Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices.
Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices.
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DOI：
10.1002/cyto.a.22272
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发表时间：
2013-05
期刊：
影响因子：
3.7
通讯作者：
Rajwa, Bartek
中科院分区：
文献类型：
--
作者：
Novo, David;Gregori, Gerald;Rajwa, Bartek
多光谱和高光谱流式细胞术（FC）仪器允许测量来自流动中的单个细胞的荧光或拉曼光谱。与常规FC一样，光谱重叠导致任何给定检测器中的测量信号是来自被分析细胞中存在的多个标记的信号的混合物。与传统的多色FC相比，这些设备利用的检测器（或多光谱检测器阵列中的通道）的数量大于标签的数量，并且没有特定的检测器先验地专用于测量任何特定标签。这种数据采集方式需要对信号形成以及用于估计标签丰度的解混程序进行严格的研究和理解。最简单的扩展传统的补偿程序的多光谱数据集是相当于一个普通的最小二乘（LS）的解决方案，用于估计丰度的标签在个别细胞。该过程与用于在各种成像领域中解混光谱数据的技术相同。本研究表明，多光谱FC数据违反LS过程的关键假设，使用LS方法可能会导致解混伪影，如人口失真（扩散）和生物标志物丰度的负值的存在。研究了各种不同的解混技术，包括相对误差最小化和方差稳定变换。最有希望的结果是通过使用泊松回归与广义线性模型框架内的身份链接函数进行解混。该公式解释了信号形成模型中泊松噪声的存在，并随后导致上级解混结果，特别是对于暗淡的荧光群体。建议泊松分解技术证明使用模拟的8通道，2-荧光染料数据和真实的32通道，6-荧光染料数据。通过计算绝对和相对误差，以及通过计算已知和近似种群之间的对称化Kullback-Leibler散度来评估解混的质量。这些结果适用于任何基于流的系统，其具有比标签更多的检测器，其中泊松噪声是整个系统噪声的主要贡献者，并且突出了这样一个事实，即明确并入适当的噪声模型是准确估计细胞上的真实标签丰度的关键。
Multispectral and hyperspectral flow cytometry (FC) instruments allow measurement of fluorescence or Raman spectra from single cells in flow. As with conventional FC, spectral overlap results in the measured signal in any given detector being a mixture of signals from multiple labels present in the analyzed cells. In contrast to traditional polychromatic FC, these devices utilize a number of detectors (or channels in multispectral detector arrays) that is larger than the number of labels, and no particular detector is a priori dedicated to the measurement of any particular label. This data-acquisition modality requires a rigorous study and understanding of signal formation as well as unmixing procedures that are employed to estimate labels abundance. The simplest extension of the traditional compensation procedure to multispectral data sets is equivalent to an ordinary least-square (LS) solution for estimating abundance of labels in individual cells. This process is identical to the technique employed for unmixing spectral data in various imaging fields. The present study shows that multispectral FC data violate key assumptions of the LS process, and use of the LS method may lead to unmixing artifacts, such as population distortion (spreading) and the presence of negative values in biomarker abundances. Various alternative unmixing techniques were investigated, including relative-error minimization and variance-stabilization transformations. The most promising results were obtained by performing unmixing using Poisson regression with an identity-link function within a generalized linear model framework. This formulation accounts for the presence of Poisson noise in the model of signal formation and subsequently leads to superior unmixing results, particularly for dim fluorescent populations. The proposed Poisson unmixing technique is demonstrated using simulated 8-channel, 2-fluorochrome data and real 32-channel, 6-fluorochrome data. The quality of unmixing is assessed by computing absolute and relative errors, as well as by calculating the symmetrized Kullback–Leibler divergence between known and approximated populations. These results are applicable to any flow-based system with more detectors than labels where Poisson noise is the dominant contributor to the overall system noise and highlight the fact that explicit incorporation of appropriate noise models is the key to accurately estimating the true label abundance on the cells.
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影响因子：
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作者：
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通讯作者：
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影响因子：
3.2
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