Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,917
datasets available to search
ShareScore release 0.7.1
Dataset results
2,917 results for “scRNA”
scRNA-seq atlases for 3 Caenorhabditis species - annotated cell datasets
<p>Annotated datasets (monocle3 objects) of scRNA-seq data for <em>C. elegans</em>, <em>C. briggsae</em> and <em>C. tropicalis</em> L2 nematodes. The datasets are published together with the manuscript "Divergence in neuronal signaling pathways despite conserved neuronal identity among <em>Caenorhabditis</em> species".</p> <p><a href="https://doi.org/10.1016/j.cub.2025.05.036" target="_blank" rel="noopener">https://doi.org/10.1016/j.cub.2025.05.036</a></p> <p>Files deposited include cell datasets for all sequenced cells ("all_cds") and datasets for all cells annotated as neurons ("neu_cds"). </p> <p><em>C. elegans</em> strain - N2.</p> <p><em>C. briggsae</em> strain - AF16.</p> <p><em>C. tropicalis</em> strain - NIC203.</p>
dual scRNA-seq analysis of P. vivax infected hepatocytes
<p>Malaria-causing <em>Plasmodium vivax</em> parasites can linger in the human liver for weeks to years, and then reactivate to cause recurrent blood-stage infection. While an important target for malaria eradication, little is known about the molecular features of the replicative and non-replicative states of intracellular <em>P. vivax</em> parasites, or their human host-cell dependencies and the host responses to them. Here, we leverage a bioengineered human microliver platform to culture patient-derived <em>P. vivax</em> parasites in primary human hepatocytes and conduct transcriptional profiling. By coupling enrichment strategies with bulk and single-cell analyses, we captured both parasite and host transcripts in individual hepatocytes throughout the infection course. We define host- and state-dependent transcriptional signatures and identify previously unappreciated populations of replicative and non-replicative parasites, sharing features with sexual transmissive forms. We find that infection suppresses transcription of key hepatocyte function genes, and that <em>P. vivax</em> elicits an innate immune response that can be manipulated to control infection. Our work provides an extendible framework and resource for understanding host-parasite interactions and reveals new insights into the biology of <em>P. vivax</em> dormancy and transmission.</p>
hvulgaris scRNA data set objects
<p>Converted scRNA data from (Cazet et al. 2022), see a detailed description of the study here: https://doi.org/10.1101/2022.06.21.496857</p> <p>Data were downloaded from https://research.nhgri.nih.gov/HydraAEP/download/scriptsdata/aepAtlasNonDub.rds and converted into AnnData (h5ad) files only keeping the RNA assay (removed integrated and SCT assay) to be able to analyse with e.g. python scanpy package.</p> <p>Note: In the original rds file, the rownames(aepAtlasNonDub@meta.data) are sorted alpha-numerical, whereas the cell order in colnames(aepAtlasNonDub) are not. I have re-sorted the rownames(aepAtlasNonDub@meta.data) according to cell order prior AnnData conversion, so that h5ad data have correct observations.</p> <p>If you use this data, please cite Cazet et al. 2022.</p>
mouse GSE199308 scRNA data set objects
<p>scRNA data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199308 (Huang et al. 2023), see a detailed description of the study here: https://atlas.gs.washington.edu/mmca_v2/public/about.html</p> <p>Data were downloaded from https://atlas.gs.washington.edu/mmca_v2/public/download.html to create an AnnData (h5ad) file with meta data to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Huang et al. 2023.</p>
scRNA-seq data for article: Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis
<p>Single-cell RNA-seq dataset from sorted CD11bInt, F4/80Hi, CD64+ mouse liver cells in naive or Leishmania infantum-infected animals at 42 d.p.i.. Data analyses and results are described in manuscript: "Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis". Data files are Seurat objects in RDS format. Filtered-out potential doublets, low quality cells and dying cells (excluded cells with <1000 genes detected, cells with >6000 genes detected, cells with mitochondrial gene expression > 10% and cells with <5000 transcript molecules). Data normalization, scaling and integration performed using Seurat.</p> <p>Filtered dataset containing all KCs and macrophages is in the "pessenda_KC_Macro_seurat" file.</p> <p>Our data were then mapped onto a reference dataset published by Remmerie et al. (DOI: 10.1016/j.immuni.2020.08.004) for annotation consistent with the literature. The reference mapped object can be found in the "pessenda_refmap_KC_Macro_seurat" file.</p> <p>Dataset containing the additional analysis of CLEC4F-TIM4+ FACS-sorted KCs can be found in the "pessenda_refmap_KCTimPos_seurat" file.</p>
Individual-donor scRNA-Seq datasets, as Seurat 4.0.5 objects
<p>The provided datasets correspond to the analyses of individual donor single-cell RNA Sequencing (scRNA-Seq) datasets, before their integration. The datasets have been saved as Seurat v4.0.5 objects. For clustering, we used default settings in Seurat 4.0.5 (resolution 0.8) and increased resolution, if necessary, to separate epithelium in proximal and distal. </p> <p>The *_clusters.pdf files show the suggested clusters in the individual datasets and the *_indiv_anno1.pdf files show the cell annotations according to the 84 cell states, described in the study with title "Developmental origins of cell heterogeneity in the human lung" (1st preprint version doi: https://doi.org/10.1101/2022.01.11.475631).</p> <p>The "*_cluster_annotations.csv" files provide information about the suggested annotations of the clusters.</p> <p>The "*_object_raw_and_log_counts.RData" objects contain the metadata and the UMI-counts [raw and log2(counts+1)] for each donor scRNA-Seq dataset.</p> <p> </p>
frog scRNA data set objects
<p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p> <p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Briggs et al. 2018 and Qiu et al. 2022.</p>
zebrafish scRNA data set objects
<p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p> <p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Farrel et al. 2018, Wagner et al. 2018 and Qiu et al. 2022.</p>
scRNA-seq of KPC mice
<p>This is processed and zipped data from GSE129455. Users can use the "readRDS(file = filename)" function in the R programming language to access it.</p>
mouse scRNA data set objects
<p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qui et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p> <p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Mohammed et al. 2017, Cheng et al. 2019, Pijuan-Sala et al. 2019, Cao et al. 2019 and Qui et al. 2022.</p>
MetaTiME scRNA Data
<p><strong>Tumor scRNAseq Data for MetaTiME.</strong></p> <p>A large collection of uniformly processed tumor single-cell RNA-seq. Includes raw data and MetaTiME score for the TME cells.</p> <p>Please also cite MetaTiME (bioRxiv <a href="https://doi.org/10.1101/2022.08.05.502989">https://doi.org/10.1101/2022.08.05.502989</a>; new journal article doi pending) and TISCH (<a href="https://doi.org/10.1093/nar/gkaa1020">https://doi.org/10.1093/nar/gkaa1020</a>) if using this data.</p> <p>MetaTiME GitHub repo for annotating cell states in tumor: <a href="https://github.com/yi-zhang/MetaTiME ">https://github.com/yi-zhang/MetaTiME</a></p> <p> </p> <p> </p> <p> </p>
celegans scRNA data set objects
<p>Combined and converted scRNA data from (Packer and Zhu et al. 2019), see a detailed description of the study here: https://www.science.org/doi/full/10.1126/science.aax1971</p> <p>Data were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126954 converted into Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Packer and Zhu et al. 2019.</p>
zebrafish GSE223922 scRNA data set objects
<p>scRNA data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223922 (Sur et al. 2023), see a detailed description of the study here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055256/</p> <p>Data were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223922 to create a R Seurat object and converted into AnnData (h5ad) file to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Sur et al. 2023.</p>
Test data for running snakePipes : scRNA-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run scRNA-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for mouse (<strong>mm10</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Transcription start site analysis for heterogenous CD4+ T cells using 5′ scRNA-seq
<p>These datasets are generated by ReapTEC (read-level pre-filtering and transcribed enhancer call) using 5' single-cell RNA-seq data on human heterogenous CD4+ T cells. By taking advantage of a unique "cap signature" derived from the 5′-end of a transcript, ReapTEC simultaneously profiles gene expression and enhancer activity at nucleotide resolution using 5′-end single-cell RNA-sequencing (5′ scRNA-seq). The detail of ReapTEC pipeline is described in https://github.com/MurakawaLab/ReapTEC.</p>
reference genome used for scRNA-seq mapping with CellRanger in the method spatial-scERA
<p>The modified <em>Drosophila </em>melanogaster (dm6) reference genome used for the mapping with CellRanger in the method paper about spatial-scERA</p> <p>The genome is composed of the original genome from EnsembleMetazo website (BDGP6.46.110). An addition of 26 chromosomes (one for the plasmid construct and 25 for the tested enhancer sequences) is also present to allow for the mapping of mRNAs comming from our constructs. </p>
scRNA-seq background RNA
<p>Data files to benchmark background RNA estimation and removal files<br>We uploaded 5 zip files, each corresponding to one 10X experiment of a mixture of mouse kidney cells from 3 mouse strains BL6, SVLMJ and CAST. The data contain information from genotype based demultiplexing also at a feature resolution.</p> <p>We added now also intermediate files of the genotype analysis.</p>
Detection of early seeding of Richter transformation in chronic lymphocytic leukemia: scRNA-seq data
<p>Richter transformation (RT) is a paradigmatic evolution of chronic lymphocytic leukemia (CLL) into a very aggressive large B cell lymphoma conferring a dismal prognosis. The mechanisms driving RT remain largely unknown. We characterized the whole genome, epigenome and transcriptome, combined with single-cell DNA/RNA-sequencing analyses and functional experiments, of 19 cases of CLL developing RT. Studying 54 longitudinal samples covering up to 19 years of disease course, we uncovered minute subclones carrying genomic, immunogenetic and transcriptomic features of RT cells already at CLL diagnosis, which were dormant for up to 19 years before transformation. We also identified new driver alterations, discovered a new mutational signature (SBS-RT), recognized an oxidative phosphorylation (OXPHOS)high–B cell receptor (BCR)low-signaling transcriptional axis in RT and showed that OXPHOS inhibition reduces the proliferation of RT cells. These findings demonstrate the early seed- ing of subclones driving advanced stages of cancer evolution and uncover potential therapeutic targets for RT.</p> <p>This repository contains the processed scRNA-seq data (expression matrices, Seurat objects, metadata) related with this publication.</p>
Combined network file for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"
<p><strong>Combined network from scRNA-seq and proteomics data</strong></p> <p>Given the complementary nature of the networks based on scRNA-seq and proteomics data individually, we decided to combine them into a single network. As the Pearson Correlation Coefficient scores from FAVA cannot be assumed to be directly comparable across the two networks, we converted them to probabilistic scores based on the KEGG benchmarks. These calibrated scores were then combined to produce a single network based on scRNA-seq as well as proteomics data. As should be expected, this network outperforms the individual networks, combining the best aspects of both.</p>
Multimodal scRNA-seq
<p>Figure depicting the breadth of multimodal scRNA-seq technologies. For a complete list of references, see https://github.com/arnavm/multimodal-scRNA-seq</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.