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975 results for “Spatial transcriptomics”
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 2
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figure 6 analyses.</p> <p>Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p> </p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p> <p> </p>
In silico spatial transcriptomic editing at single-cell resolution
<p>The data for training the GAN (Inversion) model and reproduce the results reported in the paper </p>
Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios
<p>All total 152 datasets are collected in the benchmarking study.</p> <p>Every dataset contains two parts: the gene expression matrix (or well-established model by dynwrap for trajectory) and the data information including the data id, repository, accession number, URL, technology platform, species, organ (source), cell number, gene number, data type, ERCC spike-in, dilution factor, volume, group condition, treatment, batch information and cluster labels.</p> <p>There are 23 datasets (data79-data101) for evaluating the simulation ability for cell trajectories which are derived from another Zenodo repository (https://zenodo.org/record/1443566).</p>
Single-cell and spatially resolved transcriptomic data of mouse regenerative livers under normal and fibrotic conditions
<p>A single-cell spatial-temporal transcriptomic atlas of liver regeneration under normal and fibrotic condition, including a total of 30 mouse liver samples obtained from 15 normal and 15 fibrotic mice at timepoints Day 0, 1, 2, 3, and 7 after a partial hepatectomy (PHx) procedure with three replicates for each time point, followed by the scRNA-seq and SRT sequencing for each sample using the Stereo-seq platform. </p>
Spatial transcriptomics images for papillary and anaplastic thyroid cancer IRIBHM dataset
<p>This dataset includes the image files for spatial transcriptomics data associated with the publication "Idiosyncratic and generic single nuclei and spatial transcriptional patterns in papillary and anaplastic thyroid cancers".</p>
CosMx Spatial transcriptome dataset of human gastric mucosa
<p>This dataset contains the spatial transcriptome dataset of human gastric mucosa obtained by CosMx</p> <p>A zip file contains the following folders and files:</p> <p><strong>Folders</strong></p> <p>- CellComposite folder: the composite immunofluorescent images of each FOV.</p> <p>- CellLabels folder: the cell definitions images for each FOV determined during cell segmentation.</p> <p>- CellOverlay folder: the cell boundary images for each FOV determined during cell segmentation.</p> <p>- CompartmentLabels folder: the subcellular compartment images for each FOV determined during cell segmentation. The compartment types are as follows: 0. Extracellular, 1. Nuclear, 2. Membrane, 3. Cytoplasmic</p> <p>- RawMorphologyImages folder: raw morphological TIF images for each FOV</p> <p> </p> <p><strong>Files</strong></p> <p>- Run5458_{sample_name}_exprMat_file.csv: cell expression matrix.</p> <p>- Run5458_{sample_name}_fov_positions_file.csv: each FOV relative position within global structure.</p> <p>- Run5458_{sample_name}_metadata_file.csv: the metadata of each cell.</p> <p>- Run5458_{sample_name}_tx_file.csv: the transcript file for each target gene and its position.</p> <p>- Run5458_{sample_name}-polygons.csv: the segmentation polygon file.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Ayumu Tsubosaka, Daisuke Komura, Miwako Kakiuchi, Hiroto Katoh, Takumi Onoyama, Asami Yamamoto, Hiroyuki Abe, Yasuyuki Seto, Tetsuo Ushiku, Shumpei Ishikawa, Stomach encyclopedia: Combined single-cell and spatial transcriptomics reveal cell diversity and homeostatic regulation of human stomach, Cell Reports, Volume 42, Issue 10, 2023, 113236, https://doi.org/10.1016/j.celrep.2023.113236.</p>
Single-cell spatial transcriptomics of an inducible destabilized-domain Cre mouse line to target disease associated microglia
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Spatial transcriptomics defines injury specific microenvironments and cellular interactions in kidney regeneration and disease
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Spatial transcriptomic analysis of human dorsoal root ganglia neurons
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Whole-embryo spatial transcriptomics at subcellular resolution from gastrulation to organogenesis
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MERFISH+, a large-scale, multi-omics spatial technology resolves the transcriptomic holograms of the 3D human developing heart
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Single-cell spatial transcriptomics and proteomics of APOE Christchurch in 5xFAD and PS19 mice
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Single-cell spatial transcriptomics of ACAN cKO in WT and 5xFAD mice
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Spatial transcriptomics of an innate granuloma in a mouse infection model with Chromobacterium violaceum
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Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing - Image and In SItu Sequencing data
<p>Image and ISS data for Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing reveals a biologically relevant zonation of the desmoplastic rim.</p><p>Image data consists of:</p><ul><li>Nucler stain (DAPI)</li><li>Masks for liver, rim and tumor regions</li><li>H&E images of parallel tissue sections</li></ul><p>Gene and cluster marker data is collected in the <i>markers.h5ad</i> file which can be read using AnnData (<a href="https://anndata.readthedocs.io/en/latest/">https://anndata.readthedocs.io/en/latest/)</a>. </p><p> </p>
spatiAlign: An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics
<p>Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.</p>
Transcriptomic and spatial datasets of human ex vivo right atrial tissue in ischemic heart disease and heart failure
<p>This dataset contains raw counts and processed data and annotations for our transcriptomic and spatial dissection of human ex vivo right atrial tissue in ischemic heart disease and heart failure.</p> <p> </p> <p>snRNA.zip contains the snRNA-seq dataset for heart right atrial appendade and pericardial fluid.</p> <p>VISIUM.zip contains the Visium spatial transcriptomics data</p> <p>Molecular cartography.zip contains the Resolve Biosciences molecular cartography spatial transcriptomics data</p>
Processed datasets used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics"
<p>These are the processed anndata objects used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics".</p> <p>These datasets are stored as .h5ad files and are intended to be used with the <a href="https://scanpy.readthedocs.io/en/stable/api.html">Scanpy</a> package in Python. They contain all relevant cell type annotation, unnormalized and transformed gene expression counts, as well as the inferred intercellular fow networks inferred by FlowSig.</p>
Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics (output data)
<p>This contains the output results of CalicoST (inferred CNAs and cancer clones), results of comparison methods, and CNAs inferred from WES data of 13 samples across four cancer types.</p> <p>In this updated version, we also included the simulated data and the results from CalicoST and other methods in CalicoST_simulation_deposit.zip. README contains the details of deposited files.</p>
Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics: fastq & bam files - Sample S5_Rec
<p>You can find here the fastq and bam files related to the datasets used in the publication: </p> <p>In this particular upload, you can find the fastq (version1) and bam (version2) files of the two replicates of sample S5_Rec (A121573)</p> <p><strong>Valdeolivas, A., Amberg, B., Giroud, N. <em>et al.</em> Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. <em>npj Precis. Onc.</em> 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong></p> <p> </p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.