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6,040 results for “Single-cell”
Single-Cell Autism data stored as sce object
<p>The raw autism dataset is from UCSC Cell Browser Dataset, Autism section (<a href="https://cells.ucsc.edu/">https://cells.ucsc.edu</a>). It is stored as SingleCellExperiment object for further usage. </p>
Data of: Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts
<p>These files are results obtained in<br><span><span><span><span>Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts</span></span></span></span><br>by Yoshito Hirata, Arisa H. Oda, Chie Motono, Masanori Shiro & Kunihiro Ohta.</p> <p>There are 33 files for the corresponding each reconstruction of three-dimensional chromosomone structures<br>for each cell.<br>There are 3D structures for 15 GM cells and 18 PBMC cells, which are obtained from the single cell Hi-C data of Tan et al. Science (2018).</p> <p>For each file, there are 6 columns:<br>The first column corresponds to the allele (0: maternal, 1: paternal)<br>The second column corresponds to the chromosome (1-22: chromosome's number, 23: X, 24: Y)<br>The third column corrsponds to the base point.<br>The fourth column, the fifth column and the sixth column correspond to x-, y-, and z-axes of our reconstruction.</p>
Data files: Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures
<p>Scripts, preprocessed count matrices, single-cell data objects, and generated data (tables and .rds files) from the scRNA-seq analyses performed in <strong>“Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures".</strong></p> <p> </p>
Image data for bioRxiv article named: mtFociCounter - Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci
<p>Raw imaging data to reproduce and test the findings of the bioRxiv article: <strong>mtFociCounter </strong>- Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci. It contains data from three imaging days and 2 or three technical replicates on each day.</p> <p> </p>
Dataset for: Phenotyping single-cell motility in microfluidic confinement
<p>Associated dataset and simulation codes for the publication "Phenotyping single-cell motility in microfluidic confinement" (2022), by Samuel A. Bentley, Hannah Laeverenz-Schlogelhofer, Vasileios Anagnostidis, Jan Cammann, Marco G. Mazza, Fabrice Gielen, Kirsty Y. Wan. </p>
Phertilizer: growing a clonal tree from single-cell DNA sequencing data of tumors
<p>The is the supplementary data repository for the simulation input data for Phertilizer: growing a clonal tree from single-cell DNA sequencing data of tumors.</p>
Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)
<p>Manganese porphyrins reportedly exhibit synergic effects when combined with irradiation. However, an in-depth understanding of intratumoral heterogeneity and immune pathways, as affected by Mn porphyrins, remains limited. Here, we explored the mechanisms underlying immunomodulation of a clinical candidate, MnTnBuOE-2-PyP<sup>5+</sup> (BMX-001, MnBuOE), using single-cell analysis in murine carcinoma<em> </em>model. Mice bearing 4T1 tumors were divided into 4 groups: control, MnBuOE, radiotherapy (RT), combined MnBuOE, and radiotherapy (MnBuOE/RT). In epithelial cells, epithelial-mesenchymal transition, TNF-α signaling via NF-кB, angiogenesis, and hypoxia-related genes were significantly downregulated in the MnBuOE/RT compared to the RT. All subtypes of cancer-associated fibroblasts (CAFs) were reduced in MnBuOE and MnBuOE/RT. Inhibitory receptor-ligand interactions, in which epithelial cells and CAFs interacted with CD8+ T cells, were significantly lower in the MnBuOE/RT than in the RT. Trajectory analysis showed that DC maturation-associated markers were increased in MnBuOE/RT. M1 macrophages were significantly increased in the MnBuOE/RT compared to the RT, whereas myeloid-derived suppressor cells were decreased. CellChat analysis showed that the number of cell-cell communications was the lowest in the MnBuOE/RT. Our study is the first to provide evidence for the combined radiotherapy with a novel Mn porphyrin clinical candidate, BMX-001 from the perspective of each cell type within the tumor microenvironment.</p>
Optimized summary-statistic-based single-cell meta-analysis. Input files
<p>This dataset contains information about the input files used in the Optimized summary-statistic-based single-cell meta-analysis research project. </p> <p> </p>
Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provides both common and discordant biological insights
<p>The datasets generated by <em>Cellranger </em>for all 24 samples (.h5 format).<br><br></p>
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets
<div> <div> <div> <div> <p>This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis.</p> <h3>Datasets</h3> <p>We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations:</p> <ol> <li> <p><strong>Cell Line (CL)</strong>:</p> <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> <p><strong>Human Dendritic Cells (HDC)</strong>:</p> <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> <p><strong>Human Pancreas (HP)</strong>:</p> <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> <p><strong>Mouse Brain (MB)</strong>:</p> <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> <p><strong>Mouse Cell Atlas (MCA)</strong>:</p> <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> <p><strong>Mouse Hematopoietic Stem and Progenitor Cells (MHSPC)</strong>:</p> <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> <p><strong>Mouse Retina (MR)</strong>:</p> <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> <p><strong>PBMC (human Peripheral Blood Mononuclear Cell)</strong>:</p> <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> <p><strong>Usage Notes</strong>: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file.</p> <p><strong>Keywords</strong>: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics</p> <p><strong>Contact</strong>: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a></p> <p><strong>License</strong>: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Single-cell proteo-transcriptomic profiling reveals altered characteristics of stem and progenitor cells in patients receiving cytoreductive hydroxyurea in early-phase chronic myeloid leukemia
<p>This repository contains CITE-seq data generated from CML stem and progenitor cells before and after hydroxyurea treatment using the BD Rhapsody Single-Cell Analysis System. </p> <p><strong><br>File descriptions:</strong></p> <p>1. RSEC-adjusted UMI count files generated using the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell.csv</li> </ul> <p>2. RSEC-adjusted UMI counts for cells remaining after cell quality filtering using SeqGeq software (genes expressed vs library size):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell_postQC.csv</li> </ul> <p>3. Sample tag (sample of origin) calls for each putative cell, outputted by the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1). </p> <ul> <li>CartridgeS1_Sample_Tag_Calls.csv</li> </ul> <p> </p>
FIB-SEM tomography datasets of entire single-cell human monocyte derived macrophages
<p>FIB-SEM tomography was used to determine the absolute number of internalized gold nanoparticles in GM-CSF and M-CSF human monocyte-derived macrophages at the single-cell level. All experiments were performed using a Thermo Scientific Scios 2 Dual Beam microscope (Thermo Fisher Scientific, Waltham, MA, USA). A selected cell was protected with a platinum (Pt) layer of 1 µm thickness (30 kV and current of 1 nA). Then, a trench of 5 µm in depth was milled on the front face and each side of the cell of interest using the ion beam at 7 nA current. Finally, the front face of the volume of interest was polished with an ion beam current of 1 nA until the beginning of the cell was visible to optimize the focus and contrast. FIB-SEM tomography of the whole cell was performed using the FEI Slice and View software (Thermo Fisher Scientific, Waltham, MA, USA, version 4.1). The electron beam acceleration voltage was set to 5 kV, the current to 0.4 nA, the resolution to 1536 × 1024 pixels, and the dwell time to 30 µs. Images were acquired in immersion mode with the backscattered electron detector, yielding clear signals from AuNPs due to the detector's sensitivity to backscattered electrons, which correlates with the atomic number of Au (Z = 79). The ion beam operating with a current of 1 nA current at 30 kV was used to slice through the cell of interest at an interval of 18 nm and a depth of 5 µm. The datasets available on the repository have been aligned and cropped.</p>
Noninvasive detection of macrophage activation with single-cell resolution through machine learning
<p>Data related to the article "Noninvasive detection of macrophage activation with<br> single-cell resolution through machine learning".</p> <p>The package contains 2 folders:<br> - RawData: This package contains raw data and examples of processing to extract the<br> variables employed to train and assess the models.<br> - Variables: This package contains the extracted data from the various experiments<br> showed in the article.</p>
Data for: "A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death"
<p>Data related to the publication Murschhauser <em>et al.</em>: <a href="https://doi.org/10.1038/s42003-019-0282-0">A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death</a>. It contains fluorescence time traces of single cells marked with cell-event markers and observed by time-lapse microscopy. The cells were treated with nanoparticles at different doses (NP25 and NP100), with staurosporine (sts) or were left untreated for control (ctrl). See the above-mentioned publication for more details.</p> <p>The format of the data is described below.</p> <p>The file <code>Data_A549.zip</code> contains data measured with A549 cells, and the file <code>Data_Huh7.zip</code> contains data measured with Huh7 cells. Both files have the same structure. Each file contains the directories <code>Raw</code> and <code>Fitted</code> as well as a checksum file. The <code>Raw</code> directory contains single-cell fluorescence time courses as obtained by time-lapse microscopy. The <code>Fitted</code> directory contains the results of fitting model functions as well as properties of identified events, such as event times. The checksum file contains SHA256 checksums of all files within these directories and can be used to check file integrity.</p> <p>Both directories contain measurement directories. Each measurement directory contains the data corresponding to one experiment. The name of the measurement directory is the measurement identifier. Each measurement directory contains condition directories. Each condition directory contains data corresponding to one condition measured in the measurement and is named after the condition. Each condition directory contains marker directories. They are named after the fluorescence markers measured and contain files with single-cell data corresponding to the respective markers.</p> <p>The names of those files consist of multiple parts separated by underscores. The first two parts identify a position of the microscope. Since pairs of markers were measured, each position is present in two marker directories. The third part is the measurement identifier. The other parts will be described below.</p> <p>The <code>Raw</code> directory contains only CSV files with the raw fluorescence time courses. The filenames contain no other parts and have the suffix “.txt”. The first row of each CSV file is the time (in units of 10 minutes), and the other rows are the fluorescence time courses of the cells observed at the corresponding position (in arbitrary units). Each file in the <code>Raw</code> directory corresponds to a group of files in the <code>Fitted</code> directory.</p> <p>The <code>Fitted</code> directory contains three types of CSV files. Their names have “ALL” as fourth part, a session identifier as sixth part and the suffix “.csv”. The fifth part indicates the type of file and is one of the following:</p> <ul> <li>“PARAMS” indicates the estimated values for the model parameters. Each row stands for one cell and each column for a parameter of the model function fitted to the data. The model functions are published with the <a href="https://doi.org/10.5281/zenodo.1418465">fitting software</a>.</li> <li>“SIMULATED” indicates the fitted traces. The traces are calculated using the model functions and the estimated parameters. The format is the same as for the raw traces, but the time is in units of hours and has a higher resolution.</li> <li>“STATE” indicates additional information extracted from the fitted traces. Each row stands for a cell and each column for a property. The first column is the number of the cell. The second column is the event time found (in hours); non-finite values indicate that no event time was found. The third and fourth columns contain the absolute and relative amplitude of the trace, respectively. The fifth column is the logarithmic likelihood of the best fit. The sixth column indicates an algorithm used for postprocessing, and the seventh column indicates the trace slope at the event. See the fitting software for details.</li> </ul> <p> </p>
Single-cell -omics datasets containing a trajectory
<p>Contains datasets to compare trajectory inference methods. See <a href="https://benchmark.dynverse.org">https://benchmark.dynverse.org</a></p> <p>These files can be opened in R using `readRDS` .</p> <p>Each file is a list containing at least:</p> <ul> <li>expression and counts: filtered log<sub>2</sub> normalised expression files and raw counts</li> <li>milestone_network, milestone_percentages and divergence_regions: the trajectory model</li> </ul>
Inferelator Saccharomyces Cerevisiae Single-Cell Data Set
<p>This data is associated with the Inferelator package. It has an expression data set (103118_SS_Data.tsv.gz), which is a [Cells x Genes] TSV file which has 5 included metadata columns [Genotype, Genotype_Group, Replicate, Condition, tenXBarcode]. It also contains a prior data matrix generated from the YEASTRACT database (YEASTRACT_Both_20181118.tsv), a gold standard derived from the YEASTRACT database (gold_standard.tsv), a list of transcription factors (tf_names_restrict.tsv), and a list of protein-coding genes (orfs.tsv). It was initially used in Jackson, C.A., Castro, D.M., Saldi, G.-A., Bonneau, R., and Gresham, D. (2019). Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments. BioRxiv 581678.</p>
A comparison of automatic cell identification methods for single-cell RNA-sequencing data
<p>Benchmark datasets used to evaluate the performance of 22 classifiers for cell type classification for scRNA-seq data</p>
Processed Hi-C contact matrices for "Single-cell DNA replication profiling identifies spatiotemporal developmental dynamics of chromosome organization"
<p>Processed Hi-C interaction matrices (iterative correction) saved in .hic format (40kb bins).</p> <p>.hic files were generated by juicer pipeline using processed Hi-C interaction matrices.</p> <p>Only <em>cis </em>interactions were available.</p> <p>To extract the data, please see </p> <p>https://github.com/aidenlab/juicer/wiki/Data-Extraction</p>
A Galaxy-based training resource for single-cell RNA-seq quality control and analyses
<p>This is the tutorial data for the 'Single-cell quality control with scater' tutorial on the Galaxy Training Network. The data is the same dataset that is used as the inbuilt example dataset within scater, but has been implemented as individual files.</p>
Processed data for "Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses"
<p>tar.gz of processed data in the form of compressed R files (rds) of SingleCellExperiment (<a href="https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html">https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html</a>) objects and a metadata csv for the data in the publication <em>Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses </em>(O'Flanagan et al. 2019).</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.