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196 results for “Spatial map”
Raw Data - Part 1 : Spatial multi-omic map of human myocardial infarction
<p>We provide here the raw data of snATAC-seq for the manuscript: Kuppe, Ramirez Flores, Li et al. "Spatial multi-omic map of human myocardial infarction", 2022</p>
Ensemble flood map spatial verification
<p>Assessing the spatial spread-skill of ensemble flood maps with remote sensing observations, data and code.</p> <p>Creator: Helen Hooker[1] Publication Year: 2022</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- Python functions for ensemble flood map spatial spread-skill evaluation.</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>Helen Hooker. (2022). Ensemble flood map spatial verification (v1.0) [Data set]. Zenodo. https://10.5281/zenodo.6603101</p> <p>Related publications:</p> <p>Assessing the spatial spread-skill of ensemble flood maps with remote sensing observations; 2023; NHESS; Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging
<p>Mapping the human body at single cell resolution in three dimensions (3D) is an important step toward a “digital twin” model that digitizes organ structure and the dynamics of cell-cell interactions. Current approaches for 3D reconstruction of multiplexed serial sections are relatively cumbersome and not automated. We present a novel 3D reconstruction workflow for multiplexed sequential tissue sections: MATRICS-A (Multiplexed Image Three-D Reconstruction and Integrated Cell Spatial - Analysis). This combines a reproducible and streamlined method for two-dimensional (2D) cell segmentation and cell type classification, followed by 3D volume reconstruction. We also provide novel tools for 3D visualization of immune cell cluster density, cell-to-vasculature distance, and distance maps for cellular markers of UV damage/repair and proliferation to the skin surface. We used MATRICS-A to reconstruct 26 serial sections of fixed skin from 12 donors aged between 32-72 years. Samples were collected from six distinct anatomical regions with mild to marked sun exposure effects. Each tissue section was multiplexed with 18 fluorescently labeled antibodies covering 14 cell types in the epidermis and dermis. We demonstrate several new findings that are only possible in 3D, We present novel visualization of immune cell clusters and show that there are 10-70% more T cells (total) within 30 µm of a T helper cell in 3D vs 2D. Distances of cell markers of DNA damage (p53), DNA repair (DDB2) and proliferation (Ki67) to the skin surface were consistent across all ages/sun exposure. MATRICS-A provides a new powerful integrated open access approach to quantify 3D spatial cell relationships in healthy and aging organs and could be further extended to diseased organs. </p>
Distinct spatial maps and multiple object codes in the lateral entorhinal cortex
<p>Data used for the paper "Distinct spatial maps and multiple object codes in the lateral entorhinal cortex".</p> <p>For each open field trial of each recording session, there are two files ("sop" with spike times and x-y position of the mouse at that time, plus cell number, and "xyPath" with all tracking data from that trial, i.e., x-y position and time). Naming is as follows: mouseName-date-0107_trialNumber (e.g. ib5718-16092021-0107_2)</p> <p>Code for analysis of the raw data is deposited in GitHub: https://github.com/isabarriuso/LEC_maps_2023/</p> <p> </p> <p> </p>
A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps
ClinicalTrials.gov study NCT07244094. IPD Sharing: Not stated. Countries: 1. Publications: 0.
High-resolution mapping of the spatial organization of Caulobacter crescentus chromosome by chromosome conformation capture in conjunction with next-generation sequencing (Hi-C)
GEO Series GSE45966. Caulobacter vibrioides CB15. 23 samples. Type: Other.
High-Resolution Spatial Map of the Human Facial Sebaceous Gland Reveals Marker Genes and Decodes Sebocyte Differentiation
GEO Series GSE292156. Homo sapiens. 3 samples. Type: Other.
Spatial mapping of transcriptomic and lineage plasticity in metastatic pancreatic cancer
GEO Series GSE274557. Homo sapiens. 57 samples. Type: Other.
4D marmoset brain map reveals MRI and molecular signatures for onset of multiple sclerosis–like lesions [spatial transcriptomes]
GEO Series GSE266655. Callithrix jacchus. 16 samples. Type: Other.
Spatial and Single Cell Mapping of Human Lymph Node Disease
GEO Series GSE296614. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing; Other.
Identification and mapping of human lymph node stromal cell subsets by combining single-cell RNA sequencing with spatial transcriptomics.
GEO Series GSE261747. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Spatially mapped single-cell chromatin accessibility
GEO Series GSE164849. Mus musculus; Homo sapiens. 5 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
NEST: Spatially-mapped cell-cell communication patterns using a deep learning-based attention mechanism
GEO Series GSE262245. Homo sapiens. 2 samples. Type: Other.
Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps
GEO Series GSE169504. Severe acute respiratory syndrome coronavirus 2; Homo sapiens. 373 samples. Type: Expression profiling by high throughput sequencing; Other.
Spatial mapping of thymic stromal microenvironments reveals unique features influencing T lymphoid differentiation
GEO Series GSE18281. Mus musculus. 33 samples. Type: Expression profiling by array.
Spatial gene expression maps of the intestinal lymphoid follicle and associated epithelium identify zonated expression programs
GEO Series GSE168483. Mus musculus. 25 samples. Type: Expression profiling by high throughput sequencing.
Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing
<p>This deposit contains the MERFISH data used in this study.</p>
High spatial resolution Fractional Vegetation Cover maps OAL-DE (Elbe river). Further details can be found in D4.5 of the OPERANDUM project.
<p>In order to reduce the risk posed by flooding, areas of woody vegetation have been removed along the riverbank of Elbe river in order to expedite the inflow and outflow of water from the main channel, and thus contribute to flattening the peak hydrographic response. Maintaining the effectiveness of this clearing requires that there is little or no regrowth of this woody vegetation. The NBS that have been implemented in OAL-Germany sees the use of various animals to graze these areas. NBS is devoted to prevent the re-growth of woody vegetation after an intervention which took place over a period from autumn 2014 to February 2015, when woody vegetation along the riverbank was cut back. Monitoring the effectiveness of the NBS is being performed by means of high spatial resolution remotely sensed data , i.e. Rapideye at 5 m spatial resolution. The preliminary analysis of this experiment consisted in the monitoring of the fractional vegetation cover over four of the seven NBS sites. </p> <p>The green fractional abundance (fc) was calculated by an algorithm based on scaling NDVI in-between the maximum and minimum NDVI values. A semi-empirical method based on the use of NDVI was used following Zeng et al, (2000), to calculate fc. </p> <p>The dataset contains layer stack of fractional abundance calculated for the images calculated by Rapideye images acquired on 18 April 2013, 15 April 2015, 17 March 2016, 9 April 2019. </p>
Mapping spatial organization and genetic cell state regulators to target immune evasion in ovarian cancer
<p>This collection of data accompanies the study: <a href="https://www.nature.com/articles/s41590-024-01943-5">Yeh, Aguirre, Laveroni <em>et al.</em> <strong>Mapping spatial organization and genetic cell-state regulators to target immune evasion in ovarian cancer. </strong>(2024). </a><em><strong><a href="https://www.nature.com/articles/s41590-024-01943-5">Nature Immunology</a>.</strong></em> Files are provided in the form of RObjects (extension .rds) or tabulated data (extension .csv) to reproduce the results and figures provided in the paper via the the R programming environment using Code provided <a href="https://github.com/Jerby-Lab/HGSC_SpatialPerturbational">here</a>. </p> <p> </p> <p>Data collected and processed and published as a part of this study of tubo-ovarian high grade serous carcinoma (HGSC) includes: </p> <ul> <li>~<strong>2.5 million single cell spatial transcriptomics profiles</strong> from <strong>130 HGSC tumors</strong> of <strong>94 patients</strong></li> <li>Matching de-identified <strong>clinical annotations</strong> and clinical outcomes.</li> <li>Matching <strong>targeted genomic data</strong> from the bulk tumor tissues.</li> <li><strong>Perturb-seq CRISPR knockout</strong> data in ovarian cancer cells in monoculture and co-culture with Natural Killer (NK) cells.</li> </ul> <p>The spatial transcriptomics, Perturb-Seq, and matched H&E (Hematoxylin & Eosin, where available) are also provided via the Single Cell Portal with an <strong>interactive interface</strong> (<a href="https://singlecell.broadinstitute.org/single_cell/study/SCP2640/hgsc-spatial-cohort-discovery-dataset">SCP2640</a>, <a href="https://singlecell.broadinstitute.org/single_cell/study/SCP2641/hgsc-spatial-cohort-validation-1-dataset">SCP2641</a>, <a href="https://singlecell.broadinstitute.org/single_cell/study/SCP2650/hgsc-spatial-cohort-validation-2-dataset">SCP2650</a>, <a href="https://singlecell.broadinstitute.org/single_cell/study/SCP2644/hgsc-spatial-cohort-test-1-dataset">SCP2644</a>, <a href="https://singlecell.broadinstitute.org/single_cell/study/SCP2646/hgsc-spatial-cohort-test-2-dataset">SCP2646</a>, <a href="https://singlecell.broadinstitute.org/single_cell/study/SCP2707/hgsc-spatial-study-perturb-seq">SCP2707</a>).</p> <p>The collection also includes previously published data that was analyzed and used in this study to examine the generalizability of the findings, evaluate immunotherapy predictors, and for data-driven experimental design.</p> <p>The SeuratObj.zip contains six SeuratObjects matching the five spatial transcriptomcs datasets (Discovery, Validation 1, Validation 2, Test 1 and Test 2) and Perturb-Seq data.</p> <p>The Yeh2024.zip file includes the study's data and additional datasets/results to reproduce the study's figures. A detailed description of the files included in the repository is provided in `README.txt` and `README.xlsx`</p>
Multifaceted Assessment of Patients With Wilson's Disease in a Low-Resource Setting in Upper Egypt: Service Integration, Psychosocial Burden, Dietary Practices, and the Geo-Spatial Disease Map
ClinicalTrials.gov study NCT07241832. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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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.