Showing papers by "Dennis P. Wall published in 2009"

Reply to the “Letter to the Editors” by Steven Buyske

Abstract: Neurogenetics (2009) 10:169–170 DOI 10.1007/s10048-009-0180-6 LETTER TO THE EDITORS Reply to the “Letter to the Editors” by Steven Buyske K. Abu-Elneel & T. Liu & F. S. Gazzaniga & Y. Nishimura & D. P. Wall & D. H. Geschwind & K. Lao & K. S. Kosik Received: 26 January 2009 / Accepted: 26 January 2009 / Published online: 24 February 2009 # The Author(s) 2009. This article is published with open access at Springerlink.com Dear Editors: We appreciate the opportunity to clarify the statistical treatment of our data. During the preparation of the manuscript, we did consider the use of the t-test as proposed by Dr. Buyske. Indeed, the z-test requires that the standard deviation be known, while we estimated the standard deviation from the sample. However, the t-test is not ideal either. A t-test compares two groups of values and the statistics rely on the standard deviation of both groups. It is unconventional to use the t-test to compare a single value, i.e., that of the miRNA in each case of autism against a group of values. By definition, the single values do not K. Abu-Elneel : T. Liu : F. S. Gazzaniga : K. S. Kosik (*) Neuroscience Research Institute, Department of Molecular Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA 93106, USA e-mail: kosik@lifesci.ucsb.edu Y. Nishimura : D. H. Geschwind Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095-1769, USA D. P. Wall The Center for Biomedical Informatics & Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA K. Lao Applied Biosystems, 850 Lincoln Centre Dr., Foster City, CA 94404, USA Present Address: F. S. Gazzaniga Biomedical Sciences, University of California San Francisco, San Francisco, CA 94143, USA have a standard deviation. For this reason, we chose the z- test as a first-level less stringent screen to assess differential expression between one autistic case and 13 normal cases for each miRNA. We had also addressed the issue raised by Dr. Buyske with a nonparametric approach—the Wilcoxon rank test. However, the statistical power of the rank order test is rather weak and, more importantly, we lose the information in the differences in magnitude of the miRNAs. A strength of the method we used to profile the miRNAs—real-time PCR—is its ability to detect a broad dynamic range of miRNA expression. Our method was preferable to capture this quantitative parameter. However, the miRNAs with p- values that rank among the top using the parametric test remain among the top with the nonparametric test. The statistical testing as suggested by Dr. Buyske does demonstrate some differentially expressed miRNAs. With a one-sided t-test, our top five miRNAs remained statistically significant. The main difference between the z-test and t- test in a sense is the p-value cutoff. The z-test is indeed less stringent than the t-test and the two-sided t-test further increases the stringency. On the other hand, the Bonferroni correction for multiple hypotheses testing, applied in the manuscript, is often considered overly stringent. If we control for multiple hypotheses with a 5% false discovery rate (FDR) rather than the Bonferroni correction on t- statistics, the number of dysregulated miRNAs goes from five to 13. One could further argue that even the FDR is overly stringent because many miRNAs are co-regulated, and therefore treating each as an independent query may be unnecessarily conservative. Whichever the test and correction, we did provide all p- values signifying the difference between each autistic case and the normal cases for each miRNA, and these p-values are not affected by the significance cutoff. These p-values