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11,105
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11,105 results for “single cells”
Supplementary data for: Detection of expressed mutations in acute myeloid leukemia cells using single cell RNA-sequencing
<p>Supplemental data for the publication:<br> Detection of expressed mutations in acute myeloid leukemia cells using single cell RNA-sequencing </p> <p>Contents: <br> - expression_matrices.tar - Gene/Barcode expression matrices from `cellranger count`<br> - *.seurat.rds - R object files with Seurat analyses and data structures for each sample<br> - scrna_mutations.tar.gz - copy of a git repository containing additional scripts and data - also hosted at <a href="https://github.com/genome/scrna_mutations">https://github.com/genome/scrna_mutations</a> (snapshot as of May 20, 2019)</p>
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>
Dataset of single-cell transcriptomic matrix of 10 human glioblastoma tissue
<p>Dataset of single-cell transcriptomic matrix of 10 human glioblastoma tissue. <span>scRNA-seq was performed using the droplet-based 10x Genomics platform</span><span> </span><span>(10x Genomics, Pleasanton, CA, USA)<span>. <span>GBM tissues for single-cell RNA sequencing (<a name="_Hlk147870629"></a>scRNA-seq)</span> were collected from patients admitted to Xiangya Hospital, Central South University.</span></span></p>
3D super-resolution datasets associated with the paper "Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet"
<p>3D single-molecule super-resolution datasets corresponding to reconstructions shown in <em>Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet</em> by Saliba & Gagliano, Gustavsson et. al.</p>
Single-cell and spatial transcriptomics delineate molecular traits and immunosuppressive landscape during histological progression of lung adenocarcinoma
<p>Two specimens of lung adenocarcinoma, each corresponding to the lepidic and solid histologic patterns as confirmed through histologic scrutiny, were procured in accordance with standard surgical protocols. These specimens underwent a process of formalin fixation and were subsequently encapsulated within paraffin-embedded tissue blocks. The specimens were then sectioned and subjected to hematoxylin and eosin (H&E) staining to facilitate subsequent imaging at a resolution of 40x (equivalent to 0.25 micron/pixel) via the use of Aperio GT450 scanners. The tissue slides were then conveyed to the Genomics core, where following the decoverslipping of the tissue, the Visium CytAssist device was employed to transfer transcriptomic probes from the original glass slides to capture areas on Visium slides measuring 11mm x 11mm. Comprehensive transcriptomic profiling was achieved post mRNA permeabilization, through poly(A) capture and probe hybridization. The resultant libraries were sequenced utilizing the Illumina Novaseq 6000, using paired-end sequencing with a read length of 150 base pairs.</p>
scPerturb Single-Cell Perturbation Data: RNA and protein h5ad files
<p>Collection of h5ad files for RNA and protein single-cell perturbation datasets on scPerturb.org.</p> <p>For the associated publication, see <a href="https://www.nature.com/articles/s41592-023-02144-y">https://www.nature.com/articles/s41592-023-02144-y</a>.</p> <p>H5ad files were created using scanpy 1.9.1, using gzip compression.</p> <p> </p>
FVLLMONTI Application examples: Single reconfigurable VNWFET TCAD simulation and Reference logic cell DTCO flow
<p>This data set presents two application examples collecting and highlighting some of the key components to the TCAD, compact modelling and DTCO approaches carried out as part of the FVLLMONTI project in relation to Deliverable D3.2.</p> <p>First we present an example that demonstrates the functionality of the U-shaped reconfigurable transistor, switching the device behaviour between NMOS and PMOS using the Program Gate and giving the transistor characteristics of those devices.</p> <p>Secondly, this data set gives an introduction into the FVLLMONTI DTCO approach. The provided simulation project shows a reference DTCO flow from layout to library characteristics for previously delivered physical logic cell designs. The DTCO flow provides insight into logic cell parasitics, netlist for 3rd party SPICE analysis, and KPI determination and characterization.</p> <p>Both simulation projects can be seen as template for similar setups employed to overcome the challenges and answer the detailed questions faced in investigating a newly developed semiconductor technology such as the FVLLMONTI stack.</p>
Processed data for "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER"
<p>This dataset contains the processed data for the figures and analyses performed in the publication "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER" from Lucas O., Ward S., Zaidi R., Bunkum A., ..., Zaccaria S. Nature genetics, in press, 2024.</p> <p>The processed data are separated into three respective folders:</p> <ul> <li>GT contains all the data related to the analysis of the generated ground truth datasets;</li> <li>NSCLC contains all the data related to the analysis of the NSCLC dataset;</li> <li>TNBC_HGSC contains all the data related to the analysis of the TNBC and HGSC datasets. </li> </ul>
Nonlinear methods for dimensionality reduction and clustering of bacterial single-cell sequencing data - intermediate data and figures (MSc thesis)
<p>Data, intermediate results and figures for analyses of my master's thesis in biostatistics at LMU Munich. I took a look on how to use Nonlinear Matrix Decomposition (NMD) (<a href="https://doi.org/10.1137/21M1405769">Saul, L., 2022</a>) in the context of bacterial scRNA-seq analysis (Heumos, L., et. al. 2023), replacing Principal Component Analysis in the optimized workflow, as outlined in Ostner, J. (2024).</p> <p>My thesis was structured along the following objectives:</p> <ul> <li>implement the algorithms from <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> in the Python module <a href="https://github.com/flatironinstitute/nomad/">nomad</a> in cooperation with <a href="https://www.simonsfoundation.org/flatiron/" rel="nofollow">Flatiron Institute</a></li> <li>code for the simulation study of the algorithms in <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> with varying sparsity can be found in <code>/simulation</code></li> <li>apply NMD in the context of the BacSC workflow (<a href="https://www.biorxiv.org/content/10.1101/2024.06.22.600071v1">Ostner, J., et. al. (2024)</a>) on raw and normalized counts (found in <code>/application/analysis</code>), also for manually set number of latent dimensions</li> <li>explore NMD's potential for imputation of <a href="https://www.nature.com/articles/s41467-021-27729-z" rel="nofollow">sampling zeros</a> (check <code>/application/NMD_zero_imputation /</code>)</li> <li>potential of Poisson-Hurdle model-based clustering (<a href="https://academic.oup.com/bioinformatics/article/39/1/btac782/6873739">Qiao, Z., et. al. (2023)</a>) for scRNA-seq (<code>/application/poisson_hurdle</code>).</li> </ul>
Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population
<p>This record contains all the fully processed single cell RNA sequencing objects included in the paper Nyirenda et al (2024) '<em>Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population</em>'. </p> <h3>Abstract</h3> <p><em>Postmortem single-cell studies have transformed understanding of lower respiratory tract diseases (LRTD) including Covid19, but there is minimal data from African settings where HIV, malaria and other environmental exposures may affect disease pathobiology and treatment targets. We used histology and high-dimensional imaging to characterise fatal lung disease in Malawian adults with (n=9) and without (n=7) Covid19, and generated single-cell transcriptomics data from lung, blood and nasal cells. Data integration with other cohorts showed a conserved Covid19 histopathological signature, driven by contrasting immune and inflammatory mechanisms: in USA, European and Asian cohorts by type I/III interferon responses, particularly in blood-derived monocytes, in the Malawi cohort, by response to interferon-gamma (IFN-γ) in lung-resident macrophages. HIV status had minimal impact on histology or immunopathology. Our study provides a data resource and highlights the importance of studying the cellular mechanisms of disease in underrepresented populations, indicating shared and distinct targets for treatment. </em></p> <h3>Figure 3</h3> <p>COSMIC_Lung_Atlas.h5ad</p> <p>COSMIC_Lung_Stromal_Atlas.h5ad</p> <p>COSMIC_Lung_Immune_Atlas.h5ad</p> <h3>Figure 4</h3> <p>COSMIC_HCLA_Integration.rds</p> <h3>Figure 5</h3> <p>COSMIC_Nasal_Atlas.h5ad</p> <p>COSMIC_Blood_Atlas.h5ad</p> <h3>Total Post-mortem COVID-19 Lung Autopsy Papers Integration</h3> <p>This data object was generated whereby all publicly available fatal COVID-19 lung single cell cohorts were integrated together. We include this analysis to show that the observed interferon responses detailed in the paper are preserved in not only the HLCA, but also existing post-mortem lung cohorts. The codebase for this additional analysis can be found here: <strong><a href="https://github.com/olympiahardy/Malawi_Integration">https://github.com/olympiahardy/Malawi_Integration</a> </strong></p> <p>COSMIC_Delorey_Melms_Integration.rds</p> <p> </p> <p> </p> <h3>Imaging Mass Cytometry Analysis</h3> <p>This Zenodo record can be accessed using the DOI here: <strong><a href="10.5281/zenodo.13899297">10.5281/zenodo.13899297</a> </strong>. The GitHub repository can be accessed here:<strong> <a href="https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID">https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID</a></strong></p> <p> </p>
10X Single Cell RNA-Seq On WT and Mutation mouse models
<p>Four four-week-old mice, consisting of wild type, Pten mutation, MAP3K3 mutation, and MAP3K3+Pten mutation, were sampled at 4 weeks of age. Endothelial cells were enriched using CD31 magnetic beads, followed by 10X single-cell RNA sequencing.</p>
Code and data of "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"
<p>Code and data to reproduce the analyses and figures presented in "Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA"</p>
Single-Cell Transcriptomics Reveals a Heterogeneous Cellular Response to BK Virus Infection
<p>The files are the Indrops count matrices for BKV and Mock samples corresponding to the 8 experiments and samples described in the Bioproject https://www.ncbi.nlm.nih.gov/bioproject/PRJNA715178</p> <p> </p>
Raw data to: "Persistent RNA virus infection is short-lived at the single cell level but leaves transcriptomic footprints"
<p>Raw data underlying the publication by Reuther and Martin et al. entitled "Persistent RNA virus infection is short-lived at the single cell level but leaves transcriptomic footprints"</p>
Text-fig. 13. Scanning electron microscope (SEM) images of pollen or spore clump with pollen grains or spores of unknown affinity that occur separately or adhering together in dyads, triads and tetrads; Torres Vedras locality, Portugal. a) Clump of pollen or spores that yielded the pollen or spores in this Text-figure; b–f) Grains adhering together in twos, threes or fours (b–e) or occurring singly and apparently with a proximal trilete mark (f); note that the adhering grains are connected by a smooth bandlike covering, perhaps remains of the microspore mother cell; note also abundant orbicules of various sizes among and over the grains. Specimen, TV44-S148149. Scale bars 300 Μm (a), 30 Μm (b–f). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 13. Scanning electron microscope (SEM) images of pollen or spore clump with pollen grains or spores of unknown affinity that occur separately or adhering together in dyads, triads and tetrads; Torres Vedras locality, Portugal. a) Clump of pollen or spores that yielded the pollen or spores in this Text-figure; b–f) Grains adhering together in twos, threes or fours (b–e) or occurring singly and apparently with a proximal trilete mark (f); note that the adhering grains are connected by a smooth bandlike covering, perhaps remains of the microspore mother cell; note also abundant orbicules of various sizes among and over the grains. Specimen, TV44-S148149. Scale bars 300 Μm (a), 30 Μm (b–f).
Imaging Mass Cytometry Images (APP1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Four µm-thick sections were cut from the APP1 FFPE block with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm<sup>2</sup>. These images were used to select the ROI in each slide. For APP1, a one mm<sup>2</sup> ROI containing a lymphoid follicle in its whole depth alongside a portion of lamina propria and of epithelium was selected. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</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.