André Fujita, J. Sato, Fernando H L DA Silva, Maria C Galvão, M. Sogayar, S. Miyano
{"title":"Quality control and reproducibility in DNA microarray experiments.","authors":"André Fujita, J. Sato, Fernando H L DA Silva, Maria C Galvão, M. Sogayar, S. Miyano","doi":"10.1142/9781848165632_0003","DOIUrl":null,"url":null,"abstract":"Biological experiments are usually set up in technical replicates (duplicates or triplicates) in order to ensure reproducibility and, to assess any significant error introduced during the experimental process. The first step in biological data analysis is to check the technical replicates and to confirm that the error of measure is small enough to be of no concern. However, little attention has been paid to this part of analysis. Here, we propose a general process to estimate the error of measure and consequently, to provide an interpretable and objective way to ensure the technical replicates' quality. Particularly, we illustrate our application in a DNA microarray dataset set up in technical duplicates.","PeriodicalId":73143,"journal":{"name":"Genome informatics. International Conference on Genome Informatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome informatics. International Conference on Genome Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/9781848165632_0003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Biological experiments are usually set up in technical replicates (duplicates or triplicates) in order to ensure reproducibility and, to assess any significant error introduced during the experimental process. The first step in biological data analysis is to check the technical replicates and to confirm that the error of measure is small enough to be of no concern. However, little attention has been paid to this part of analysis. Here, we propose a general process to estimate the error of measure and consequently, to provide an interpretable and objective way to ensure the technical replicates' quality. Particularly, we illustrate our application in a DNA microarray dataset set up in technical duplicates.