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6 results for “point pattern analysis”
Dissecting glial scar formation by spatial point pattern and topological data analysis
<p>These data were generated by the Laboratory of Neurovascular Interactions (https://elalilab.com/) at University Laval (Quebec, Canada), and reported in "Dissecting glial scar formation by spatial point pattern and topological data analysis". </p> <p>Please refer to the Open Science Framework (OSF) repository (https://osf.io/3vg8j/) or GitHub (https://github.com/elalilab/GlialScar_PPA-TDA_2022) to see the processing pipeline.</p> <p><strong>AUTHORS</strong><br> Manrique-Castano, Daniel; Bhaskar, Dhananjay; ElAli, Ayman</p> <p><strong>KEYWORDS</strong><br> Stroke, cerebral ischemia, brain injury, glial scar, reactive astrocytes, reactive microglia, </p> <p><br> <strong>1. STUDY DESCRIPTION </strong> <br> This research provides a quantitative analysis of reactive glia and glial scar formation in a mouse model of cerebral ischemia. The dataset in this repository consists of raw widefield microscopy images from healthy and ischemic animals. </p> <p><strong>2. EXPERIMENTAL CONDITIONS</strong><br> Six-month-old C57BL/6 mice were subjected to 30 minutes of cerebral ischemia by middle cerebral artery occlusion (MCAO). Brains were harvested at 5, 15, and 30 days post-ischemia (DPI) (see 10.5281/zenodo.3559570). 5 sham animals were included as controls. The full protocol for brain harvesting is available at 10.17504/protocols.io.4r3l27q5pg1y/v1. Brain sections were stained with NeuN, Gfap, and Iba1 antibodies to detect neurons and reactive glia after injury. Full protocol available at 10.17504/protocols.io.yxmvmk94og3p/v1 <br> <br> <strong>3. FILE DESCRIPTION</strong></p> <p><strong>- GT5X_Gfap_Iba1_NeuN.rar: </strong>Contain widefield (5x magnification) .tif images grouped by animals (5-7 images per animal; see research article for further details). The images were taken with the following parameters.</p> <p>Objective: Fluar 5x/0.25 M27<br> Scaling per pixel: 1.300 x 1.300 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 3 s<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 4 s<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 1 s<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 50 ms</p> <p>We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_5x_GenerateTiffs.jim.</p> <p><strong>- GT10X_Gfap_Iba1_NeuN.rar:</strong> Contain a single widefield (10x magnification) .tif image per animal at the level of the MCA territory (see research article for further details). The images were taken with the following parameters.</p> <p>Objective: ECM paln-NeoFluar 10x/0.30 M27<br> Scaling per pixel: 0.45 x 0.45 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 200 ms<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 250 ms<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 100 ms<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 10 ms</p> <p><br> We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_10x_GenerateTiffs.jim.<br> <br> For 5x and 10x images, the following naming strings apply:</p> <p>GT5x: Research project identifier indicating the magnification<br> M01(n): Animal ID<br> 5D(n): Days post-ischemia. 0D refers to healthy (naive) animals. <br> Scene1(n): Bregma level. Scene 1 corresponds to the most anterior area sampled, while Scene 6 or 7 is the most posterior.</p> <p><strong>- PointPatterns_10x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises a horizontal ROI from the ventricular area to the outer border of the dorsolateral cerebral cortex. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_10x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- PointPatterns_5x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises the ischemic hemisphere. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_5x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- QupathProjects_5x.rar: </strong>QuPath project folder for 5x images (GT5X_Gfap_Iba1_NeuN.rar). Each subfolder (per animal) contains the necessary files to import annotations (alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, and NeuN folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" in each folder opens the QuPath project in QuPath and reads the classifiers and data folders. Each folder also contains "_Alignement.json" and "_Registration_json" files generated during the alignment and annotation procedures in ABBA. However, when the route of the source images is changed, the plugin does not allow rerouting, and the files are of no practical use. The issue has been reported to the ABBA Github repository. </p> <p><strong>- QupathProjects_10x.rar:</strong> QuPath project folder for 10x images (GT5X_Gfap_Iba1_NeuN.rar). The folder contains the necessary files to import annotations (Alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, NeuN, and DAPI folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" opens the QuPath project in QuPath and reads the classifiers and data folders. </p>
Data from: A new digital method of data collection for spatial point pattern analysis in grassland communities
<p>A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of <i>Stipa</i><i> grandis</i> in grazed and ungrazed <i>S. grandis</i> communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of <i>S. grandis</i> by using the pair correlation function <i>g</i>(<i>r</i>) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of <i>S. grandis</i> were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.</p>
Data from: A new digital method of data collection for spatial point pattern analysis in grassland communities
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Spatial point pattern analysis of traces (SPPAT): an approach for visualizing and quantifying site-selectivity patterns of drilling predators
<p>Site-selectivity analysis in drilling predation may provide useful behavioral information of a predator interacting with its prey. However, traditional approaches exclude some spatial information (i.e., oversimplified trace position) and are dependent on the scale of analysis (e.g., arbitrary grid system used to divide the prey skeleton into sectors). Here we introduce the spatial point pattern analysis of traces (<i>SPPAT</i>), an approach for visualizing and quantifying the distribution of traces on shelled invertebrate prey, which includes improved collection of spatial information inherent to drillhole location (morphometric-based estimation), improved visualization of spatial trends (Kernel density and hotspot mapping), and distance-based statistics for hypothesis testing (<i>K</i>-, <i>L</i>-, and pair correlation functions). We illustrate the <i>SPPAT</i> approach through case studies of fossil samples, modern beach-collected samples, and laboratory feeding trials of naticid gastropod predation on bivalve prey. Overall results show that Kernel density and hotspot maps enable visualization of subtle variations in regions of the shell with higher density of predation traces, which can be combined with the maximum clustering distance metric to generate hypotheses on predatory behavior and anti-predatory responses of prey across time and geographic space. Distance-based statistics also capture the major features in the distribution of traces across the prey skeleton, including aggregated and segregated clusters, likely associated with different combinations of two modes of drilling predation, edge- and wall-drilling. The <i>SPPAT </i>approach is transferrable to other paleoecologic and taphonomic data such as encrustation and bioerosion, allowing for standardized investigation of a wide range of biotic interactions.</p>
Spatial point pattern analysis of traces (SPPAT): an approach for visualizing and quantifying site-selectivity patterns of drilling predators
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Individual multidrug resistance patterns in AML patients point to the need for personalized molecular analysis
GEO Series GSE33787. Homo sapiens. 22 samples. Type: Expression profiling by RT-PCR.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.