Pyne Saumyadipta, Hu Xinli, Wang Kui, Rossin Elizabeth, Lin Tsung-I, Maier Lisa M, Baecher-Allan Clare, McLachlan Geoffrey J, Tamayo Pablo, Hafler David A, De Jager Philip L, Mesirov Jill P
Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge MA 02142, USA.
Proc Natl Acad Sci U S A. 2009 May 26;106(21):8519-24. doi: 10.1073/pnas.0903028106. Epub 2009 May 14.
Flow cytometric analysis allows rapid single cell interrogation of surface and intracellular determinants by measuring fluorescence intensity of fluorophore-conjugated reagents. The availability of new platforms, allowing detection of increasing numbers of cell surface markers, has challenged the traditional technique of identifying cell populations by manual gating and resulted in a growing need for the development of automated, high-dimensional analytical methods. We present a direct multivariate finite mixture modeling approach, using skew and heavy-tailed distributions, to address the complexities of flow cytometric analysis and to deal with high-dimensional cytometric data without the need for projection or transformation. We demonstrate its ability to detect rare populations, to model robustly in the presence of outliers and skew, and to perform the critical task of matching cell populations across samples that enables downstream analysis. This advance will facilitate the application of flow cytometry to new, complex biological and clinical problems.
流式细胞术分析通过测量荧光团偶联试剂的荧光强度，能够对细胞表面和细胞内的决定因素进行快速单细胞检测。新平台的出现使得能够检测越来越多的细胞表面标志物，这对通过手动设门识别细胞群体的传统技术提出了挑战，导致对开发自动化、高维分析方法的需求不断增加。我们提出了一种直接的多变量有限混合建模方法，使用偏态和重尾分布，以解决流式细胞术分析的复杂性，并处理高维细胞测量数据，而无需进行投影或转换。我们展示了它检测稀有群体的能力、在存在异常值和偏态的情况下进行稳健建模的能力，以及执行跨样本匹配细胞群体这一关键任务的能力，从而实现下游分析。这一进展将促进流式细胞术在新的、复杂的生物学和临床问题中的应用。
