Cluster analysis for localisation-based data sets: dos and don'ts when quantifying protein aggregates.

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in bioinformatics Pub Date : 2023-11-24 eCollection Date: 2023-01-01 DOI:10.3389/fbinf.2023.1237551
Luca Panconi, Dylan M Owen, Juliette Griffié
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引用次数: 0

Abstract

Many proteins display a non-random distribution on the cell surface. From dimers to nanoscale clusters to large, micron-scale aggregations, these distributions regulate protein-protein interactions and signalling. Although these distributions show organisation on length-scales below the resolution limit of conventional optical microscopy, single molecule localisation microscopy (SMLM) can map molecule locations with nanometre precision. The data from SMLM is not a conventional pixelated image and instead takes the form of a point-pattern-a list of the x, y coordinates of the localised molecules. To extract the biological insights that researchers require cluster analysis is often performed on these data sets, quantifying such parameters as the size of clusters, the percentage of monomers and so on. Here, we provide some guidance on how SMLM clustering should best be performed.

基于定位数据集的聚类分析:量化蛋白质聚集时的注意事项。
许多蛋白质在细胞表面呈现非随机分布。从二聚体到纳米级团簇,再到大型微米级聚集体,这些分布调节着蛋白质与蛋白质之间的相互作用和信号传递。虽然这些分布显示的组织长度尺度低于传统光学显微镜的分辨率极限,但单分子定位显微镜(SMLM)可以绘制出纳米级精度的分子位置图。单分子定位显微镜的数据不是传统的像素化图像,而是以点图案的形式出现--即定位分子的 x、y 坐标列表。为了提取研究人员所需的生物学洞察力,通常会对这些数据集进行聚类分析,量化诸如聚类大小、单体百分比等参数。在此,我们将就如何最好地进行 SMLM 聚类提供一些指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.60
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