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ShareScore release 0.9.0
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68 results for “mass cytometry”
Mass cytometry and integration sequencing data and code from "Quantification of intrinsic regulatory factors refines human hematopoietic progenitor definitions and reveals early erythroid lineage priming"
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Inter and Intra-tumour Heterogeneity of Paediatric-type Diffuse High-Grade Glioma Revealed by Single-Cell Mass Cytometry
<p>raw data (fcs file) from mass cytometry experiments </p>
Imaging mass cytometry data: Diffuse large B-cell lymphoma lymph node section
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Imaging mass cytometry data: Head and neck squamous cell carcinoma tissue section
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Deep phenotyping by mass cytometry and single cell RNA-sequencing reveals LYN regulated signaling profiles underlying monocyte subset heterogeneity and lifespan
GEO Series GSE146216. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry
GEO Series GSE120446. Homo sapiens. 33 samples. Type: Expression profiling by high throughput sequencing.
Single-cell mass cytometry reveals the impact of graphene nanomaterials with human primary immune cells
GEO Series GSE99929. Homo sapiens. 18 samples. Type: Expression profiling by array.
Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [bulk]
GEO Series GSE120444. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level
GEO Series GSE262591. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Imaging Mass Cytometry: p161
<p>Imaging Mass Cytometry: p161</p> <p>Raw acquisitions for the paper:</p> <p><strong>A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids</strong></p> <p> Vito RT Zanotelli<br> Matthias Leutenegger<br> Xiao‐Kang Lun<br> Fanny Georgi<br> Natalie de Souza<br> Bernd Bodenmiller</p> <p><em>Mol Syst Biol. (2020) 16: e9798</em><br> <a href="https://doi.org/10.15252/msb.20209798">https://doi.org/10.15252/msb.20209798</a></p> <p><em>Please cite this article if you re-use any of the data or code.</em></p>
RUCova: Removal of Unwanted Covariance in mass cytometry data
<p>Mass cytometry data, cell area measurement data, and R Markdown reports to reproduce the figures of our publication and understand the application of RUCova.</p> <p>Raw data were saved post de-convolution and spillover-compensation. Gates for singlets and non-dead cells (low_Pt) are included as logical columns and should be applied prior to usage.</p> <p>Mass cytometry data sets:</p> <ul> <li>data1_HNSCC.csv -> Data from 10 Head-and-neck squamous cell carcinoma (HNSCC) cell lines in control condition and 48 h after irradiation with 10 Gy. <ul> <li>marker_category_HNSCC.csv -> table for each marker in the mass cytometry panel and category.</li> </ul> </li> <li>data2_FACS.csv -> Data from Cal33 cell line under different stimuli and FACS-sorted by size (small and large). <ul> <li>size_gates.png -> Image depicting the FACS sorting.</li> </ul> </li> <li>data3_ru.csv -> Data from Cal33 cell line with Ruthenium staining (Rapsomaniki et. al. 2018, doi: <span>10.1038/s41467-018-03005-5</span><span>).</span></li> <li>data_ruthenium_rapsomaniki.csv (data from Rapsomaniki et. al. 2018) </li> </ul> <p>Cell area measurement data:</p> <ul> <li>cell_area_HNSCC.csv -> Measurements of the cell area [µm^2] via microscopy images for the 10 HNSCC in control condition and after irradiation with 10 Gy.</li> </ul> <p>R Markdown reports:</p> <ul> <li>analysis.Rmd </li> <li>analysis.html</li> </ul>
Flow cytometry data of intestinal immune cells related to Seumetry toolkit for analysis of high-dimensional flow and mass cytometry data
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Mass cytometry data of immune cells in mice that had experienced explosions.
<p>Mass cytometry was used to assess the expression of immune markers and intracellular proteins in immune cells collected from mice that had experienced explosions. Following mass cytometry detection, .fcs files were generated. The raw data were packaged and uploaded.</p>
Results of IMC-Denoise: a content aware pipeline to enhance Imaging Mass Cytometry
<ul> <li>Generated training sets: <ul> <li>training_set_supp_table6.zip</li> <li>training_set_supp_table7.zip</li> <li>training_set_supp_table8.zip</li> <li>training_set_supp_table9.zip</li> <li>training_set_supp_table10.zip</li> <li>training_set_supp_table11.zip</li> </ul> </li> <li>Trained weights of experimental data: <ul> <li>training_result_supp_table7.zip</li> <li>training_result_supp_table8.zip</li> <li>training_result_supp_table9.zip</li> <li>training_result_supp_table10.zip</li> <li>training_result_supp_table11.zip</li> </ul> </li> <li>Simulation results: <ul> <li>Simulation_results.zip</li> </ul> </li> <li>Experimental results: <ul> <li>Human_bone_marrow_IMC_denoising_results.zip</li> <li>Human_breast_cancer_IMC_denoising_results.zip</li> <li>Human_pancreatic_cancer_IMC_denoising_results.zip</li> <li>MIBI_denoising_results.zip</li> </ul> </li> <li>Ilastik-processed or manual-labeled results: <ul> <li>DIMR_Ilastik_results.zip</li> <li>background_removal_results.zip</li> <li>manual_annotated_public_datasets.zip</li> </ul> </li> <li>Extracted single cell data and the corresponding phenotyping results from both DIMR and DeepSNiF-based segmented cell masks: <ul> <li>Single_cell_analysis.zip</li> </ul> </li> </ul>
Optimization of Time-of-Flight Mass Cytometry (CyTOF) Analysis for Evaluation of Immune Changes Following Surgery
ClinicalTrials.gov study NCT01882569. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Searching for Predictive Biomarkers of Efficacy in Small Cell Lung Cancer Patients Treated With Chemotherapy-immunotherapy Combination Using Imaging Mass Cytometry (HYPE)
ClinicalTrials.gov study NCT06558903. IPD Sharing: YES. Countries: 1. Publications: 0.
Understanding Ozanimod's MOA Via Mass Cytometry in Ulcerative Colitis
ClinicalTrials.gov study NCT06311123. IPD Sharing: YES. Countries: 1. Publications: 0.
Investigation of Novel Immunological Biomarkers by Mass Cytometry in Patients With Early Multiple Sclerosis (CISCO)
ClinicalTrials.gov study NCT04510350. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [scRNA]
GEO Series GSE120221. Homo sapiens. 25 samples. Type: Expression profiling by high throughput sequencing.
Identification by Cytometry by Mass of Predictive Immunological Profiles of Answer to Treatmentby Biotherapics for Patients With Crohn's Disease
ClinicalTrials.gov study NCT03712826. 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.