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278 results for “cytometry”
Exploratory mass cytometry analysis reveals immunophenotypes of cancer treatment-related pneumonitis
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Viscoelastic properties of suspended cells measured with shear flow deformation cytometry
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Mass cytometry immunophenotyping data of two-week-old mouse pups' spleens depleted of maternal cells
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Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose
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Highly-multiplexed mass cytometry screen of human bone marrow hematopoietic stem and progenitor cells
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CellCognize: a neural network pipeline for cell type classification from flow cytometry data
<p>Readme file content</p> <p>The files stored here contain the following material as supplementary and source data for the publication</p> <p>Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data</p> <p>Birge D. Özel Duygan1, Noushin Hadadi1, Ambrin Farizah Bab1, Markus Seyfried2, Jan R. van der Meer1</p> <p>1 Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland<br> 2 Biotechnology Department, Firmenich SA, Geneva, Switzerland</p> <p>%%%%%%%<br> Flow cytometry data<br> %%%%%%</p> <p>FCM_files:</p> <p>.mat files with cleaned data as described in the supplementary methods section</p> <p>Ecoli_lakewater: raw FCM data (in .csv format) of E. coli cultures and E. coli cultures mixed to lakewater</p> <p>MIX_experiment_ACL_AJH_PVR: raw FCM data (in .csv format) of the synthetic three culture experiment with E. coli, A. johnsonii and P. veronii, as described in the main text and SI methods.</p> <p>PHE_OCT_enrichments: raw FCM data (in .csv format) of the phenol and 1-octanol enrichments and the 1-octanol isolates, as described in the main text and SI methods.</p> <p>%%%%%%%<br> Neural network data<br> %%%%%%</p> <p>NN_file_example: three ANN functions, to be used in conjunction with the SI methods section</p> <p>Supplementary_Methods.docx: Detailed description on the construction, usage and scripts for the ANN. To be used in conjunction with the Flow Cytometry data</p> <p>%%%%%%%<br> 16S sequencing data<br> %%%%%%</p> <p>raw fastq- files of the sample reads of the 1-octanol and phenol enrichments described in the paper, at t=0 and t=3d, each in triplicates, forward and reverse.</p> <p>Readme_16S_sequence_files.txt: sample description of the read files</p>
Imaging Mass Cytometry: p173
<p>Imaging Mass Cytometry: p173</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>
Imaging Mass Cytometry: p165
<p>Imaging Mass Cytometry: p165</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>
Imaging Mass Cytometry: p176
<p>Imaging Mass Cytometry: p176</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>
Discrete flow cytometry from the Gradients 2017 cruise using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations and heterotrophic bacteria from discrete flow cytometry data collected during the Gradients 2017 (Gradients 2/MGL1704) oceanographic research cruise in the Northeast Pacific Ocean. The analysis includes cell abundance, forward light scatter, and pigment fluorescence of individual cells, including bacteria, picoeukaryotes and the cyanobacteria Prochlorococcus and Synechococcus. Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Discrete flow cytometry from the Gradients 2016 cruise using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations and heterotrophic bacteria from discrete flow cytometry data collected during the Gradients 2016 (Gradients 1/KOK1606) oceanographic research cruise in the Northeast Pacific Ocean. The analysis includes cell abundance, forward light scatter, and pigment fluorescence of individual cells, including bacteria, picoeukaryotes, and the cyanobacteria Prochlorococcus and Synechococcus. Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Raw single cell mass cytometry data from breast cancer patient derived xenografts
<p>Raw single cell mass cytometry data from breast cancer patient derived xenografts.</p> <p>Data is organised in an expressioset R object</p>
Data from: The ORFIUS complex regulates ORC2 localization at replication origins (Flow cytometry)
<p>In this work, we use bromodeoxyuridine (BrdU)/propidium iodide cell cycle flow cytometry profiling to demonstrate that treatment of ovarian cancer cell lines with gene specific siRNAs, shRNAs, or drugs leads to minimal or no cell cycle arrest which might otherwise influence observed phenotypes. All raw FCS files for flow cytometry data presented in Figures 4E, 6G, S3A, S4C, S5B, S5F, S8C, S8D, S8E, S9C, S10C, and S12B are available here.</p>
Mass cytometry data for "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma"
<p>Raw suspension mass cytometry data supporting the publication "High-dimensional mass cytometry reveals stemness state heterogeneity in pancreatic ductal adenocarcinoma".</p>
Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning
<p>In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning".</p> <p>These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification. </p> <p>The datasets are as follows:</p> <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> <p>We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH</p> <p> </p>
Imaging Mass cytometry of pediatric liver biopsies in patients with AHUO and possible PASC
<p>IMC Dataset as described in Roettele et al. "<strong><span>Characteristic immune cell interactions in livers of children with acute hepatitis revealed by spatial single-cell analysis identify a possible </span></strong><strong><span>post-acute sequel of COVID-19"</span></strong><strong><span> </span></strong></p>
UVAE toy data (flow cytometry sample)
<p>A sample of flow cytometry data containing 3 panels of 3 batches with artificial batch effects.</p>
3D tissue cytometry analysis files for, "An atlas of healthy and injured cell states and niches in the human kidney"
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, "An atlas of healthy and injured cell states and niches in the human kidney". </p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the preprint doi: 10.1101/2021.07.28.454201. This collection includes the individual tissue specimens, XX-XXXX.zip, further described in detail in "Supplementary Table 2. 3D imaging and spatial transcriptomic experiments." </p> <p>2) neighborhoods by specimen as indicated above and calculated by Volumetric Tissue Exploration and Analysis (VTEA) for a radius of 50 voxels(accounting for anisotropy of voxels) or ~25 um as described in the methods of the preprint, doi: 10.1101/2021.07.28.454201.</p> <p>3) R environment file (.RData as a .zip file) for regenerating analysis from code at: https://github.com/KPMP/Cell-State-Atlas-2022</p> <p>Contents of XX-XXXX.zip files:</p> <p>1) maximum projections as used in the manuscript (all archived at kpmp.org)<br> 2) .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/)<br> 3) .csv file including all the segmented objects and associated measurements<br> 4) .csv file including the neighborhood analysis results also found in the combined .zip file<br> 5) folder of ImageJ/FIJI folder which includes the expert ROIs and the pixel-wise consensus of ROIs drawn by experts used in cell classification strategy outlined in the methods<br> 6) folder of VTEA gate files and images of gating .png files used to label objects and used in cell classification strategy outlined in the methods of doi: 10.1101/2021.07.28.454201</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: 10.1101/2021.07.28.454201.</p> <p> </p> <p> </p>
Flow cytometry data of NEREA Augmented Observatory
<p>FCM sample were processed using a Becton-Dickinson FACSVerse flow cytometer, equipped with standard laser (488 nm) and filter set. Volumes were estimated using TruCount Beads run before every batch run or estimated from the volumetric count of the instrument.</p> <p> </p>
Imaging mass cytometry data from IDH wildtype glioblastomas
<p>Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We now have limited knowledge of the tumor microenvironment (TME) determinants that influence the localization and the functions of the diverse myeloid cell populations in GBM. In this dataset, we have used imaging mass cytometry to identify and map the various myeloid populations in the human GBM tumor microenvironment (TME) using known markers for myeloid and neoplastic cells in GBM. Our analyses of these data found that different myeloid populations had distinct and reproducible compartmentalization patterns in the GBM TME that were driven by tissue hypoxia and varied homotypic and heterotypic cellular interactions. This dataset consists of imaging mass cytometry data (16-bit TIFF images) for 8 glioblastomas and 1 tonsil sourced from the Salford Royal NHS Trust Biobank.</p>
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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.