Niemistö Antti, Nykter Matti, Aho Tommi, Jalovaara Henna, Marjanen Kalle, Ahdesmäki Miika, Ruusuvuori Pekka, Tiainen Mikko, Linne Marja-Leena, Yli-Harja Olli
Institute of Signal Processing, Tampere University of Technology, Tampere, Finland.
EURASIP J Bioinform Syst Biol. 2007;2007(1):46150. doi: 10.1155/2007/46150.
Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter (FACS). The second method is based on budding index analysis. An automated image analysis method is presented for the task of detecting the cells and buds. The proposed methods can be used to obtain quantitative information on the cell cycle phase distribution of a budding yeast S. cerevisiae population. They therefore provide a solid basis for obtaining the complementary information needed in deconvolution of gene expression data. As a case study, both methods are tested with data that were obtained in a time series experiment with S. cerevisiae. The details of the time series experiment as well as the image and FACS data obtained in the experiment can be found in the online additional material at http://www.cs.tut.fi/sgn/csb/yeastdistrib/http://www.cs.tut.fi/sgn/csb/yeastdistrib/.
本文提出了两种用于估计出芽酵母(酿酒酵母)细胞群体细胞周期阶段分布的计算方法。第一种是基于对群体中单个细胞DNA含量分析的非参数方法。DNA含量通过荧光激活细胞分选仪(FACS)进行测量。第二种方法基于芽殖指数分析。本文提出了一种用于检测细胞和芽的自动图像分析方法。所提出的方法可用于获取关于酿酒酵母细胞群体细胞周期阶段分布的定量信息。因此,它们为在基因表达数据反卷积中获得所需的补充信息提供了坚实的基础。作为一个案例研究,两种方法都用在酿酒酵母的时间序列实验中获得的数据进行了测试。时间序列实验的详细信息以及实验中获得的图像和FACS数据可在http://www.cs.tut.fi/sgn/csb/yeastdistrib/http://www.cs.tut.fi/sgn/csb/yeastdistrib/的在线补充材料中找到。