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11,105
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Dataset results
11,105 results for “single cells”
Single Cell RNAseq Pancreatic Cancer Atlas
<p>Complete object for scRNAseq PDAC atlas.</p> <p> </p> <p>Please cite: Loveless IM, Kemp SB, Hartway KM, Mitchell JT, Wu Y, Zwernik SD, Salas-Escabillas DJ, Brender S, George M, Makinwa Y, Stockdale T, Gartrelle K, Reddy RG, Long DW, Wombwell A, Clark JM, Levin AM, Kwon D, Huang L, Francescone R, Vendramini-Costa DB, Stanger B, Alessio A, Waters AM, Cui Y, Fertig EJ, Kagohara LT, Theisen B, Crawford HC, Steele NG. Human pancreatic cancer single cell atlas reveals association of CXCL10+ fibroblasts and basal subtype tumor cells. Clin Cancer Res. 2024 Dec 5. doi: 10.1158/1078-0432.CCR-24-2183. Epub ahead of print. PMID: 39636224.. </p>
Teaching Datasets for Single-Cell RNA-seq Analysis Course
<p>This repository contains teaching datasets used in the Single-Cell RNA-seq Analysis Course, which is part of the SeuratExtend project (<a href="https://github.com/huayc09/SeuratExtend">https://github.com/huayc09/SeuratExtend</a>). The course materials are available at <a href="https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1">https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1</a>, with code and scripts hosted at <a href="https://github.com/huayc09/single-cell-course">https://github.com/huayc09/single-cell-course</a>.</p> <p>The datasets include:</p> <ol> <li>3k Peripheral Blood Mononuclear Cells (PBMCs)</li> <li>Paired PBMC samples processed with 10x Genomics' 3' kit</li> <li>Paired PBMC samples processed with 10x Genomics' 5' kit</li> </ol> <p>All original data were obtained from 10x Genomics (<a href="https://www.10xgenomics.com/resources/datasets">https://www.10xgenomics.com/resources/datasets</a>) and processed for educational purposes.</p>
Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy
<p>This repository contains code used to produce the results in: Till Braun, Michael Rade and Maximilian Merz et al., Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy</p> <p>Contents:<br>- Instructions for using the singularity image and R packages can be found here: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity">https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity</a><br>- "seurat_harmony.Rds" and "seurat_harmony_t.Rds": These Seurat objects were used to produce Figure 2 in this publication. "seurat_tcell_obj.Rds" is a subset of "seurat_harmony.Rds" and contains only T-cells. In addition, the metadata was extended by the output of TCR-Seq (using the R package scRepertoire). The following script uses the objects: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R">https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R</a></p> <p> </p>
Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions
<p><em><strong>Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions</strong></em></p> <p> </p> <p>Tools and scripts were used to generate results in Zhang et al 2024.</p> <p>Supplementary files that were not included in the initial submission.</p> <p>Full summary statistics of eQTLs including top eQTLs and all SNP-gene pairs of each cell type, consistent.tar.gz for consistent eQTLs per cell and response.tar.gz for response eQTLs, respectively.</p>
The single-cell spatial landscape of stage III colorectal cancers
<p>We added H&E images with pathologist annotations in this version (v2). All other files are available in version 1. </p> <p> </p> <p>The Seurat and Scanpy objects for single-cell data, along with a CSV file containing coordinates and cell types for all single cells, have been deposited. Additionally, the original IMC image data has also been uploaded.</p> <p>The Seurat object is: <em>20220215_COAD_cancer_control_umapped_annotated<strong>.rds</strong></em></p> <p>The Scanpy object is: <em>20220215_COAD_cancer_control_umapped_annotated.<strong>h5ad</strong></em></p> <p> </p>
Association of Peripheral Monocytic-Myeloid-Derived Suppressor Cells with Molecular Subtypes in Single Center Endometrial Cancer Patients Receiving Carboplatin + Paclitaxel/Avelumab (MITO END-3 Trial)
<p><span>The MITO-END3 trial compared carboplatin and paclitaxel (</span><span>CP</span><span>) with avelumab plus carboplatin and paclitaxel (</span><span>CPA)</span><span> as first-line treatment in endometrial cancer (EC) patients and </span><span>demonstrated a significant interaction between avelumab response and mismatch repair status. To investigate prognostic/predictive biomarker, </span><span>twenty-nine MITO-END3-EC patients were evaluated at pre-treatment (B1) and at the end of CP/CPA treatment (B2) for </span><span>peripheral Myeloid derived suppressor cells (MDSC) and Tregs. </span><span>At B2, <span>effector</span> Tregs frequency was significantly higher in patients treated with CPA as compared to CP (p=0.038). Both treatments (CP/CPA) induced significant decrease in peripheral M-MDSC (-5.41%) in TCGA 2-MSI-High as compared to TCGA-category 4 tumors (p=0.004). In accordance, both treatments induced M-MDSCs (+5.34%) in MSS patients as compared to MSI-High patients (p=0.001). Moreover, in a subgroup of patients, </span><span>primary tumors were highly infiltrated by M-MDSCs in MSS as compared to MSI-high ECs. </span><span>A post hoc analysis displayed higher frequeny of M-MDSCs (p=0.020) and lower frequency of CD4+ (p<0.005) at pretreatment in EC patients as compared to healthy donors. </span><span>In conclusion, </span><span>the</span><span> peripheral evaluation of MDSCs and Tregs correlated with molecular features in EC treated with CP/CPA and may add insights in identifying EC patients responder to first line chemo/chemo-immunotherapy. </span></p>
Single-cell and spatial transcriptomics of stricturing Crohn's disease
<p>This folder contains the spatial transcriptomics data + code. This code was generated by members of the Smillie Lab @ MGH and Harvard Medical School.</p> <ul> <li><strong>github.tar.gz: </strong>spatial analysis code and data</li> <li><strong>anndata.h5ad:</strong> anndata object (scanpy)</li> <li><strong>V*tar.gz:</strong> raw spatial transcriptomics files</li> </ul> <p>The <strong>github.tar.gz</strong> folder contains everything you need to reproduce the spatial transcriptomics figures. It is structured as follows:</p> <ul> <li><strong>1.BayesPrism:</strong> code for running BayesPrism on spatial data</li> <li><strong>2.SparCC:</strong> code for running SparCC on spatial data</li> <li><strong>3.Lasso: </strong>code for running lasso regression on spatial data</li> <li><strong>4.Analysis: </strong>code for reproducing all figures in the paper</li> <li><strong>4.Analysis/1.analysis.r</strong><strong>: </strong>script to reproduce all figures in the paper ***</li> <li><strong>code:</strong> code library containing all necessary functions</li> <li><strong>load_data.r: </strong>code to load the single-cell and spatial datasets</li> <li><strong>sco.rds:</strong> single-cell analysis object (10X Chromium) formatted as an R list</li> <li><strong>vis.rds:</strong> spatial analysis object (10X Visium) formatted as an R list</li> </ul> <p>All scripts are numbered. You need to run everything in order. For convenience, we include the output files for <strong>1.BayesPrism</strong>, <strong>2.SparCC</strong>, and <strong>3.Lasso</strong>, allowing you to skip straight to the analysis code in <strong>4.Analysis.</strong></p> <p>To reproduce all figures in the paper, you need to do the following:</p> <ol> <li>Edit your PROJECT_FOLDER in the header of <strong>load_data.r</strong></li> <li>Install the packages listed at the top of <strong>load_data.r</strong></li> <li>Go to the <strong>4.Analysis</strong> directory, start an interactive R session, and type:<br>> source('1.analysis.r')</li> </ol> <p>This will load the beginning of the <strong>1.analysis.r</strong> script (until the stop() statement on line 68). You can run the code in two different ways:</p> <ol> <li>You can step through the code line by line in your interactive R session (starting at line 68)</li> <li>Alternatively, remove the stop() statement from the script, then run the code start to finish</li> </ol> <p>If you encounter any errors, try to debug them using a combination of Google+ChatGPT. If you still have trouble, please contact the Smillie Lab.</p> <p><strong>Note: </strong>the single-cell and spatial code are also available on GitHub. However, the spatial analysis requires large files that cannot be hosted on GitHub. Therefore, it is better to download the code + files from Zenodo. The GitHub link is provided below:</p> <p><a href="https://github.com/LJ-Kong/fibrosis_scRNA_stRNA">https://github.com/LJ-Kong/fibrosis_scRNA_stRNA</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
Data for 'Membrane marker selection for segmenting single cell spatial proteomics data'
<p>Additional data for 'Membrane marker selection for segmenting single cell spatial proteomics data'</p>
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.
<p>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133</p>
Single-cell RNA sequencing of CNS-infiltrating HSC-derived phagocytes of Ms4a3Ai14, BM chimeric mice (CD45.2 Csf2rb-/-: CD45.1 Csf2rb+/+ and CD45.2 Ifngr1-/-: CD45.1 Ifngr1+/+) using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of CNS-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE, BM chimeric mice (CD45.2 <em>Csf2rb</em><sup>-/-</sup>: CD45.1 <em>Csf2rb</em><sup>+/+</sup> and CD45.2 <em>Ifngr1<sup>-/-</sup></em>: CD45.1 <em>Ifngr1<sup>+/+</sup></em>) using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Lymph node-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Bone Marrow-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of Ms4a3Ai14 using 10X Genomics platform. IFN-γ and GM-CSF control complementary differentiation programs in the monocyte to phagocyte transition during neuroinflammation.
<p><strong>Single-cell RNA sequencing of Blood-infiltrating HSC-derived phagocytes of <em>Ms4a3</em><sup>Ai14</sup> at onset and peak EAE using 10X Genomics platform.</strong></p> <p>The sorted cells were loaded into 10x Genomics Chromium in parallel. Libraries were prepared as per the manufacturer's protocol (Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 protocol) and sequenced on an Illumina NovaSeq sequencer according to 10X Genomics recommendations (paired-end reads, R1=28, i7=8, R2=91) to a depth of around 50,000 reads per cell.</p> <p>Initial processing was done using Cell Ranger (v3.1.0) mkfastq and count (reads were aligned to GENCODE reference build GRCm38.p6 Release M23 with added tdTomato sequence for the dataset from <em>Ms4a3</em><sup>Ai14</sup> mouse and collapse UMIs). Starting from the filtered gene-cell count matrix produced by CellRranger's in-built cell calling algorithms, we proceeded with Seurat v4 workflow.</p>
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
<p>A detailed list of the selected scRNA-seq datasets used in the paper, also provided in Additional file <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1898-6#MOESM1">1</a>: Table S1-S2.</p>
Correlating single-molecule rupture mechanics with cell population adhesion by yeast display
<p>Data underlying the figures in the publication “Correlating single-molecule rupture mechanics with cell population adhesion by yeast display”, published in <em>Biophysical Reports, </em><em><strong>2022</strong></em><em>, Volume 2, Issue 1, 100035, ISSN 2667-0747.</em></p> <p><em><a href="https://doi.org/10.1016/j.bpr.2021.100035">https://doi.org/10.1016/j.bpr.2021.100035</a></em></p> <p>Table of contents:</p> <p><strong>1. Fig2_SMFS.xlsx</strong>: Contour Length vs probability density values for <em>Figure 2D</em>. Rupture Force and Loading rate values for all SMFS curves analysed for <em>Figure 2E</em> and <em>2F</em>.</p> <p><strong>2. Fig3_SDA.xlsx</strong>: Shear Stress <em>vs</em> Cell Density values of all replicates and speeds for F<em>igures 3D</em> and <em>3E</em>.</p> <p><strong>3. FigS1_SPR curves.xlsx</strong>: SPR curves reported in Supplementary <em>Figure 1</em>.</p> <p><strong>4. FigS2_one step FLN.xlsx</strong>: Force <em>vs</em> Extension trace and Contour Length Histogram values for one step FLN unfolding shown in <em>Supplementary Figure 2</em>.</p> <p><strong>5. FigS3_SMFS_internal control.xlsx</strong>: Rupture Force and Loading rate values for N-S25H reported in <em>Supplementary Figure 3</em>.</p> <p><strong>6. FigS5_SDA_internal control.xlsx</strong>: Shear Stress <em>vs</em> Cell Density values of N-WT shown in <em>Supplementary figure 5B</em>.</p> <p><strong>7. FigS6_SDA_induction timepoints.xlsx</strong>: Shear Stress <em>vs</em> Cell Density values and median of expression detected by flow cytometry reported in <em>Supplementary Figure 6</em>.</p>
Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"
<p>Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"</p>
Repository - Sharp, localized phase transitions in single neuronal cells
<p>This repository is for the journal article "Sharp, localized phase transitions in single neuronal cells" (PNAS) and provides additional data as it contains single measurement files (plots) for cell measurements on which the presented data (mean values) is based.</p>
Single cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis
<p>Single <span>cell RNA sequencing (scRNAseq) is a powerful technique that continues to expand across various biological applications. However, incomplete 3' UTR annotations can impede single cell analysis resulting in genes that are partially or completely uncounted. Performing scRNAseq with incomplete 3' UTR annotations can hinder the identification of cell identities and gene expression patterns and lead to erroneous biological inferences. We demonstrate that performing single cell isoform sequencing (ScISOr-Seq) in tandem with scRNAseq can rapidly improve 3' UTR annotations. Using threespine stickleback fish (</span><em>Gasterosteus aculeatus</em><span>), we show that gene models resulting from a minimal embryonic ScISOr-Seq dataset retained 26.1% greater scRNAseq reads than gene models from Ensembl alone. Furthermore, pooling our ScISOr-Seq isoforms with a previously published adult bulk Iso-Seq dataset from stickleback, and merging the annotation with the Ensembl gene models, resulted in a marginal improvement (+0.8%) over the ScISOr-Seq only dataset. In addition, isoforms identified by ScISOr-Seq included thousands of new splicing variants. The improved gene models obtained using ScISOr-Seq lead to successful identification of cell types and increased the reads identified of many genes in our scRNAseq stickleback dataset. Our work illuminates ScISOr-Seq as a cost-effective and efficient mechanism to rapidly annotate genomes for scRNAseq.</span></p>
Files for Manuscript "A pulmonologist's guide to perform and analyse cross-species single-lung-cell transcriptomics"
<p>Input Files for Manuscript "A pulmonologist’s guide to perform and analyse cross-species single-lung-cell transcriptomic"</p> <p>See https://github.com/GenStatLeipzig/pulmonologists_interspecies_scRNA for details.</p> <p>Manuscript authored by:</p> <p>Peter Pennitz1,2*, Holger Kirsten3*, Vincent D. Friedrich3,4, Emanuel Wyler5, Cengiz Goekeri1,2,6, Benedikt Obermayer7, Gitta A. Heinz8, Mir-Farzin Mashreghi8,9, Maren Büttner10,11 Jakob Trimpert12, Markus Landthaler5,13, Norbert Suttorp2, Andreas C. Hocke1,2, Stefan Hippenstiel2, Mario Tönnies14, Markus Scholz3, Wolfgang M. Kuebler15,16, Martin Witzenrath1,2,16, Katja Hoenzke1,2 and Geraldine Nouailles1,2,# </p> <p> </p> <p>1 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Division of Pulmonary Inflammation, Berlin, Germany. </p> <p>2 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany. </p> <p>3 University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany. </p> <p>4 Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany. </p> <p>5 Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany. </p> <p>6 Cyprus International University, Faculty of Medicine, Nicosia, Cyprus. </p> <p>7 Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Unit Bioinformatics, Berlin, Germany. </p> <p>8 Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), A Leibniz Institute, Therapeutic Gene Regulation, Berlin, Germany. </p> <p>9 Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Berlin, Germany. </p> <p>10 University of Bonn, Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, Bonn, Germany. </p> <p>11 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Systems Medicine, Bonn, Germany. </p> <p>12 Freie Universität Berlin, Institute of Virology, Berlin, Germany. </p> <p>13 Humboldt-Universität zu Berlin, Institute for Biology, IRI Life Sciences, Berlin, Germany. </p> <p>14 HELIOS Clinic Emil von Behring, Department of Pneumology and Department of Thoracic Surgery, Chest Hospital Heckeshorn, Berlin, Germany. </p> <p>15 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Physiology, Berlin, Germany. </p> <ol> <li> <p>German Center for Lung Research (DZL), Berlin, Germany. </p> </li> </ol> <p> </p> <p>* Authors contributed equally to this work </p> <p> </p> <p>References for original datasets :</p> <p>Human Charité: Hocke A, Hönzke K, Obermayer B, Baumgardt M, Wyler E, Hippenstiel S, Mache C. Charité Berlin /Berlin Institute of Health. GEO accessions GSM5958267, GSM5958272, GSM5958283, GSM5958285</p> <p>Human Travaglini et al.: published at <a href="https://www.synapse.org/">https://www.synapse.org</a> by Travaglini et al. (<a href="https://doi.org/10.1038/s41586-020-2922-4">https://doi.org/10.1038/s41586-020-2922-4</a>)</p> <p>Monkey: published at <a href="https://www.ncbi.nlm.nih.gov/geo">https://www.ncbi.nlm.nih.gov/geo</a> by Speranza et al. (<a href="https://doi.org/10.1126/scitranslmed.abe8146">https://doi.org/10.1126/scitranslmed.abe8146</a>)</p> <p>Hamster: Charité Berlin, see: <a href="https://doi.org/10.1038/s41467-021-25030-7">https://doi.org/10.1038/s41467-021-25030-7</a></p> <p>Mouse: Pennitz P, Witzenrath, M, Nouailles G, Berlin Charité.</p> <p>Rat and Pig: published at <a href="https://www.ncbi.nlm.nih.gov/geo">https://www.ncbi.nlm.nih.gov/geo</a> by Raredon et al. (<a href="https://doi.org/10.1126/sciadv.aaw3851">https://doi.org/10.1126/sciadv.aaw3851</a>) .</p> <p>Annotation: Ensembl BioMart</p>
single-cell RNA-seq of EHT process
<p>This file 'atfr.rds' contains onject including the raw and log-normalizd expression counts, and TF activity matrix generated from metaRegulon. In addition, the file 'cell_weight_pseudotime.rds' contains the pseudotime information.</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.