基于混合样本质控的代谢组学数据校正和整合策略  

Metabolomic data calibration and integration stragety based on pooled quality controls

作　　者：刘佳健[1] 赵晴[1] 郑晓皎[1] 游懿君 陈天璐[1] 

机构地区：[1]上海交通大学附属第六人民医院转化医学中心,上海200233

出　　处：《计算机与应用化学》2016年第8期905-908,共4页Computers and Applied Chemistry

基　　金：国家自然科学基金青年科学基金(31501079);上海市第六人民医院院级科学研究基金(YNLC201421)

摘　　要：代谢组学研究中,高通量数据批次内和批次间存在的非生物误差对分析结果有不可忽视的影响。针对该问题,我们提出了一个七步策略,基于调整后的质控(简称PQCs)信息对样本中的所有代谢物(或变量)进行逐一校正,可同时调整数据集的批内和批间差异。校正后的多个数据集可以直接拼合用于后续分析,有利于样本数量和物质数量的扩充。实现此功能的专用软件具有小巧简洁的图形用户界面,提供足够多的参数和选项以满足各层次用户的多种需求,已成功应用于多项代谢组学研究中,为资源整合提供技术支持。In metabolomics studies, nonbiological error occurred in both within- and between- batch of high-throughput data sets shows inevitable influence to analytical results. Here we developed a seven-step workflow to calibrate every metabolites （or variables） of the samples by pooled quality controls （PQCs） which was able to adjust both within- and between- batch variance of the datasets in one run. Calibrated data sets can be combined directly and be used for subsequent analysis. This workflow provided a new way for the integra- tion of samples and metabolites. A software specific to the proposed workflow and functions has a simple and integrated GUI and pro- vides lots of options and parameters hoping to satisfy users from various levels and fields. It has been applied successfully in numerous metabolomics studies and provided perfect technical support for information integration.

关 键 词：数据校正 数据整合 混合样本 质量控制 

分 类 号：TQ015.9[化学工程] TP391.9[自动化与计算机技术—计算机应用技术]