Factors affecting the accuracy of a class prediction model in gene expression data.
Factors affecting the accuracy of a class prediction model in gene expression data.
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DOI：
10.1186/s12859-015-0610-4
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发表时间：
2015-06-21
影响因子：
3
通讯作者：
Eijkemans MJ
中科院分区：
文献类型：
--
作者：
Novianti PW;Jong VL;Roes KC;Eijkemans MJ
类预测模型已被证明在临床基因表达数据集中具有不同的性能。以前的评估研究，主要是在癌症领域进行的，表明类预测模型的准确性因数据集而异，并取决于分类函数的类型。虽然大量的信息是已知的分类功能的特性，很少有人做，以确定哪些基因表达数据的特性对分类器的性能有影响。本研究旨在根据经验确定影响分类模型预测准确性的数据特征，而不是癌症领域。下载了25项符合预定义入选和排除标准的研究的数据集。九个分类功能被选中，属于以下类别：判别分析或贝叶斯分类器，基于树，正则化和收缩和最近邻方法。因此，使用相同的程序为每个数据集建立了九类预测模型，并通过计算其准确度来评估其性能。记录每个实验的特征（即，观察到的疾病、医学问题、组织/细胞类型和样本大小）以及基因表达数据的特征，即差异表达基因的数量、倍数变化和类内相关性。通过随机效应logistic回归对它们对类预测模型准确性的影响进行统计学评估。差异表达基因的数量和平均倍数变化对分类模型的准确性有显着影响，并分别给出高达72%和57%的预测准确性的个体解释变异。多变量随机效应logistic回归与正向选择产生了上述两个研究因素和类内相关性作为影响分类功能准确性的因素，解释了91.5%的研究间变异。我们评估了研究和数据相关的因素，这些因素可能解释了非癌症数据集中分类函数的不同性能。我们的研究结果表明，差异表达基因的数量，倍数变化，基因表达数据的相关性显着影响类预测模型的准确性。本文的在线版本（doi：10.1186/s12859-015-0610-4）包含补充材料，可供授权用户使用。
Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer. Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation. We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models. The online version of this article (doi:10.1186/s12859-015-0610-4) contains supplementary material, which is available to authorized users.
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影响因子：
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