Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data.
Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data.
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DOI：
10.1093/bioinformatics/btu134
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发表时间：
2014-07-01
期刊：
影响因子：
--
通讯作者：
Theis FJ
中科院分区：
文献类型：
--
作者：
Buettner F;Moignard V;Göttgens B;Theis FJ
动机：高通量单细胞定量实时聚合酶链反应（qPCR）是一种很有前途的技术，允许在复杂的细胞过程的新见解。然而，PCR反应只能检测到一定的检测限，而失败的反应可能是由于低表达或缺乏表达，真实的表达水平是未知的。由于这种删失可能发生在高比例的数据中，因此在处理单细胞qPCR数据时，它是主要挑战之一。主成分分析（PCA）是一种重要的工具，用于可视化的结构，高维数据，以及用于识别细胞亚群。然而，到目前为止，还不清楚如何执行删失数据的PCA。我们提出了一种概率方法，该方法考虑了删失，并对包含单细胞qPCR数据的两个典型数据集进行了评估。结果如下：我们使用高斯过程潜变量模型框架，通过引入适当的噪声模型并允许每个维度使用不同的内核来解释审查。我们通过执行线性和非线性概率PCA来评估这种新方法用于两个典型的qPCR数据集（分别为小鼠胚胎干细胞和血液干/祖细胞）。考虑到删失导致数据的2D表示，这更好地反映了其已知结构：在两个数据集中，我们的新方法导致已知细胞类型的更好分离，并且能够揭示一个数据集中无法使用标准PCA解决的亚群。可用性和实施：该实现基于现有的高斯过程潜变量模型工具箱（https：//github.com/SheffieldML/GPmat）;噪声模型和用于审查的内核的扩展可在www.example.com上获得http://icb.helmholtz-muenchen.de/censgplvm。联系方式：fbuettner. gmail.com补充信息：补充数据可在生物信息学在线获得。
Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data. Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA. Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm. Contact: fbuettner.phys@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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