Selecting Classification Methods for Small Samples of Next-Generation Sequencing Data.

Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA.

下一代测序已成为基因表达定量分析的一项关键技术。在医学研究中，RNA测序（RNA - seq）数据通常用于识别患者所患的疾病类型。由于RNA - seq数据的离散性质，为微阵列数据开发的现有统计方法不能直接应用于RNA - seq数据。现有统计方法通常用离散分布对RNA - seq数据进行建模，例如泊松分布、负二项分布，或在零点有一个质点且包含泊松分布的混合分布，以进一步适应有过多零点的数据。因此，已经开发出了与上述三种离散分布相对应的分析工具：泊松线性判别分析（PLDA）、负二项线性判别分析（NBLDA）以及零膨胀泊松逻辑判别分析（ZIPLDA）。然而，当应用于一个新的真实数据集时，这些分类的真实分布情况尚不清楚。考虑到计数数据集经常具有过多零点和过度离散的特征，本文将现有分布扩展为在零点有一个质点且包含负二项分布的混合分布，并提出一种零膨胀负二项逻辑判别分析（ZINBLDA）用于分类。更重要的是，我们从模型参数的角度对上述四种分类方法进行了比较，因为了解参数对于为RNA - seq数据选择最优方法是必要的。此外，我们确定在某些情况下上述四种方法可以相互转换。通过模拟研究，我们在多种设置下对这些分类方法的性能进行了比较和评估，并且我们还提出了一个决策树模型，以帮助我们为一个新的RNA - seq数据集选择最优的分类器。两个真实数据集的结果与理论和模拟分析结果相符。本研究中使用的方法在开源的R脚本中实现，其源代码可在https://github.com/FocusPaka/ZINBLDA免费获取。