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A new method for class prediction based on signed-rank algorithms applied to Affymetrix ? microarray experiments

机译:一种新的基于符号秩算法的班级预测方法,应用于Affymetrix?微阵列实验

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Background The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix? technology provides both a quantitative fluorescence signal and a decision ( detection call : absent or present) based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM) patients. Results After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i) determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii) predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii) predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM). Conclusion This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with clinical groups, and looks particularly promising through international cooperative projects like the "Microarray Quality Control project of US FDA" MAQC as a predictive classifier for diagnostic, prognostic and response to treatment. Finally, it can be used as a powerful tool to mine published data generated on Affymetrix systems and more generally classify samples with binary feature values.
机译:背景技术DNA芯片产生的大量数据是分类各种病理的强大基础。然而,微阵列技术的不断发展使其难以混合来自不同芯片类型的数据来进行有限样本群体的分类预测。 Affymetrix ?技术基于应用于每个基因的多个杂交重复的带符号秩算法,提供了定量荧光信号和决策(检测调用:不存在或存在),并按芯片进行归一化。我们仅基于最近Affymetrix芯片类型的检测调用,开发了一种新的类别归属预测方法。通过在纯化的正常B细胞​​和三个独立的多发性骨髓瘤(MM)患者组的细胞的U133A,U133B和U133Plus 2.0微阵列上杂交获得生物学数据。结果经过基于呼叫的数据缩减步骤以过滤掉非类别区分的探针集后,通过迭代删除探针集可顺序校正类别间的比较及其意义,将获得的基因列表简化为预测子,并通过多次测试进行校正。使用留一法和5倍交叉验证法确定方法的错误率。它已成功应用于(i)正常供体组确定性别预测因子,除具有Y染色体缺失的男性MM样品外,所有患者组中的性别均无错误;(ii)预测由免疫组蛋白表达的免疫球蛋白轻链和重链验证组的恶性骨髓瘤克隆,并且(iii)预测每位新患者的性别,轻链和重链性质。最后,与流行的分类方法微阵列预测分析(PAM)相比,该方法显示出了强大的功能。结论这种无归一化方法通常用于质量控制和纠正患者报告给临床医生的错误。它可以很容易地扩展到适合临床人群的多类别预测,并且通过国际合作项目(例如“美国FDA的微阵列质量控制项目” MAQC)作为诊断,预后和对治疗的预测分类器,看起来特别有希望。最后,它可以用作挖掘Affymetrix系统上生成的已发布数据的功能强大的工具,并且更一般地使用二进制特征值对样本进行分类。

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