On the Use of Ripley's K-Function and Its Derivatives to Analyze Domain Size
On the Use of Ripley's K-Function and Its Derivatives to Analyze Domain Size
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DOI：
10.1016/j.bpj.2009.05.039
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发表时间：
2009-08-19
影响因子：
3.4
通讯作者：
Kenworthy, Anne K.
中科院分区：
文献类型：
--
作者：
Kiskowski, Maria A.;Hancock, John F.;Kenworthy, Anne K.
Ripley的K-、H-和L-函数越来越多地用于识别膜微区中蛋白质的聚类。在该方法中，如果在另一蛋白质的距离r内的蛋白质的平均数量在统计学上大于随机分布的预期数量，则识别聚集（或聚类）。然而，目前还不完全清楚该函数如何用于定量确定发生聚类的域的大小。在这里，我们评估在何种程度上可以确定的域半径由不同的解释里普利的K-统计在理论上，理想化的背景下。我们还评估了噪声实验数据的措施，并使用Monte Carlo模拟来分离不同类型的实验噪声的影响。我们发现，最大聚集半径近似的域半径，而确定的域边界与H（r）的导数的最小值是非常准确的理想化条件。这两种测量的准确性受到实验数据中存在的噪声的影响;例如，在这里，存在大部分作为单体和域间相互作用分布的颗粒。这些发现有助于描述Ripley's K在现实生活中的局限性和潜力。
Ripley's K-, H-, and L-functions are used increasingly to identify clustering of proteins in membrane microdomains. In this approach, aggregation (or clustering) is identified if the average number of proteins within a distance r of another protein is statistically greater than that expected for a random distribution. However, it is not entirely clear how the function may be used to quantitatively determine the size of domains in which clustering occurs. Here, we evaluate the extent to which the domain radius can be determined by different interpretations of Ripley's K-statistic in a theoretical, idealized context. We also evaluate the measures for noisy experimental data and use Monte Carlo simulations to separate the effects of different types of experimental noise. We find that the radius of maximal aggregation approximates the domain radius, while identifying the domain boundary with the minimum of the derivative of H(r) is highly accurate in idealized conditions. The accuracy of both measures is impacted by the noise present in experimental data; for example, here, the presence of a large fraction of particles distributed as monomers and interdomain interactions. These findings help to delineate the limitations and potential of Ripley's K in real-life scenarios.