A framework for evaluating the performance of SMLM cluster analysis algorithms

Nieves, Daniel J.; Pike, Jeremy; Williamson, David J.; Baragilly, Mohammed; Oloketuyi, Sandra Folarin; Marco, Ario de; Griffié, Juliette; Sage, Daniel; Cohen, Edward; Sibarita, Jean‐Baptiste; Heilemann, Mike; Owen, Dylan M.

doi:10.1038/s41592-022-01750-6

Cited by 32 publications

(40 citation statements)

References 41 publications

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“…We first compared the performance of Diinamic-R and Diinamic-V on various sets of simulations of SMLM data. In a recent article, Nieves et al ( 23 ) proposed a framework to evaluate SMLM cluster analysis algorithms, with simulated scenarios representing diverse situations of density of detections and size of clusters in a squared area of 2 μm × 2 μm. They also proposed two metrics, ARI, which analyzes the classification of detections into the same clusters as the ground truth; and IoU, which analyzes the overlap between the cluster areas in the output and the ground truth (here, the correct result).…”

Section: Resultsmentioning

confidence: 99%

“…The DBSCAN program was sourced from Pageon et al ( 16 ) and adapted to be run on MATLAB. The calculation of the adjusted rand index (ARI) and intersection over union (IoU) scores was implemented in MATLAB following the algorithms described in Nieves et al ( 23 ) . Clustering detection was carried out on regions of interest (ROIs) drawn on top of pointillistic images constructed from the coordinates of detections.…”

Section: Methodsmentioning

confidence: 99%

“…Table 1. Performance analysis of Diinamic-R and Diinamic-V against Ground truth scenarios 2-10 from Nieves et al (23) . Note.…”

Section: Performance Analysis On Point Classification and Geometric O...mentioning

confidence: 99%

“…Table 2. Performance analysis of Diinamic-R and Diinamic-V against scenarios 2-10 from Nieves et al (23) , simulating multiple blinking of fluorophores.…”

Section: Performance Analysis On Point Classification and Geometric O...mentioning

confidence: 99%

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Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Paupiah,

Marques,

Merlaud

et al. 2023

Biol. Imaging

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Super-resolution microscopy allowed major improvements in our capacity to describe and explain biological organization at the nanoscale. Single-molecule localization microscopy (SMLM) uses the positions of molecules to create super-resolved images, but it can also provide new insights into the organization of molecules through appropriate pointillistic analyses that fully exploit the sparse nature of SMLM data. However, the main drawback of SMLM is the lack of analytical tools easily applicable to the diverse types of data that can arise from biological samples. Typically, a cloud of detections may be a cluster of molecules or not depending on the local density of detections, but also on the size of molecules themselves, the labeling technique, the photo-physics of the fluorophore, and the imaging conditions. We aimed to set an easy-to-use clustering analysis protocol adaptable to different types of data. Here, we introduce Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters. On simulated or real SMLM data, Diinamic correctly identified clusters of different sizes and densities, being performant even in noisy datasets with multiple detections per fluorophore. It also detected subdomains ("nanodomains") in clusters with non-homogeneous distribution of detections. Impact StatementSingle molecule localization microscopy not only provide images with higher resolution than classical fluorescence microscopy, but the pointillistic character of its data opened a new field of biological image analysis. The possibility to "see" molecules one by one offers the perfect way to analyse the distribution of molecules, and several analytical tools were proposed to describe the formation of aggregates or clusters. However, clusters in biological samples can be very variable in size and density and the available analytical tools are, in general, effective only for a certain type of distribution. Moreover, the characteristics of clusters depend not only on the molecules themselves, but also on the labelling technique, the photo-physics of the fluorophore and the imaging conditions. We developed Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters in biological samples. By combining progressive analysis steps, Diinamic can be easily adapted to a large variety of molecular distributions. In addition, it provides the possibility to introduce biology-based criteria to describe the clustering behaviour of molecules. To help with its application, we provide cues about the strategy to follow depending on the characteristics of the dataset.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Table 1. Performance analysis of Diinamic-R and Diinamic-V against Ground truth scenarios 2-10 from Nieves et al (23) . Note.…”

Section: Performance Analysis On Point Classification and Geometric O...mentioning

confidence: 99%

“…Table 2. Performance analysis of Diinamic-R and Diinamic-V against scenarios 2-10 from Nieves et al (23) , simulating multiple blinking of fluorophores.…”

Section: Performance Analysis On Point Classification and Geometric O...mentioning

confidence: 99%

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Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Paupiah,

Marques,

Merlaud

et al. 2023

Biol. Imaging

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show abstract

“…In our nanoparticle size analysis, we used FWHM to extract size information after correction of localization error. Several other clustering algorithms have been used for sizing nanostructures, including density-based spatial clustering of applications with noise (DBSCAN), fast optimized clustering algorithm for localization (FOCAL), cluster analysis by machine learning (CAML), tessellation-based methods such as clusterVisu and SR-Tesseler, and pairwise nearest distance measurement . All of these methods are focused on finding the boundary of clusters to estimate size.…”

Section: Discussionmentioning

confidence: 99%

Error-Correction Method for High-Throughput Sizing of Nanoscale Vesicles with Single-Molecule Localization Microscopy

et al. 2023

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Single-molecule localization microscopy (SMLM) allows super-resolution imaging, mapping, counting, and sizing of biological nanostructures such as cell organelles and extracellular vesicles (EVs), but sizing structures smaller than ∼100 nm can be inaccurate due to single-molecule localization error caused by distortion of the point spread function and limited photon number. Here we demonstrate a method to correct localization error when sizing vesicles and other spherical nanoparticles with SMLM and compare sizing results using two vesicle labeling schemes. We use mean approximation theory to derive a simple equation using full width at half-maximum (FWHM) for correcting particle sizes measured by two-dimensional SMLM, validate the method by sizing streptavidin-coated polystyrene nanobeads with the SMLM technique dSTORM with and without error correction, using transmission electron microscopy (TEM) for comparison, and then apply the method to sizing small seminal EVs. Nanobead sizes measured by dSTORM became increasingly less accurate (larger than TEM values) for beads smaller than 50 nm. The error-correction method reduced the size difference versus TEM from 15% without error correction to 7% with error correction for 40 nm beads, from 44% to 9% for 30 nm beads, and from 66% to 15% for 20 nm beads. Seminal EVs were labeled with a lipophilic membrane dye (MemBright 700) and with an Alexa Fluor 488-anti-CD63 antibody conjugate, and were sized separately using both dyes by dSTORM. Error-corrected exosome diameters were smaller than uncorrected values: 72 nm vs 79 nm mean diameter with membrane dyes; 84 nm vs 97 nm with the antibody-conjugated dyes. The mean error-corrected diameter was 12 nm smaller when using the membrane dye than when using the antibody-conjugated dye likely due to the large size of the antibody. Thus, both the error-correction method and the compact membrane labeling scheme reduce overestimation of vesicle size by SMLM. This error-correction method has a low computational cost as it does not require correction of individual blinking events, and it is compatible with all SMLM techniques (e.g., PALM, STORM, and DNA-PAINT).

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Cloud migration framework clustering method for social decision support in modernizing the legacy system

Aslam,

Rahim,

Watada

et al. 2023

Trans Emerging Tel Tech

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Cloud solutions accelerate the large‐scale acceptance of IoT projects. By diminishing the need for maintaining on‐premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in‐home appliances) and opened the doors for large‐scale deployment of IoT applications on the cloud. However, shifting legacy systems to the cloud environment can be considerably difficult. Accordingly, this article proposes a method that may support organizations in deciding to modernize their legacy systems. The main concept of this study is to discuss the modernization strategies in detail and to support organizations in selecting the most accurate and appropriate cloud migration strategy, based on their requirements of the legacy system. This article introduces a novel research process, called the K‐means cosine cloud clustering method (K3CM). K3CM is a statistical knowledge‐based method for identifying and clustering the most relevant and similar cloud migration strategies. The quality of a cluster is evaluated by measuring intra‐cohesiveness. Simulation experiments statistically analyzed, evaluated, and verified the quality of K3CM clusters. Correspondence analysis explored the similarity and relationship among cloud migration frameworks and validated the proposed technique. The statistical and simulation results of this study focus on the analytics and decision support system implementation that provides a reliable, valid, and robust clustering method for modernizing the legacy system.

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A framework for evaluating the performance of SMLM cluster analysis algorithms

Cited by 32 publications

References 41 publications

Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Error-Correction Method for High-Throughput Sizing of Nanoscale Vesicles with Single-Molecule Localization Microscopy

Cloud migration framework clustering method for social decision support in modernizing the legacy system

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