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1,322 results for “Cells maps”
SeMRA Cell and Cell Line Mappings Database
<p>Originally a reproduction of the EFO/Cellosaurus/DepMap/CCLE scenario posed in the Biomappings paper, this configuration imports several different cell and cell line resources and identifies mappings between them. See instructions for reproduction and usage in the attached README.md.</p>
Process-structure-property map for organic solar cells
<p>This archive contains:<br> - 1708 morphologies generated using Cahn-Hilliard equation for two parameters: blend ratio in range (0.5-0.63) and chi/interaction parameter (2.3-4.0). Blend ratio and chi are the processing conditions. Morphologies are given in two formats: plt files (srcdata folder) - raw data from simulations, and txt file (data folder) in the row-wise format for Graspi. For quick visualization morphologies are visualized and stored in folder figs.</p> <p>The dataset contains:<br> Five folders:<br> - srcdata: the source data with all plt files (1708 files) generated by Cahn Hilliard equation solver<br> - data: the data used by graspi to compute descriptors (these are txt files stored as row-wise array, the volume fraction has been segmented using tools of graspi)<br> - logs: 1708 log files with the descriptors generated by graspi (native C++ version)<br> - figs: 1708 png files with visualized morphologies</p> <p>Two tables (in comma separated values format):<br> - Combined PSP.csv (combined data on process-structure-property maps), where process information consists of three variables: PHI, CHI, NN (volume fraction, interaction parameter and time step index)<br> - AllPropertiesCurated.csv - File with results from EDD - see reference below for more details</p> <p>One shell script: extractDesc.sh to build CombinedPSP.csv table from three sources: filename (contains info about PHI, CHI, NN), descriptors (graspi and logs), and Jsc (from AllPropertiesCurated.csv)</p> <p>More details on the dataset: Wodo, O., J. Zola, . Pokuri, P. Du, and B. Ganapathysubramanian. "Automated, high throughput exploration of process–structure–property relationships using the mapreduce paradigm." Materials discovery 1 (2015): 21-28.</p> <p> </p>
Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in an Alzheimer disease model
<p>Amyloid-β plaques and neurofibrillary tau tangles are the neuropathologic hallmarks of Alzheimer’s disease (AD), but the spatiotemporal cellular responses and molecular mechanisms underlying AD pathophysiology remain poorly understood. Here we introduce STARmap PLUS to simultaneously map single-cell transcriptional states and disease marker proteins in brain tissues of AD mouse models at a voxel size of 95 95 350 nm. This high-resolution spatial transcriptomics map revealed a core-shell structure where disease-associated microglia (DAM) closely contact amyloid-β plaques, whereas disease-associated astrocyte-like cells (DAA-like) and oligodendrocyte precursor cells (OPC) are enriched in the outer shells surrounding the plaque-DAM complex. Hyperphosphorylated tau emerged mainly in excitatory neurons in the CA1 region accompanied by infiltration of oligodendrocyte subtypes into the axon bundles of hippocampal alveus. The integrative STARmap PLUS method bridges single-cell gene expression profiles with tissue histopathology at subcellular resolution, providing an unprecedented roadmap to pinpoint the molecular and cellular mechanisms of AD pathology and neurodegeneration.</p>
Reference model and embedding for human kidney endothelial cell mapping
<p>Reference model and embedding for human kidney endothelial cell mapping</p><p>The reference model serves as a basis for the mapping of new data to the HLCA using scArches (Lotfollahi et al., https://doi.org/10.1038/s41587-021-01001-7). </p>
Datasets for: Integrative Mapping of Human CD8+ T Cell in Inflammation and Cancer
<p>Files for the integrated pan-disease CD8+ T cell atlas: </p> <ul> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.all_genes.h5ad.gz?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.all_genes.h5ad.gz</a> : 19957 genes raw matrix </li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.X_gex.npy?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.X_gex.npy</a> : scatlasavae model embedding</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.clone_subtype.csv?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.clone_subtype.csv</a> : clone type definition</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.supervised.model</a>. scatlasavae model checkpoint</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : Tex subset of 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model</a>. scatlasavae model checkpoint for the Tex subset</li> </ul> <p>Files for the TILs CD8+ T cell atlas</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_chu.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_chu.h5ad</a>. the Chu <em>et al.</em>, 2023 Dataset</li> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_zheng.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_zheng.h5ad.</a> The Zheng <em>et al.</em>, 2021 Dataset</li> </ul> <p>Files for the transfer (query) datasets:</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/Zhang_LC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Zhang_LC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_TIL.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_TIL.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_PBMC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_PBMC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Watson_MELA.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Watson_MELA.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bassez_BC.cohort1.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bassez_BC.cohort1.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bi_RCC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bi_RCC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Caushi_NSCLC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Caushi_NSCLC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Liu_TNBC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Liu_TNBC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Borra%CC%80s_2024_Colorectal_cancer.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Borràs_2024_Colorectal_cancer.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Garner_2023_MAIT.h5ad" target="_blank" rel="noopener">Garner_2023.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Vorkas_2022_MAIT.h5ad?download=1" target="_blank" rel="noopener">Vorkas_2022_MAIT.h5ad</a></li> </ul>
New Ideas for Brain Modelling 4-Figure 1. Example mapping of cells presented as an image.
<p>The author has used this structure before in Greer (2016) and it is a type of entropy classifier. It attempts to reduce the error overall and is not so concerned with minimising individual associations. The paper Greer (2017) describes a classifier that is conjecturally more visual in nature than other types and it also uses a complete linking method. Instead of several levels of feature refactoring, it is a 1-level impression only. With the image classifier, each cell stores a count of every other cell it gets associated with, when averaging this can determine what cells are most similar to the pixel in question. Figure is an example of the clustering technique. If the top LHS grid is the first image to be mapped, then for cell A1, the other black cells are recorded as shown, with a count of 1. The count would then be incremented each time a cell is recorded again, for example, after the second image, cell A3 would lose a count. The idea of linking everything this way has now been used 3 times.</p>
Training material for the mapping and quantification of single-cell ATAC-seq 10X Datasets
<p>The data provided here is part of the Galaxy Training Network tutorial that analyses 10x genomics single-cell ATAC-seq data from the 10x platform. The original data is from 1k Peripheral Blood Mononuclear Cells (PBMCs) from a Healthy Donor.</p> <p>Due to time constraints during training, the datasets were subsampled to reads that map to chromosome 21 only.</p> <p>The 10x Genomics Datasets follow the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license.</p> <p>There is an additional count matrix in Anndata format created from full datasets.</p>
Source data for paper "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data"
<p>Sample-paired scRNA-seq and scATAC-seq data collected from human peripheral blood mononuclear cells with <em>Staphylococcus aureus</em><em> </em>infection. ScATAC-seq data collected from human peripheral blood mononuclear cells with <em>COVID-19</em> infection. </p>
Code repository for: Base editing mutagenesis maps functional alleles to tune human T cell activity
<p>Jupyter notebook and supplemental datasets required to created critical figures for the publication.</p>
Mapping of nanoparticles diffusion in cells
<p>We mapped the diffusion of particles in the cytoplasm. To do so, we electroporated QDs-SB particles in cells as shown in Debayle et al 2019. Here we were using RPE1 cells. We present the recording of three cells and the scripts that we used to generate some maps of diffusion in the cell.</p> <p>The scripts were made to use the output tracks from Slimfast 1.103e but can be adapted by changeing the beginning of the script.</p> <p>Some functions were found in Matlab Central and their corresponding license is in their respective folders.</p>
SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections (Revision data)
<p>The submitted dataset, correspond to the RAW (*.czi format) and analysis files of the <strong>revisions</strong> of manuscript "SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections", which has been processed for revision (2020-01-31) by PLOS Biology.</p> <p>The files are in *.zip format and the naming follows thenumbering of the manuscript figures, which will be found in bioRxiv.org (<strong>ID#: BIORXIV/2020/938571</strong>). Each *.zip files contains a document with the description of all the provided files.</p>
Other supporting data for our manuscript "Mapping the Single Cell Transcriptomic Response of Murine Diabetic Kidney Disease to Therapies"
<p>Other supplementary data for our paper "Mapping the Single Cell Transcriptomic Response of Murine Diabetic Kidney Disease to Therapies"</p>
Systematic spatio-temporal mapping reveals divergent cell death pathways in three mouse models of hereditary retinal degeneration
<p>Values for immuohistochemical analysis or enzymatic analysis of various markers theorised to have roles in retinal dystrophies in three models of mouse retinal degeneration with a control. Analysis has been broken down by marker, mouseline, age and region of the retina recorded from</p>
Mapping cis-regulatory chromatin contacts in neural cells links neuropsychiatric disorder risk variants to target genes
<p>ATAC-seq peaks are in narrowPeak format. RNA-seq results are organized according to cell type. The normalized RPKM is reported for each gene in GENCODE 19. All data was mapped to hg19.</p>
Dataset of "Mapping of Internal Ionic/Electronic Transient Dynamics in Current-Voltage Operation of Perovskite Solar Cells"
<p><span>This dataset supports the article: "Mapping of Internal Ionic/Electronic Transient Dynamics in Current-Voltage Operation of Perovskite Solar Cells" </span></p> <p><span>Raw data for the article "Mapping of Internal Ionic/Electronic Transient Dynamics in Current-Voltage Operation of Perovskite Solar Cells". For further details see the readme.txt file.</span></p>
Resolving Organoid Brain Region Identities by Mapping Single-Cell Genomic Data to Reference Atlases
<p>Data underlying the figures in the publication “Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases”, published in <em>Cell Stem Cell, </em><strong>2021</strong><em>, </em>28, 1148–1159.</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1934590921000655">https://www.sciencedirect.com/science/article/pii/S1934590921000655</a></p> <p>Table of contents:</p> <p><strong>1. patscreen_srt.rds</strong>; Numerical data for <em>Figure 7</em>: RNA-seq data of a patterning screen in organoids with an array of small molecules. The dataset is in the rds data format, which can be opened in the R programming language using the function `readRDS()`. Once opened, the dataset is a Seurat object (https://satijalab.org/seurat/) and contains both the transcript counts and the metadata for all samples in the screen. The raw data used in figure 7 was also deposited in ArrayExpress (<a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/</a>)</p>
High-resolution mapping of the period landscape reveals polymorphism in cell cycle frequency tuning
<p>Biological oscillators adapt to environmental changes with widely tunable frequencies, a property theoretical studies attributed to positive feedbacks. However, no experiments have tested this theory. Here, we created synthetic cells to independently tune the frequency and feedback strength of a cell-cycle oscillator, enabling continuous mapping of period landscape in response to network perturbations. We found that although inhibiting positive feedback of cyclin-dependent kinase (Cdk1) reduces the tunability, the reduction is not as significant as theoretically predicted, and the Cdk1-counteracting phosphatase, PP2A, provides additional machinery to ensure frequency regulation. Additionally, cells exhibit polymorphic responses to PP2A inhibition, showing a monomodal distribution of oscillatory cells at low or high PP2A inhibition or a bimodal distribution at both low and high inhibitions. We explained the polymorphism by a model of two interlinked bistable switches of Cdk1 and PP2A where cell-cycle oscillations exhibit two modes in the presence or absence of PP2A bistability.</p>
Operando magnetic resonance imaging for mapping of temperature and redox species in thermo-electrochemical cells
<p>Raw data for our publication "Operando magnetic resonance imaging for mapping of temperature and redox species in thermo-electrochemical cells", including imaging files (Paravision), ASCII, MATLAB and EC Lab data files organised corresponding to each figure in the paper and supplementary information.</p>
Supplementary Figures -B Cell Epitopes Mapping of the Vibrio cholera Toxins A, B, and P and an ELISA assay
<p>Figure S1- List of <em>Vibrio cholerae</em> [toxin A (P01555), B (P01556), and P (P29485)] synthetic peptides and position in the cellulose membrane of Spot synthesis.</p> <p>Figure S2- Purification of the peptide B (Vc/TxB-11) by HPLC using an XBridge BEH C18 (2.7 μ, 5 cm x 4.6 mm) column coupled to a Water HPLC system at a flow rate of 1.2 ml min<sup>-1 </sup>using mobile phases A [0.05% formic acid in water (18 MΩ × cm)] and B [(0.05% formic acid in ACN (acetonitrile acid)] (v/v) in water. Detection at 200-300 nm using a diode array.</p> <p>Figure S3- Mass spectrometry. The peptide B (Vc/TxB-11) was solubilized in deionized water to a final concentration of 10 µg/ml and then added formic acid to a final concentration of 0.1%. The mass spectrometer used was the Water UPLC model Acquity-I Class. The samples were electronically injected by the equipment at 1 µl/min. The range used for ion detection ranged from 1000-11500 m/z.</p>
Multi-Nucleic Acid Interaction Mapping in Single Cell (MUSIC) for simultanouse chromatin, RNA-chromatin and transcriptome mapping at single cell resolution
<p><a href="https://doi.org/10.1101/2023.06.28.546457">MUSIC manuscript:</a> Joint profiling of multiplex chromatin interactions, gene expression, and RNA-chromatin associations in single cells of the human brain.</p> <p>MUSIC-docker is the customized pipeline to process the raw fastq files to bam files: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/intro.html.</p> <p>Each bam file records the final output of our single cell mixed species analysis. Read name recods the cell barcode, complex barcode and I7 index. For each DNA/RNA read, read header contains <code>raw read name</code>| <code>BC3</code>_<code>BC2</code>_<code>BC1</code> - <code>10x barcode</code> # <code>UMI</code>. Details of the read name can be found here: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/step.html#demultiplexing.</p> <p>merge_DNA [RNA]_human [mouse].sort.bam: bam file of DNA [RNA] reads from the mix species library (H1+E14) that can uniquely mapped to the human [mouse] genome. PCR duplicates have been removed. </p> <p> </p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
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.