Abu-Elneel, K.

Gazzaniga, F. S.

Geschwind, D. H.

Kosik, K. S.

Lao, K.

Liu, T.

Nishimura, Y.

Wall, Dennis Paul Paul

Neurogenetics

Harvard University - DASH 

 Reply to the “Letter to the Editors” by Steven Buyske  (Article begins on next page)The Harvard community has made this article openly available.Please share how this access benefits you. Your story matters.Citation Abu-Elneel, K., T. Liu, F. S. Gazzaniga, Y. Nishimura, D. P.Wall, D. H. Geschwind, K. Lao, and K. S. Kosik. 2009. Reply tothe “Letter to the Editors” by Steven Buyske. Neurogenetics10(2): 169-170.Published Version doi://10.1007/s10048-009-0180-6Accessed February 19, 2015 3:55:12 AM ESTCitable Link http://nrs.harvard.edu/urn-3:HUL.InstRepos:4882820Terms of Use This article was downloaded from Harvard University's DASHrepository, and is made available under the terms and conditionsapplicable to Other Posted Material, as set forth athttp://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAALETTER TO THE EDITORSReply to the “Letter to the Editors” by Steven BuyskeK. Abu-Elneel & T. Liu & F. S. Gazzaniga &Y. Nishimura & D. P. Wall & D. H. Geschwind & K. Lao &K. S. KosikReceived: 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.comDear Editors:We appreciate the opportunity to clarify the statisticaltreatment of our data. During the preparation of themanuscript, we did consider the use of the t-test asproposed by Dr. Buyske. Indeed, the z-test requires thatthe standard deviation be known, while we estimated thestandard deviation from the sample. However, the t-test isnot ideal either. A t-test compares two groups of values andthe statistics rely on the standard deviation of both groups.It is unconventional to use the t-test to compare a singlevalue, i.e., that of the miRNA in each case of autism againsta group of values. By definition, the single values do nothave a standard deviation. For this reason, we chose the z-test as a first-level less stringent screen to assess differentialexpression between one autistic case and 13 normal casesfor each miRNA.We had also addressed the issue raised by Dr. Buyskewith a nonparametric approach—the Wilcoxon rank test.However, the statistical power of the rank order test israther weak and, more importantly, we lose the informationin the differences in magnitude of the miRNAs. A strengthof the method we used to profile the miRNAs—real-timePCR—is its ability to detect a broad dynamic range ofmiRNA expression. Our method was preferable to capturethis quantitative parameter. However, the miRNAs with p-values that rank among the top using the parametric testremain among the top with the nonparametric test.The statistical testing as suggested by Dr. Buyske doesdemonstrate some differentially expressed miRNAs. With aone-sided t-test, our top five miRNAs remained statisticallysignificant. The main difference between the z-test and t-test in a sense is the p-value cutoff. The z-test is indeed lessstringent than the t-test and the two-sided t-test furtherincreases the stringency. On the other hand, the Bonferronicorrection for multiple hypotheses testing, applied in themanuscript, is often considered overly stringent. If wecontrol for multiple hypotheses with a 5% false discoveryrate (FDR) rather than the Bonferroni correction on t-statistics, the number of dysregulated miRNAs goes fromfive to 13. One could further argue that even the FDR isoverly stringent because many miRNAs are co-regulated,and therefore treating each as an independent query may beunnecessarily conservative.Whichever the test and correction, we did provide all p-values signifying the difference between each autistic caseand the normal cases for each miRNA, and these p-valuesare not affected by the significance cutoff. These p-valuesNeurogenetics (2009) 10:169–170DOI 10.1007/s10048-009-0180-6K. Abu-Elneel:T. Liu:F. S. Gazzaniga:K. S. Kosik (*)Neuroscience Research Institute, Department of MolecularCellular and Developmental Biology,University of California Santa Barbara,Santa Barbara, CA 93106, USAe-mail: kosik@lifesci.ucsb.eduY. Nishimura: D. H. GeschwindDepartment of Neurology, David Geffen School of Medicine,University of California at Los Angeles,Los Angeles, CA 90095-1769, USAD. P. WallThe Center for Biomedical Informatics & Department of SystemsBiology, Harvard Medical School,Boston, MA 02115, USAK. LaoApplied Biosystems,850 Lincoln Centre Dr.,Foster City, CA 94404, USAPresent Address:F. S. GazzanigaBiomedical Sciences, University of California San Francisco,San Francisco, CA 94143, USAallow the reader to see all the data and apply their owncutoff as Dr. Buyske has done.We think that our approach, z-test (less stringent than t-test) plus Bonferroni correction (more stringent than FDR),as well as the inclusion of all the raw data in our manuscriptprovides researchers a better opportunity to explore theeffects of miRNAs on autism. Some validation of the resultsappears in our paper. We reported that, among the implicatedmiRNAs, severaltarget genesknowntobeinvolvedinautismare predicted. Furthermore, four of the miRNAs we reported(miR-23a, miR-132, miR-146b, and miR-320) were reportedinasubsequentstudyusinglymphoblastoidcelllinesfromsixsubjects with autism and six controls (Talebizadeh Z et al.,Autism Research 1:240–250, 2008).We thank Dr. Buyske for his comments and theopportunity to better convey the findings.Open Access This article is distributed under the terms of theCreative Commons Attribution Noncommercial License which per-mits any noncommercial use, distribution, and reproduction in anymedium, provided the original author(s) and source are credited.170 Neurogenetics (2009) 10:169–170

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

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

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