Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review

Ben-Said, Mariem

doi:10.1186/s13717-021-00314-4

Cited by 62 publications

(51 citation statements)

References 163 publications

(429 reference statements)

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“…These marks may represent protein intensity in IMC, macrophage phenotype in our ABM, or the concentration of a diffusible cytokine such as TGF-β or CSF-1. While 'marked point patterns' have been studied in ecology [61][62][63] and astronomy [64,65], few methods consider the spatial correlation of continuous marks. Arguably the most common is Stoyan's mark correlation function, k mm (r) [61,64,66,67].…”

Section: Spatial Statisticsmentioning

confidence: 99%

“…While 'marked point patterns' have been studied in ecology [61][62][63] and astronomy [64,65], few methods consider the spatial correlation of continuous marks. Arguably the most common is Stoyan's mark correlation function, k mm (r) [61,64,66,67]. It determines whether the marks of two points separated by a distance r are spatially correlated: k mm (r) > 1 indicates that the marks of points separated by r are larger than the average mark, while k mm (r) < 1 indicates that they have marks smaller than the average [61].…”

Section: Spatial Statisticsmentioning

confidence: 99%

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Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions

Bull

Byrne

2022

Preprint

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Cross-talk between tumour and immune cells, including macrophages, is complex, dynamic and contributes to tumour heterogeneity. In this paper, we introduce a hybrid agent-based model (ABM) to investigate how tumour-macrophage dynamics evolve over time and how they influence spatial patterns of tumour growth. Macrophage phenotype is determined by microenvironmental cues and governs the extent to which macrophages are pro- or anti-tumour, i.e., whether they infiltrate and attack tumour cells, or promote metastasis by guiding tumour cell migration towards blood vessels.We perform extensive ABM simulations to investigate how changes in macrophage sensitivity to microenvironmental cues – specifically, cytokines produced by tumour cells and perivascular fibroblasts – alters their spatial and phenotypic distributions and the tumour’s growth dynamics. We identify outcomes that include: compact tumour growth, tumour elimination, and diffuse patterns of invasion, characterised by clustering of tumour cells and macrophages around blood vessels.We compare the ability of different statistics to characterise the diverse spatial patterns that the ABM generates. These include the weighted pair correlation function (wPCF), a new statistic that quantifies the spatial distributions of cells labelled with a continuous value (e.g., macrophage phenotype). We assess the ability of each statistic to discriminate between the various spatial patterns that the ABM exhibits.By combining statistical analysis with ABM simulations, we show how mechanistic models can be used to generate synthetic data for validation of novel statistics (here, the wPCF) and to assess the extent to which specific statistical descriptions can distinguish different spatial patterns and model behaviours. Such statistics can then be applied to biological imaging data, such as multiplexed medical images, with increased confidence in the interpretation of the results. We show that the wPCF accurately describes differences in macrophage localisation between images, and posit that it would be a valuable tool for analysing multiplexed imaging data.Author summaryMacrophage phenotype is regulated by complex microenvironmental cues. It affects their spatial position and behaviour. In solid tumours, the spatial distribution of macrophages can vary significantly, and correlate with patient prognosis.In this paper, we use an agent-based model (ABM) to investigate how changes in sensitivity to tumour-induced microenvironmental cues affect macrophage phenotype and, in turn, how such phenotypic heterogeneity affects tumour composition and morphology. We illustrate the wide range of tumour outcomes that can arise from changing macrophage sensitivity to microenvironmental cues.We apply a variety of statistics to simulation outputs to characterise the range of observed spatial patterns. Different statistics identify different aspects of these patterns, with the most descriptive characterisations obtained from spatial statistics, such as the weighted pair correlation function (wPCF), that account for both phenotype and cell localisation.More generally, this paper illustrates how ABMs can be used to generate synthetic data which mimics data that can be extracted from different imaging modalities. This synthetic data can be used to test new statistics, like the wPCF, and validate their use for future application to multiplex histological imaging.

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Section: Spatial Statisticsmentioning

confidence: 99%

Section: Spatial Statisticsmentioning

confidence: 99%

Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions

Bull

Byrne

2022

Preprint

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“…There is no specific sample size formula for spatial point patterns; however, it is recommended to include at least 100 points. 10 This article adheres to the STROBE guidelines.…”

Section: Spatial Statistical Analysismentioning

confidence: 99%

“…15 Spatial analysis involves familiar steps in statistics. 10 First summary statistics explore the distribution of variables such as counts per unit area or density. These would be analogous to means and standard deviations for numerical data.…”

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confidence: 99%

“…Spatial point pattern analysis investigates the arrangement of points in 2- or 3-dimensional space. The points can also be associated with a measure of interest, such as time to intubation (called marks 9,10 ). Spatial statistics can establish whether points cluster together or are unusually dispersed or if the values of marks are regularly arranged or randomly scattered.…”

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confidence: 99%

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The Association of Laryngeal Position on Videolaryngoscopy and Time Taken to Intubate Using Spatial Point Pattern Analysis of Prospectively Collected Quality Assurance Data

Miller

Gemal

Ware

et al. 2022

Anesthesia &Amp; Analgesia

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BACKGROUND: During videolaryngoscopy (VL), the larynx appears within the defined area of the video screen, and its location can be measured as a point within this space. Spatial statistics offer methods to explore the relationship between location data and associated variables of interest. The aims of this study were to use spatial point pattern analysis to explore if the position of the larynx on VL is associated with longer times to intubate, increased risk of a needing >1 intubation attempt, or percentage of glottic opening. METHODS: Quality assurance data and clinical notes from all prehospital intubations using C-MAC Pocket Monitor with CMAC-4 blade (Karl Storz) from January 1, 2018, to July 31, 2020, were reviewed. We extracted 6 measurements corresponding to the time taken to obtain the initial and then best laryngeal view, time to manipulate a bougie, and time to place the endotracheal tube, as well a percentage of glottic opening and a number of intubation attempts. Larynx location was the middle of the base of glottis, in cm from the left and bottom on the C-MAC screen. Two plots were produced to summarize the base of glottis location and time to perform each time component of intubation. Next, a cross mark function and a maximum absolute deviation hypothesis test were performed to assess the null hypotheses that the spatial distributions were random. The association between glottis location and >1 intubation attempt was assessed by a spatial relative risk plot. RESULTS: Of 619 eligible intubations, 385 had a video for analysis. The following time variables had a nonrandom spatial distribution with a tendency for longer times when the larynx was offcenter to the top or right of the screen: laryngoscope passing from teeth to glottis, glottis first view to best view of the larynx, time from bougie appearing to being placed in the cords, and overall time from teeth to endotracheal tube passing through cords. There was no increased relative risk for >1 intubation attempt. CONCLUSIONS: Spatial point pattern analysis identified a relationship between the position of the larynx during VL and prolonged intubation times. We did not find a relationship between larynx location and >1 attempt. Whether the location of the larynx on the screen is a marker for difficult VL or if optimizing the larynx position to the center of the screen improves intubation times would require further prospective studies. (Anesth Analg 2022;134:1288-96) KEY POINTS• Question: Is the position of the larynx on videolaryngoscopy associated with longer times to intubate or an increased risk of a second attempt at intubation, using spatial point pattern analysis? • Findings: Laryngeal location to the top and right of the screen is associated with longer times for intubation and bougie manipulation but not requirement for >1 attempt. • Meaning: Larynx location may be associated with intubation performance measures. GLOSSARY CCP = critical care paramedic; C-MAC = Karl Storz C-MAC videolaryngoscopy range; CMAC-4 = Karl Storz C-MAC macintosh siz...

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stelfi: An R package for fitting Hawkes and log‐Gaussian Cox point process models

Jones‐Todd,

van Helsdingen

2024

Ecology and Evolution

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Modelling spatial and temporal patterns in ecology is imperative to understand the complex processes inherent in ecological phenomena. Log‐Gaussian Cox processes are a popular choice among ecologists to describe the spatiotemporal distribution of point‐referenced data. In addition, point pattern models where events instigate others nearby (i.e., self‐exciting behaviour) are becoming increasingly popular to infer the contagious nature of events (e.g., animal sightings). While there are existing R packages that facilitate fitting spatiotemporal point processes and, separately, self‐exciting models, none incorporate both. We present an R package, stelfi, that fits spatiotemporal self‐exciting and log‐Gaussian Cox process models using Template Model Builder through a range of custom‐written C++ templates. We illustrate the use of stelfi's functions fitting models to Sasquatch (bigfoot) sightings data within the USA. The structure of these data is typical of many seen in ecology studies. We show, from a temporal Hawkes process to a spatiotemporal self‐exciting model, how the models offered by the package enable additional insights into the temporal and spatial progression of point pattern data. We present extensions to these well‐known models that include spatiotemporal self‐excitation and joint likelihood models, which are better suited to capture the complex mechanisms inherent in many ecological data. The package stelfi offers user‐friendly functionality, is open source, and is available from CRAN. It offers the implementation of complex spatiotemporal point process models in R for applications even beyond the field of ecology.

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Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review

Cited by 62 publications

References 163 publications

Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions

Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions

The Association of Laryngeal Position on Videolaryngoscopy and Time Taken to Intubate Using Spatial Point Pattern Analysis of Prospectively Collected Quality Assurance Data

stelfi: An R package for fitting Hawkes and log‐Gaussian Cox point process models

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