Poisson factor models with applications to non-normalized microRNA profiling
Poisson factor models with applications to non-normalized microRNA profiling
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DOI：
10.1093/bioinformatics/btt091
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发表时间：
2013-05-01
期刊：
影响因子：
5.8
通讯作者：
Dittmer, Dirk P.
中科院分区：
文献类型：
--
作者：
Lee, Seonjoo;Chugh, Pauline E.;Dittmer, Dirk P.
动机：下一代（NextGen）测序作为转录谱分析的替代方案正变得越来越受欢迎，就像微RNA（miRNA）谱分析和分类的情况一样。miRNA是一类新的分子，其在分化、肿瘤发生或感染中受到调节。我们的主要动机应用是基于宿主miRNA谱的诱导变化来识别不同的病毒感染。由于NextGen测序数据的特殊特征，遇到了统计学挑战：数据是极度偏斜和非负的读段计数;读段总数在需要适当归一化的样品之间变化很大。统计工具开发的微阵列表达数据，如主成分分析，是次优的分析NextGen测序data.Results：我们提出了一个家庭的泊松因子模型，明确考虑到测序数据的计数性质，并自动纳入通过使用偏移样品归一化。我们开发了一个有效的算法来估计泊松因子模型，题为泊松奇异值分解偏移（PSVDOS）。仿真研究表明，该方法优于其他几种归一化和降维方法。通过对一个miRNA谱分析实验的分析，我们进一步说明了我们的模型实现了对18个样本的miRNA谱的有见地的降维：提取的因子导致细胞系的更准确和有意义的聚类。
Motivation: Next-generation (NextGen) sequencing is becoming increasingly popular as an alternative for transcriptional profiling, as is the case for micro RNAs (miRNA) profiling and classification. miRNAs are a new class of molecules that are regulated in response to differentiation, tumorigenesis or infection. Our primary motivating application is to identify different viral infections based on the induced change in the host miRNA profile. Statistical challenges are encountered because of special features of NextGen sequencing data: the data are read counts that are extremely skewed and non-negative; the total number of reads varies dramatically across samples that require appropriate normalization. Statistical tools developed for microarray expression data, such as principal component analysis, are sub-optimal for analyzing NextGen sequencing data.Results: We propose a family of Poisson factor models that explicitly takes into account the count nature of sequencing data and automatically incorporates sample normalization through the use of offsets. We develop an efficient algorithm for estimating the Poisson factor model, entitled Poisson Singular Value Decomposition with Offset (PSVDOS). The method is shown to outperform several other normalization and dimension reduction methods in a simulation study. Through analysis of an miRNA profiling experiment, we further illustrate that our model achieves insightful dimension reduction of the miRNA profiles of 18 samples: the extracted factors lead to more accurate and meaningful clustering of the cell lines.