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196 results for “Spatial map”
Data from: Ultra-fine scale spatially-integrated mapping of habitat and occupancy using structure-from-motion
Organisms respond to and often simultaneously modify their environment. While these interactions are apparent at the landscape extent, the driving mechanisms often occur at very fine spatial scales. Structure-from-Motion (SfM), a computer vision technique, allows the simultaneous mapping of organisms and fine scale habitat, and will greatly improve our understanding of habitat suitability, ecophysiology, and the bi-directional relationship between geomorphology and habitat use. SfM can be used to create high-resolution (centimeter-scale) three-dimensional (3D) habitat models at low cost. These models can capture the abiotic conditions formed by terrain and simultaneously record the position of individual organisms within that terrain. While coloniality is common in seabird species, we have a poor understanding of the extent to which dense breeding aggregations are driven by fine-scale active aggregation or limited suitable habitat. We demonstrate the use of SfM for fine-scale habitat suitability by reconstructing the locations of nests in a gentoo penguin colony and fitting models that explicitly account for conspecific attraction. The resulting digital elevation models (DEMs) are used as covariates in an inhomogeneous hybrid point process model. We find that gentoo penguin nest site selection is a function of the topography of the landscape, but that nests are far more aggregated than would be expected based on terrain alone, suggesting a strong role of behavioral aggregation in driving coloniality in this species. This integrated mapping of organisms and fine scale habitat will greatly improve our understanding of fine-scale habitat suitability, ecophysiology, and the complex bi-directional relationship between geomorphology and habitat use.
Spatial mapping of the hepatocellular carcinoma landscape identifies unique intratumoural perivascular-immune neighbourhoods
<p>The uploaded data includes results from imaging mass cytometry (IMC) data collected from hepatocellular carcinoma patients. The associated publication can be found <a href="https://journals.lww.com/hepcomm/fulltext/2024/11010/spatial_mapping_of_the_hcc_landscape_identifies.11.aspx">here</a> (Marsh-Wakefield <em>et al.</em>, 2024, <em>Hepatology Communications</em>).</p> <p>The CSV file contains segmented cells from IMC data. This includes the Patient, ROI, and Group each cell is assigned. Marker signal intensities underwent arcsine transformation and were rescaled. The “simprof_cluster” column contains the final iteration of clustering following initial X-shift clustering.</p> <p>Notes on additional columns:</p> <ul> <li>“Sample” is barcoded such that the first three digits are the ablation number, followed by the region on the TMA, the group, and the patient. I.e., “[ablation.number]_[TMA.location]_[group]_[patient]”.</li> <li>“x” and “y” refer to the coordinates of samples.</li> <li>“Group” refers to the tissue type. Included non-tumour (NT), invasive margin (IM), and tumour (T) regions.</li> <li>The area for each ROI has been calculated (µm^2 and mm^2).</li> <li>“Batch” refers to TMA.</li> <li>In most cases each area from each patient has three ablation sites. Three samples have an extra ablation site due to technical difficulties during the ablation, and hence have a “split” sample.</li> </ul> <p>The PDF file contains patient information associated with the IMC data.</p> <p>DOI of dataset:</p> <p>10.5281/zenodo.10622397</p> <p>Any further questions can be addressed to Felix Marsh-Wakefield felix.marsh-wakefield@sydney.edu.au</p>
STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
<p>Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at <a href="https://github.com/JEFworks-Lab/STalign">https://github.com/JEFworks-Lab/STalign</a> and as supplementary software with additional documentation and tutorials available at <a href="https://jef.works/STalign">https://jef.works/STalign</a>.</p> <p>Here we have included alignment results that were used in performance analysis of STalign:</p> <p>We aligned Slice 2 Replicate 3 to Slice 2 Replicate 2 of the MERFISH mouse coronal brain sections available from Vizgen Data Release V1.0. May 2021 (<a href="https://info.vizgen.com/mouse-brain-map">https://info.vizgen.com/mouse-brain-map</a>).</p> <ul> <li>STalign_S2R3_to_S2R2.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to S2R2 with STalign, cell positions of S2R3 after supervised affine alignment to S2R2, and counts for genes and blanks.</li> <li>STalign_S2R2.csv.gz contains cell ids, cell centroid positions of S2R2 and counts for genes and blanks.</li> </ul> <p>Additionally, we aligned Slice 2 Replicate 3 to a Visium dataset of an FFPE preserved adult mouse brain were obtained from the 10X Datasets website for <em>Spatial Gene Expression Dataset by Space Ranger 1.3.0</em> (<a href="https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0">https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0</a>).</p> <ul> <li>STalign_S2R3_to_Visium.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to Visium H&E staining with STalign, and counts for genes and blanks.</li> </ul> <p>Furthermore, we performed alignments with the 50um resolution 3D Allen Reference Atlas Nissl common coordinate framework, CCF (<a href="https://help.brain-map.org/display/mouseconnectivity/API">https://help.brain-map.org/display/mouseconnectivity/API</a>). We applied STalign to align the Allen CCF to each of the 9 MERFISH slices (3 slice locations with 3 biological replicates) provided by Vizgen. Because the Allen CCF has annotated brain regions, we were able to lift over those brain region annotations to label all cells in the MERFISH datasets.</p> <p>Also, since the STalign mappings from the Allen CCF to the MERFISH slices are invertible, for each slice we can apply the inverse of the mapping to get cell positions in the Allen CCF coordinates.</p> <ul> <li>STalign_SXRX_with_structure_id_name.csv.gz contains cell ids for Slice X Replicate X, original cell centroid positions, cell xyz-coordinates in Allen CCF, brain structure id per cell, brain structure acronym</li> </ul> <p>To evaluate the 3D CCF alignment, we performed unified transcriptional clustering analysis and cell-type annotation. All MERFISH datasets were combined. Transcriptional clustering analysis and cell type annotation was performed using the SCANPY package [version 1.9.1]. Data were normalized to counts per million (scanpy: normalize_total) and log transformed (scanpy: log1p). PCA (scanpy: pca) was computed on the cell by gene matrix. A neighborhood graph of cells using the top 10 PCs and 10 nearest neighbors was created (scanpy: neighbors), and Leiden clustering was performed on this graph (scanpy: leiden) to identify 29 clusters. Differentially expressed genes were extracted from each cluster (scanpy: rank_genes_groups), and cell-types were annotated based on marker genes in each cluster.</p> <ul> <li>STalign_celltypeannotations_merfishslices_v2.csv.gz contains for all nine slices cell ids and cell type annotations</li> </ul> <p>This updated (v2) cell-type annotation file contains a new column with simplified cell-types. Briefly, we fixed typos, standardized lower case/upper case formats, merged subclasses of each cell-types. For example, subclasses of astrocytes­­, which are originally labeled as “Astrocytes”, “Astrocytes(1)”, “Astrocytes(2)”, “Astrocytes(3)”, are all labeled as “Astrocytes” in the added column.</p> <p>Note: Cell ids may have been mutated from original string of numbers through reading and writing across programming languages that handle numbers with different precision. If using R to read the files shared here, one can find the cells in STalign_celltypeannotations_merfishslices_v2.csv.gz that correspond with STalign_SXRX_with_structure_id_name.csv.gz when cell ids are formatted as a double in scientific notation, which is how R will read the file automatically.</p>
Spatial probability maps of the superior parietal sulcus in the human brain
<p><span>The superior parietal sulcus (SPS) is the defining sulcus within the superior parietal lobule. The morphological variability of the SPS was examined in individual magnetic resonance imaging (MRI) scans of the human brain that were registered to the Montreal Neurological Institute (MNI) standard stereotaxic space. Two primary morphological patterns were consistently identified across hemispheres: 1) the SPS was identified as a single sulcus, separating the anterior from the posterior part of the superior parietal lobule and 2) the SPS was found as a complex of multiple sulcal segments. These morphological patterns were subdivided based on whether the SPS or SPS complex remained distinct or merged with surrounding parietal sulci. The morphological variability and spatial extent of the SPS were quantified using volumetric and surface spatial probabilistic mapping. The current investigation e</span><span>stablished consistent morphological patterns in a common anatomical space, the MNI stereotaxic space, to facilitate structural and functional analyses within the superior parietal lobule. </span></p>
Raw Data - Part 2 : Spatial multi-omic map of human myocardial infarction
<p>We provide here the raw data of snATAC-seq and snRNA-seq for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Raw Data - Part 4 : Spatial multi-omic map of human myocardial infarction ---- Raw image
<p>We provide here the raw image for the visium data for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Spatial Mapping of Mobile Genetic Elements and their Cognate Hosts in Complex Microbiomes - Identifying the host taxon of a previously undescribed plasmid
<p>We investigated the taxonomic association of an unknown plasmid within a plaque biofilm of a patient diagnosed with stage 3 periodontitis. We combined long- and short- read sequencing to identify a complete plasmid with minimal homology to any sequence in the RefSeq database. The plasmid carried several predicted genes for mobilization and toxin-antitoxin systems. We designed MGE-FISH probes for the plasmid and combined this MGE-FISH stain with an 18-genera HiPR-FISH panel.</p> <p>Images are labeled by collection time such that the laser order for a given field of view (fov) is: 488nm Lambda, 514nm Lambda, 561nm Lambda, 633nm Airyscan, 405nm Lambda. We used Flye (https://github.com/fenderglass/Flye) to assemble the plasmid using long read Nanopore sequencing only and we used OPERA-MS (https://github.com/CSB5/OPERA-MS) to do hybrid assembly with Illumina short reads and Nanopore long reads. The assemblies are in the fasta files and the reads that map to the assemblies are in the fastq files. </p>
Spatial Mapping of Mobile Genetic Elements and their Cognate Hosts in Complex Microbiomes - Combined MGE and taxonomic mapping
<p>We used rRNA-FISH to stain five common oral genera, <em>Veillonella, Streptococcus, Corynebacterium, Lautropia, </em>and <em>Neisseria, </em>each with a different fluorophore, and we used MGE-FISH to stain the <em>termL</em> gene of the active prophage with a sixth fluorophore.</p> <p>We assembled contigs using combined long- and short-read sequencing and identified a highly abundant plasmid. Alignment of this contig to the plasmid database (PLSDB) showed that the plasmid had previously been observed in <em>Prevotella nigrescens</em> (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_018127865.1/). We selected two genes from the contig with metallo-β-lactamase (MBL) domains as targets for MGE-FISH (https://www.uniprot.org/uniprotkb/V8CNR4/entry, https://www.uniprot.org/uniprotkb/V8CNR9/entry). We stained both putative MBL genes (<em>pMBL</em>) with the same color using MGE-FISH. For taxonomic mapping, we broadened our target panel by employing HIPR-FISH. We selected a target panel of 18 genera that are highly abundant and prevalent in human plaque. We designed a HiPR-FISH spectral encoding using a 5-fluorophore combinatorial barcoding scheme, whereby each fluorophore represents a binary bit, providing 31 possible barcodes (2^5 - 1 = 31). The fluorophore for MGE-FISH was spectrally distinct from those of HiPR-FISH, enabling simultaneous implementation of both methods.</p> <p>Images are labeled by collection time such that the laser order for a given field of view (fov) is: 488nm Lambda, 514nm Lambda, 561nm Lambda, 633nm Airyscan, 405nm Lambda. We used OPERA-MS (https://github.com/CSB5/OPERA-MS) to do hybrid assembly with Illumina short reads and Nanopore long reads. The assemblies are in the fasta files and the reads that map to the assemblies are in the fastq files. </p>
Dakar population estimates at 100x100m spatial resolution - grid layer - Dasymetric mapping
<p>This dataset contains the a raster layer with the population estimates obtained using a dasymetric mapping procedure (top-down approach). For a detailed description of the methodology, please refer to the following paper:</p> <p>Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. 2019. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” <em>Data</em> 4 (1): 13. <a href="https://doi.org/10.3390/data4010013">https://doi.org/10.3390/data4010013</a>.</p> <p>Funding and aknowledgement: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be/">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p> <p>The authors gratefully thanks the \href{http://assess-sn.org/}{ASSESS project}, funded by the \href{https://www.ares-ac.be}{ARES-CDD}, that provided the access to the census data.</p>
Potential map generated by the RF spatial model to define ideal zones for the occurrence of high density of giant trees in the Amazon
<p>The provided image is a theoretical map of giant tree density in the Amazon, generated from a spatial model based on the **Random Forest** algorithm. The map displays the spatial distribution of tree density, representing the number of trees taller than 60 meters per square kilometer (trees/km²). The model was developed using climatic, topographic, and soil variables to predict areas with higher concentrations of these giant trees.</p> <p>The areas are color-coded according to different density ranges, where:<br>- Lighter shades indicate lower tree density (≤ 5 trees/km²),<br>- Darker shades indicate higher density (up to 141 trees/km²).</p> <p>Biogeographic provinces within the Amazon biome, such as the **Guiana Shield**, **Xingu-Tapajós**, and **Roraima**, are highlighted, showing distinct density patterns across the Amazon region. This map is a valuable tool for understanding the spatial distribution of giant trees in the Amazon and plays a crucial role in conservation efforts and ecological monitoring in the region.</p>
Dataset : Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps
<ul> <li><strong>The three files in the dataset are:</strong></li> </ul> <p>1) Healthy Sheep Atria Fibre Orientation Dataset 300µm</p> <p>2) Heart Failure Sheep Atria Fibre Orientation Dataset 300µm</p> <p>3) Human Atria Fibre Orientation Dataset 330µm</p> <ul> <li><strong>Data is stored as numpy binary files. Given below is an example .py script to open the flat datasets:</strong></li> </ul> <p> import numpy as np<br> data = np.load("Human_330um.npy")</p> <ul> <li><strong>Volume and fibre orientation dataset stored in flat format as given below:</strong></li> </ul> <p> i, j, k, v1, v2, v3, ... repeated for each voxel </p> <p>where (i, j, k) are voxel coordinates and (v1, v2, v3) are vector components corresponding to fibre orientation within that voxel.</p>
Dataset for "A graph-theoretic approach for spatial filtering and its impact on mixed-type spatial pattern recognition in wafer bin maps"
<p>This is the dataset used in the paper, Ezzat, Liu, Hochbaum, and Ding, 2021, “A graph-theoretic approach for spatial filtering and its impact on mixed-type spatial pattern recognition in wafer bin maps,” <em>IEEE Transactions on Semiconductor Manufacturing</em>, Vol. 34, pp. 194-206.</p>
Data set to: Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
<p>Data for the manuscript "Mapping a brain parasite: occurrence and spatial distribution in fish encephalon", doi: 10.1016/j.ijppaw.2023.03.004. Description of the distribution of metacercariae from the trematode species <em>Cardiocephaloides longicollis</em> in the brain of fish. </p>
Spatial soil properties maps for Switzerland at 30 m resolution
<p>The Swiss Soil Property Map (SSPM) was developed using the quantile random forest machine learning algorithm and remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. The SSPM dataset provides maps at 30 m resolution for different soil depths (0, 30, 60, and 100 cm) in GeoTIFF format. The mean and respective uncertainty information is provided for each map. Please note that the phosphorus spatial map is only available for the topsoil (0-20 cm) due to the unavailability of the dataset at deeper depths.</p> <table> <caption>Description of soil properties (SP) and their units</caption> <tbody> <tr> <td>SP</td> <td>Description</td> <td>units</td> </tr> <tr> <td>Sand </td> <td>Sand content</td> <td>%</td> </tr> <tr> <td>Clay</td> <td>Clay content</td> <td>%</td> </tr> <tr> <td>OC</td> <td>Organic carbon content </td> <td>%</td> </tr> <tr> <td>N</td> <td>Nitrogen content</td> <td>% </td> </tr> <tr> <td>P</td> <td>Phosphorus content</td> <td>mg/kg</td> </tr> </tbody> </table> <p>For more details / to cite this dataset please use:</p> <ul> <li><strong>Gupta, S. </strong>, Hasler, K. J., Alewell, C.: Mapping soil properties of Switzerland using remote sensing datasets and machine learning approach. Manuscript <strong>submitted</strong>, <strong>Geoderma Regional</strong>, 2023</li> </ul> <p> </p>
Review data for: SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
<p>Climatology of snow water equivalent of Switzerland between winters 1962 and 2021. Obtained using quartile mapping between a model using data assimilation since 1998 and a model without data assimilation. This version of the dataset corresponds to the publication revision time. The publication has been submitted to GMD Copernicus journal as: <em>SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping</em></p>
Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Mapping MGEs in oral plaque biofilms at high specificity
<p>We stained for the GFP gene in samples that contained mixtures of plaque and GFP-transformed E. coli. We mapped mefE, an AMR gene located on a plasmid and encoding an antibiotic efflux pump, in the plaque metagenomic data of volunteer A but not volunteer B. To test the efficacy of gel embedding and clearing, we used orthogonal FISH probes, designed to not target any sequence in the plaque. We identified a T7-like prophage via metagenomic analysis and developed probes targeting its capsB gene, which encodes the minor capsid protein. We identified a highly prevalent prophage of the class Caudoviricetes with a large terminase gene, termL, and were able to design a large set of FISH probes to stain in three different colors simultaneously. We identified three non-plasmid AMR genes within metagenome assembled genomes: patA, patB, and adeF.</p>
Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Optimization of single molecule MGE FISH
<p>We used <em>Escherichia coli </em>transformed with pJKR-H-tetR plasmids encoding an inducible <em>GFP</em> gene as a model system to assess and optimize MGE-FISH on a confocal microscope. We designed FISH probes for the non-coding strand of the <em>GFP</em> gene, used non-transformed <em>E. coli </em>as a negative control, and tested six different FISH protocols.<strong> </strong></p>
Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Combined taxonomic mapping and MGE mapping
<p>We used rRNA FISH to stain five common oral genera, <em>Veillonella, Streptococcus, Corynebacterium, Lautropia, </em>and <em>Neisseria</em>, each with a different fluorophore, and we used MGE-FISH to stain the <em>termL</em> gene of an active prophage with a sixth fluorophore. </p> <p>We chose a target panel of 18 genera that are highly abundant and prevalent in human plaque and designed a HiPR-FISH probe panel using a 5-fluorophore combinatorial barcoding scheme. Using MGE-FISH, we stained a plasmid carrying mefE, subunit of a major-facilitator-superfamily antibiotic efflux pump. </p>
Predicted Spatially Complete Zoning Map of North Carolina
<p>Spatially-complete zoning map of North Carolina, USA. The <strong>results </strong>folder contains results of a machine learning (random forest) model predicting 3 core district zones (residential, non-residential, and mixed use) and 13 sub-district zones (open space, industrial, commercial, office, planned use, high-density residential, medium-high-density residential, medium-density residential, medium-low-density residential, low-density residential, agricultural residential, mixed use, and downtown). Results are provided as 30-m rasters (.tif) with each value corresponding to a zoning district. Table containing zone district ID (number) and zone district name (character string) is included in <strong>zone_classification.csv</strong>. Final (spatially complete statewide maps) can be found in the <strong>final_predicted </strong>folder. This folder includes Statewide core district results in <strong>NC_predicted_core.tif</strong> and statewide sub-district results in <strong>NC_predicted_sub.tif</strong>. </p> <p>Zoning was generalized and reclassified into 3 core district zones and 13 sub-district zones (described above). Reclassified zoning data, collected from 39 counties in North Carolina is provided in the <strong>observed </strong>folder with core districts in <strong>core_district_observed_zones.tif</strong> and sub-districts in <strong>sub_district_observed_zones.tif</strong>. Also in this folder is <strong>zoning_implementation_NC.csv</strong> which includes links to the source data (zoning map and zoning ordinance) for all collected data.</p> <p>Two models were created to predict zones under different data availability scenarios (i.e., scenarios that assume different levels of data availability). Predictions labeled “within_county” utilized the within-county model which predicts zoning districts in areas where zoning data is partially available for that county. To approximate scenarios of incomplete data accessibility, 20% of the data was randomly removed from training and reserved for independent performance assessments. Predictions labeled “between-county” utilized the between-county model which predicts zoning districts in areas where zoning data is inaccessible. To approximate this scenario, multiple between-county model iterations were computed by randomly removing entire counties from the training dataset and computing performance metrics on the removed (test) counties. Predictions are provided for both core districts and sub-districts (described above). Results from these models can be found in the <strong>predicted </strong>folder. This folder contains four subfolders: <strong>core_district_within_county</strong>, <strong>sub_district_within_county</strong>, <strong>core_district_between_county</strong>, and <strong>sub_district_between_county</strong>. Within each of these folders are predicted maps 30-m raster (.tif), performance reports including precision, recall, and f1 score overall and per district (.csv), and accuracy maps (3-km grid shapefile [.shp, .shx, .prj, .dbf]) with values corresponding to the proportion of misclassified pixels within a grid cell. Multiple randomized testing county samples were conducted for the between-county models. Each random sample is labeled <strong>r*_</strong> where * is replaced with a number between 1 and 15.</p>
Data from: Ultra-fine scale spatially-integrated mapping of habitat and occupancy using structure-from-motion
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.