true changes in expression. We have a novel method for calculating this confidence, which gives better results than the standard methods. Our method reflects the broader change of focus in the field from studying a few genes with many replicates to studying many (possibly thousands) of genes simultaneously, but with relatively few replicates. Our approach differs from standard methods in that it exploits the fact that there are many genes on the arrays. These are used to estimate for each sample type an appropriate distribution that is employed to control the false-positive rate of the predictions made. Satisfactory results can be obtained using this method with as few as two replicates. RESULTS: The method is illustrated through applications to macroarray and microarray datasets. The first is an erythroid development dataset that we have generated using nylon filter arrays. Clones for genes whose expression is known in these cells were assigned expression patterns which are in accordance with what was expected and which are not picked up by the standards methods. Moreover, genes differentially expressed between normal and leukemic cells were identified. These included genes whose expression was altered upon induction of the leukemic cells to differentiate. The second application is to the microarray data by Alizadeh et al. (2000). Our results are in accordance with their major findings and offer confidence measures for the predictions made. They also provide new insights for further analysis.
Authors: David Finkelstein; Rob Ewing; Jeremy Gollub; Fredrik Sterky; J Michael Cherry; Shauna Somerville Journal: Plant Mol Biol Date: 2002-01 Impact factor: 4.076
Authors: Adel M Talaat; Susan T Howard; Walker Hale; Rick Lyons; Harold Garner; Stephen Albert Johnston Journal: Nucleic Acids Res Date: 2002-10-15 Impact factor: 16.971
Authors: Michael D Hogarty; Murray D Norris; Kimberly Davis; Xueyuan Liu; Nicholas F Evageliou; Candace S Hayes; Bruce Pawel; Rong Guo; Huaqing Zhao; Eric Sekyere; Joanna Keating; Wayne Thomas; Ngan Ching Cheng; Jayne Murray; Janice Smith; Rosemary Sutton; Nicola Venn; Wendy B London; Allen Buxton; Susan K Gilmour; Glenn M Marshall; Michelle Haber Journal: Cancer Res Date: 2008-12-01 Impact factor: 12.701
Authors: Amit Bahl; Brian Brunk; Jonathan Crabtree; Martin J Fraunholz; Bindu Gajria; Gregory R Grant; Hagai Ginsburg; Dinesh Gupta; Jessica C Kissinger; Philip Labo; Li Li; Matthew D Mailman; Arthur J Milgram; David S Pearson; David S Roos; Jonathan Schug; Christian J Stoeckert; Patricia Whetzel Journal: Nucleic Acids Res Date: 2003-01-01 Impact factor: 16.971