On the Use of Ripley's K-Function and Its Derivatives to Analyze Domain Size
AUTOR(ES)
Kiskowski, Maria A.
FONTE
The Biophysical Society
RESUMO
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.
ACESSO AO ARTIGO
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2726315Documentos Relacionados
- A domain substitution procedure and its use to analyze voltage dependence of homotypic gap junctions formed by connexins 26 and 32.
- On the convergence of derivatives of B-splines to derivatives of the Gaussian function
- Microinjection of Zebrafish Embryos to Analyze Gene Function
- The Use of Entropy to Analyze Phyllotactic Mutants: A Theoretical Analysis
- Use of RNase H and primer extension to analyze RNA splicing.