Statistical tests for measures of colocalization in biological microscopy.
Statistical tests for measures of colocalization in biological microscopy.
复制标题
生物学显微镜共定位测量的统计检验。
DOI：
10.1111/jmi.12093
复制
发表时间：
2013-12
影响因子：
2
通讯作者：
Dunn KW
中科院分区：
文献类型：
--
作者：
McDonald JH;Dunn KW
共定位分析是荧光显微镜图像定量分析中最常用的技术。已经开发了几种度量用于测量两个探针的共定位，包括Pearson相关系数（PCC）和Manders相关系数（MCC）。然而，一旦测量，这些测量值的含义可能不清楚;解释PCC或MCC值需要能够评估特定测量值的意义，或两组测量值之间差异的意义。在以前的工作中，我们展示了空间自相关如何混淆常用于共定位数据统计分析的随机化技术。在这里，我们使用计算机模拟的生物图像表明，学生的单样本t-检验可以用来测试的PCC或MCC测量的共定位的意义，和学生的双样本t-检验可以用来测试在不同的实验条件下获得的测量之间的差异的意义。
Colocalization analysis is the most common technique used for quantitative analysis of fluorescence microscopy images. Several metrics have been developed for measuring the colocalization of two probes, including Pearson's correlation coefficient (PCC) and Manders' correlation coefficient (MCC). However, once measured, the meaning of these measurements can be unclear; interpreting PCC or MCC values requires the ability to evaluate the significance of a particular measurement, or the significance of the difference between two sets of measurements. In previous work, we showed how spatial autocorrelation confounds randomization techniques commonly used for statistical analysis of colocalization data. Here we use computer simulations of biological images to show that the Student's one-sample t-test can be used to test the significance of PCC or MCC measurements of colocalization, and the Student's two-sample t-test can be used to test the significance of the difference between measurements obtained under different experimental conditions.
登录
查看更多内容
影响因子：
4.5
作者：
Rondanino, Christine;Rojas, Raul;Apodaca, Gerard
通讯作者：
Apodaca, Gerard
影响因子：
1.3
作者：
Fortin, MJ;Payette, S
通讯作者：
Payette, S
影响因子：
2
作者：
Lachmanovich, E;Shvartsman, DE;Weiss, AM
通讯作者：
Weiss, AM
影响因子：
3.4
作者：
Costes, SV;Daelemans, D;Lockett, S
通讯作者：
Lockett, S
影响因子：
3.3
作者：
Babbey, Clifford M.;Ahktar, Nahid;Dunn, Kenneth W.
通讯作者：
Dunn, Kenneth W.