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Dataset results
485 results for “ScRNAseq”
Urbanus and Cosgrove et al. Nature Communications (2023) - CellRanger outputs for scRNAseq experiments in Figures 2,5, and 6
<p>Attached are the outputs of the cell ranger pipeline for 10x 3' scRNAseq of HSPCs. For any questions regarding this dataset please contact Leila Perie (leila.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 12 months scRNAseq fastq files (mouse 1) for Figures 5 and 6
<p>This dataset contains .fastq files for the 12 month timepoint (mouse 1) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 12 months scRNAseq fastq files (mouse 2) for Figures 5 and 6
<p>This dataset contains .fastq files for the 12 month timepoint (mouse 2) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Set of loom files for three Ewing sarcoma cell lines CHLA9, CHLA10, TC71 profiled with scRNASeq at single cell level
<p>The raw sequence files were published in <a href="https://www.mdpi.com/2072-6694/12/4/948">Miller et al, 2020, Cancers</a> . The files were downloaded and processed using kallisto mapper.</p> <p>The loom files were used to build a <a href="https://doi.org/10.1101/2021.06.14.448414">model of cell cycle with switches</a> and in the development of <a href="https://github.com/csgroen/scycle">scycle Python package</a>.</p>
Urbanus and Cosgrove Nature Communications (2023) - scRNAseq fastq files GFP positive sample in Figure 2
<p>This dataset contains .fastq files for the GFP negative sample in the scRNAseq dataset used in Figures 2. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 18 months scRNAseq fastq files (mouse 2) for Figures 5 and 6
<p>This dataset contains .fastq files for the 18 month timepoint (mouse 2) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p> <p> </p> <p> </p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 6 months scRNAseq fastq files (mouse 1 and 2) for figures 5 and 6
<p>This dataset contains .fastq files for the 6 month timepoint (mouse 1 & 2) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Example ScRNAseq Dataset 1 for Learning Web-based Tools
<p>This is one of the three example ScRNAseq datasets used to follow the guided example analyses within "A Guide to Single-Cell RNA Sequencing Analysis Using Web-based Tools for Non-Bioinformaticians" in the FEBS Journal. This dataset can be downloaded and imported into a variety of web-based tools and used as a learning device to gain more familiarity with the tools. As described in the paper, this dataset represents the untreated control. </p>
Example ScRNAseq Dataset 2 for Learning Web-based Tools
<p>This is one of the three example ScRNAseq datasets used to follow the guided example analyses within "A Guide to Single-Cell RNA Sequencing Analysis Using Web-based Tools for Non-Bioinformaticians" in the FEBS Journal. This dataset can be downloaded and imported into a variety of web-based tools and used as a learning device to gain more familiarity with the tools. As described in the paper, this dataset represents the negative control (carrier only). </p>
scRNAseq of Danio rerio telencephalon
<p>scRNA-sequencing dataset of Danio rerio telencephalon is provided for easier access as .rds objects:</p><ul><li>telencephalon_cells.rds contains all cells and their respective supercluster-ID</li><li>neurons.subset.rds contains all neuronal cell types and their subcluster ID</li></ul>
Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled scRNAseq
<p><span>During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities.<span> Here, we </span>combine<span> </span>single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We then introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell-types. Our study provides a quantitative approach to study mRNA regulation during</span> a dynamic spatio-temporal response<span>.</span></p> <p> </p> <p>This repository contains the raw microscopy data that is analyzed in Figures 6F-I and Supplementary Figure S4 B-D.</p>
Developmental Mouse Brain scRNAseq Giotto Object
<p>Giotto object created from the single cell mouse brain atlas dataset from <a href="https://www.nature.com/articles/s41586-021-03775-x">Manno et al. 2021</a> (https://doi.org/10.1038/s41586-021-03775-x). This object contains expression information and cell annotations of developmental mouse brain cell types that are used with spatial DWLS deconvolution of Stereo-seq data in the Giotto Suite manuscript.</p> <p>The original data was downloaded from <a href="http://mousebrain.org/development/downloads.html">http://mousebrain.org/development/downloads.html </a>as a .loom file, and then loaded into Giotto.</p>
Microglia protect against age-associated brain pathologies - all scRNAseq datasets
<p>Zipped cellranger_matrices file contains the filtered_feature_bc_matrices and the raw_feature_bc_matrices obtained from cellranger for the young (E...), middle-aged(S...), old (...vo...) and thalamic (TH...) datasets. </p> <p> The cellinfo csv files give the metadata for each cell kept in the analysis, with a relationship between each barcode and information such as the umi counts or the cell type annotation.</p> <p>The metadata file gives information about each sample, to trace back the sample names used in cellranger to the biological samples. On other tabs it also has qc information such as the thresholds used for each sample.</p> <p>The raw fastq files and RDS files are available at GEO: GSE267545 and GEO:GSE215440 with the same metadata as here as well as the SingleCellExperiment in form of RDS objects (with information such as normalised reads and dimensional reduction embedings) available at GEO: GSE267545</p> <p>The code used to do the analysis is available in Anna-Williams GitHub and also in ZENODO: </p> <p> https://github.com/Anna-Williams/David-young (10.5281/zenodo.11199128) for the young dataset</p> <p> https://github.com/Anna-Williams/David-old (10.5281/zenodo.11199278) for the middle-aged dataset</p> <p>https://github.com/Anna-Williams/David-vold (10.5281/zenodo.11199322) for the old dataset<br>https://github.com/Anna-Williams/David-Thalamus (10.5281/zenodo.11199349) for the thalamic dataset<br>https://github.com/Anna-Williams/David-AgeIntegration (10.5281/zenodo.11199367) for the integration between the young, middle-aged and old datasets.</p> <p> </p> <p>Microglia are brain-resident macrophages that contribute to central nervous system development, maturation, and preservation. Here, we examine the consequences of lifelong absence of microglia on ageing using the Csf1rΔFIRE/ΔFIRE mouse model. In juvenile Csf1rΔFIRE/ΔFIRE mice, we show that microglia are largely dispensable for the transcriptomic maturation of other brain cell types. In contrast, with advancing age, multiple pathologies accumulate in Csf1rΔFIRE/ΔFIRE brains, astrocytes and oligodendrocyte-lineage cells become increasingly dysregulated, and white matter integrity declines, mimicking many of the pathological features of human CSF1R-related leukoencephalopathy. The thalamus is particularly sensitive to neuropathological changes in the absence of microglia, with atrophy, neuron loss, vascular disturbances, macroglial dysregulation, and severe calcifications all detected in this region. Thalamic calcification formation, which often occurs with normal ageing, is dramatically accelerated in Csf1rΔFIRE/ΔFIRE brains but can be prevented via transplantation of wild-type microglia. Our results indicate that lifelong absence of microglia results in an age-related neurodegenerative condition that can be prevented by the transplantation of healthy microglia.</p>
EBAII n1 scRNAseq : 10X PBMC10K Cell Ranger v3 MEX matrices
<ul> <li>A public dataset created and provided by <strong>10X Genomics</strong>, that consists in ~ <strong>10,000 PBMC</strong> (peripheral bone marrow cells) from a <strong>human donor</strong></li> <li>The experiment was performed with the <strong>3’ capture kit v3</strong></li> <li>The analysis was performed with <strong>Cell Ranger v3</strong></li> <li>Mapping was performed on the <strong>GRCh38-2020-A</strong> manufacturer reference</li> <li>The structure of these Cell Ranger outputs are described <a href="https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/outputs/cr-outputs-mex-matrices" target="_blank" rel="noopener">here</a></li> <li>The sparse matrix format corresponds to the <a href="https://math.nist.gov/MatrixMarket/formats.html" target="_blank" rel="noopener">Market Exchange Format (MEX)</a></li> <li>For educational purpose (speed), the dataset columns were reduced to 1/5th (from 737,280 to 147,456 barcodes)</li> </ul>
Urbanus and Cosgrove et al. Nature Communications (2023) - 18 months scRNAseq fastq files (mouse 1) for Figures 5 and 6
<p>This dataset contains .fastq files for the 18 month timepoint mouse 1 sample in the scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Urbanus and Cosgrove Nature Communications (2023) - scRNAseq fastq files GFP negative sample in Figure 2
<p>This dataset contains .fastq files for the GFP negative sample in the scRNAseq dataset used in Figures 2. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
Myelomeningocele spinal cord organoids scRNAseq
Open the record for dataset details and reuse information.
MelanomaCITResponse_scRNAseq_TILandPBMC_publication_data
<p>Processed data related to the publication "<strong>Myeloid-T cell interplay and cell state transitions associated with checkpoint inhibitor response in melanoma</strong>" by <a href="https://pubmed.ncbi.nlm.nih.gov/38593812/">Schlenker et al. 2024</a> </p> <p>Raw data was deposited in <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-13770?query=E-MTAB-13770"> ArrayExpress E-MTAB-13770</a></p> <p>Contains: h5ads from full analysis, monocyte-macrophage analysis and CD8+ T cell analysis; includes annotations; for convenience, per cell metadata of the initial immune analysis (Fig.1 in manuscript) is additionally available as a tsv file - <a href="https://zenodo.org/api/records/15590216/draft/files/pub_sw_besca2_immune.annotated.cellmetadata.tsv.zip/content" target="_blank" rel="noopener noreferrer">pub_sw_besca2_immune.annotated.cellmetadata.tsv.zip</a>. Raw count data is available as <a href="https://zenodo.org/uploads/15593993" target="_blank" rel="noopener noreferrer">pub_sw_besca2_immune.annotated.raw.h5ad</a>. </p>
EBAII n1 scRNAseq : Common resources (gene lists, ...)
<p>This repository contains R objects (RDS) for</p> <ol> <li>Gene lists used for the single-cell RNAseq analysis pre-processing <ol> <li>Considered gene-lists : <ol> <li>mitochondrial genes</li> <li>ribosomal protein-coding genes</li> <li>mechanical stress response genes</li> </ol> </li> <li>Considered species : <ol> <li>homo sapiens (human)</li> <li>mus musculus (mouse)</li> <li>rattus norvegicus (rat</li> </ol> </li> </ol> </li> <li>Reference bulk RNAseq profiles from ImmGenData, for automatic cell type annotation through celldex </li> </ol>
EBAII n1 scRNAseq : Training intermediate Seurat (v5) objects
<p>Intermediate Seurat (v5) objects produced for the EBAII n1 single cell RNAseq training.</p> <ul> <li><code>01_TD3A_S5_Metrics.Tech_31053.4587.RDS</code> : Technical metrics (nFeature, nCount, ...)</li> <li><code>02_TD3A_S5_Metrics.All_31053.4587.RDS</code> : Biological metrics (%mito, %ribo, %stress)</li> <li><code>03_TD3A_S5_CC_31053.4587.RDS</code> : Estimated cell cycles scores and phase</li> <li><code>04_TD3A_S5_Metrics.Filtered_12508.4278.RDS</code> : Cells and features filtered according to metrics</li> <li><code>05_TD3A_S5_Doublets.filtered_12508.4035.RDS</code> : Cell doublets removed</li> <li><code>06_TD3A_S5_LogNorm_12508.4035.RDS</code> : LogNormalization applied</li> <li><code>07_TD3A_S5_Scaled.2k_12508.4035.RDS</code> : Scaling using 2000 HVGs</li> <li><code>08_TD3A_S5_Scaled.2k_Reg.PCrb_12508.4035.RDS</code> : Same with regression for <code>percent_rb</code> (% ribo)</li> <li><code>09_TD3A_S5_DimRed.PCA_12508.4035.RDS</code> : PCA with 50 components</li> <li><code>10_TD3A_S5_Clustered.0.8_12508.4035.RDS</code> : Louvain clustering with resolution 0.8</li> <li><code>11_TD3A.TDCT_S5_Merged_12926.3886</code> : Merging of the pre-processed TD3A and TDCT samples (each halved for its cells, using random subsampling with the seed 1337)</li> <li><code>12_TD3A.TDCT_S5_Integrated_12926.3886.RDS</code> : Results of TD3A and TDCT samples integration using 4 different methods (Seurat's CCA, RPCA, Harmony ; and standalone Harmony), and Louvain clustering for each (res 0.8)</li> <li><code>13_TD3A.TDCT_S5_Integrated_Annotated.RDS</code> : Results of the automatic cell type annotation</li> </ul>
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.