MOTIVATION: Many stimuli to biological systems result in transcriptional responses that vary across the individual organism either in type or in timing. This creates substantial difficulties in detecting these responses. This is especially the case when the data for any one individual are limited and when the number of genes, probes or probe sets is large. RESULTS: We have developed a procedure that allows for sensitive detection of transcriptional responses that differ between individuals in type or in timing. This consists of four steps: one is to identify a group of genes, probes or probe sets that detect genes that belong to a molecular class or to a common pathway. The second is to conduct a statistical test of the hypothesis that the gene is differentially expressed for each individual and for each gene in the set. The third is to examine the collection of these statistics to see if there is a detectable signal in the aggregate of them. The final step is to assess the significance of this by resampling to avoid correlational bias. AVAILABILITY: Software in the form of R code to perform the required test is available from the first author or from his website http://www.idav.ucdavis.edu/~dmrocke/software; however the procedures are also easily performed using any standard statistical software.
MOTIVATION: Many stimuli to biological systems result in transcriptional responses that vary across the individual organism either in type or in timing. This creates substantial difficulties in detecting these responses. This is especially the case when the data for any one individual are limited and when the number of genes, probes or probe sets is large. RESULTS: We have developed a procedure that allows for sensitive detection of transcriptional responses that differ between individuals in type or in timing. This consists of four steps: one is to identify a group of genes, probes or probe sets that detect genes that belong to a molecular class or to a common pathway. The second is to conduct a statistical test of the hypothesis that the gene is differentially expressed for each individual and for each gene in the set. The third is to examine the collection of these statistics to see if there is a detectable signal in the aggregate of them. The final step is to assess the significance of this by resampling to avoid correlational bias. AVAILABILITY: Software in the form of R code to perform the required test is available from the first author or from his website http://www.idav.ucdavis.edu/~dmrocke/software; however the procedures are also easily performed using any standard statistical software.
Authors: Sunirmal Paul; Christopher A Barker; Helen C Turner; Amanda McLane; Suzanne L Wolden; Sally A Amundson Journal: Radiat Res Date: 2011-01-10 Impact factor: 2.841
Authors: Susanne R Berglund; David M Rocke; Jian Dai; Chad W Schwietert; Alison Santana; Robin L Stern; Joerg Lehmann; Christine L Hartmann Siantar; Zelanna Goldberg Journal: Int J Radiat Oncol Biol Phys Date: 2007-11-08 Impact factor: 7.038