ArticleScientific reports2016
On characterizing protein spatial clusters with correlation approaches.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Membrane topography and the overestimation of protein clustering in single molecule localisation microscopy - identification and correction.Communications biology · 2024Article
- Sticky, Adaptable, and Many-sided: SAM protein versatility in normal and pathological hematopoietic states.BioEssays : news and reviews in molecular, cellular and developmental biology · 2023Review
- Protocol for multicolor three-dimensional dSTORM data analysis using MATLAB-based script package Grafeo.STAR protocols · 2021Article
- Transcription-mediated supercoiling regulates genome folding and loop formation.Molecular cell · 2021Article
- K-Neighbourhood Analysis: A Method for Understanding SMLM Images as Compositions of Local Neighbourhoods.Frontiers in bioinformatics · 2021Article
- Multicolor 3D-dSTORM Reveals Native-State Ultrastructure of Polysaccharides' Network during Plant Cell Wall Assembly.iScience · 2020Article
- Eleven grand challenges in single-cell data science.Genome biology · 2020Review
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3 authors.
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Abstract
Spatial aggregation of proteins might have functional importance, e.g., in signaling, and nano-imaging can be used to study them. Such studies require accurate characterization of clusters based on noisy data. A set of spatial correlation approaches free of underlying cluster processes and input parameters have been widely used for this purpose. They include the radius of maximal aggregation ra obtained from Ripley's L(r) - r function as an estimator of cluster size, and the estimation of various cluster parameters based on an exponential model of the Pair Correlation Function(PCF). While convenient, the accuracy of these methods is not clear: e.g., does it depend on how the molecules are distributed within the clusters, or on cluster parameters? We analyze these methods for a variety of cluster models. We find that ra relates to true cluster size by a factor that is nonlinearly dependent on parameters and that can be arbitrarily large. For the PCF method, for the models analyzed, we obtain linear relationships between the estimators and true parameters, and the estimators were found to be within ±100% of true parameters, depending on the model. Our results, based on an extendable general framework, point to the need for caution in applying these methods.
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