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412 results for “snRNA-seq”
Data used in ECLIPSER methods paper and GTEx snRNA-seq cross-tissue reference map analysis
<p>The tables were used in the papers: Rouhana*, Wang* <em>et al.,</em> ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci, bioRxiv 2021, doi: https://doi.org/10.1101/2021.11.24.469720; and Eraslan <em>et al.,</em> Single-nucleus cross-tissue molecular reference maps to decipher disease gene function, bioRxiv 2021, doi: https://doi.org/10.1101/2021.07.19.452954. '<a href="https://zenodo.org/api/files/1f8d48d0-6bf7-4bec-b6ef-5c7a9ead8079/GTEx_v8_HG38_all_variants.tsv.gz">GTEx_v8_HG38_all_variants.tsv.gz</a>' is an input file for running GWASvar2gene on GTEx v8 eQTLs and sQTLs, and all other files are input files for ECLIPSER.</p>
Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation"
<p>Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation". All nuclei passed QC and were corrected for background noise using cellBender. Files are in R objects saved in RDS (R Data Serialization) format. This repo contains one Seurat v4 object and one gene-by-cell raw RNA count matrix in sparse matrix format (dgCMatrix).</p>
Data for Altered Glia-Neuron Communication in Alzheimer's Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq
<p><strong>data.tar.gz contains all files from the data directory associated with the 230313_TS_CCCinHumanAD GitHub project and includes the following:</strong></p><ul><li><strong>CellRangerCounts/</strong><ul><li><strong>GSE157827/</strong><ul><li><strong>post_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>matrix.mtx</strong></li></ul></li></ul></li><li><strong>pre_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>filtered_feature_ bc_matrix.h5</strong></li><li><strong>Raw_feature_bc_matrix.h5</strong></li></ul></li></ul></li></ul></li><li><strong>GSE174367/ : </strong>contains 19 directories for 19 samples, which contain 3 files each from Cell Ranger alignment of fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN19128610_S1_CTRL/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>Matrix.mtx</strong></li></ul></li></ul></li></ul></li><li><strong>ccc/</strong><ul><li><strong>nichenet_grn/</strong><ul><li><strong>gr_network_human_21122021.rds : </strong>accessed in October 2023, gene regulation network – gene regulatory information from MultiNicheNet</li><li><strong>ligand_tf_matrix_nsga2r_final.rds: </strong>accessed in October 2023, ligand tf matrix for signaling path determination from MultiNicheNet</li><li><strong>signaling_network_human_21122021.rds : </strong>accessed in October 2023, signaling network – protein-protein interaction information from MultiNicheNet</li><li><strong>weighted_networks_nsga2r_final.rds : </strong>accessed in October 2023, networks weighted by literature evidence from MultiNicheNet</li></ul></li><li><strong>nichenet_prior/</strong><ul><li><strong>ligand_target_matrix.rds : </strong>accessed in April 2023, ligand to target matrix from NicheNet</li><li><strong>lr_network.rds : </strong>accessed in April 2023, ligand-receptor matrix from NicheNet</li></ul></li><li><strong>nichenet_v2_prior/</strong><ul><li><strong>ligand_target_matrix_nsga2r_final.rds : </strong>accessed in June 2023, ligand to target matrix from MultiNicheNet used to predict target genes.</li><li><strong>lr_network_human_21122021.rds : </strong>accessed in June 2023, ligand-receptor matrix from MultiNicheNet used to predict ligand-receptor pairs.</li></ul></li><li><strong>geo_multinichenet_output.rds </strong>: MultiNicheNet output for Morabito et al., 2021 data</li><li><strong>geo_signaling_igraph_objects.rds </strong>: list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Morabito et al., 2021 dataset. </li><li><strong>gse_multinichenet_output.rds</strong> : MultiNicheNet output for Lau et al., 2020 data</li><li><strong>gse_signaling_igraph_objects.rds</strong> : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Lau et al., 2020 dataset </li></ul></li><li><strong>seurat_preprocessing/</strong><ul><li><strong>geo_filtered_seurat.rds : </strong>merged and filtered seurat object of Morabito et al., 2021 data</li><li><strong>geo_integrated_seurat.rds :</strong> seurat object integrated using harmony of Morabito et al., 2021 data</li><li><strong>geo_clustered_seurat.rds : </strong>clustered seurat object of Morabito et al., 2021 data</li><li><strong>geo_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Morabito et al., 2021 data</li><li><strong>gse_filtered_seurat.rds : </strong>merged and filtered seurat object of Lau et al., 2020 data</li><li><strong>gse_integrated_seurat.rds : </strong>seurat object integrated using harmony of Lau et al., 2020 data</li><li><strong>gse_clustered_seurat.rds :</strong> clustered seurat object of Lau et al., 2020 data</li><li><strong>gse_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Lau et al., 2020 data </li></ul></li></ul>
AMP-PD release 4: snRNA-seq and WGS dataset
<p>snRNA-seq dataset from 100 Parkinson’s disease cases and controls profiled in 5 postmortem brain regions. </p>
Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets
<p>Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals:</p> <p>Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019).</p> <p>Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020).</p> <p>Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020).</p> <p>Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018).</p> <p>Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).</p>
Cellxgene VIP snRNA-seq demo dataset for visualization and DE analysis
<p>H5ad file can be used as demo input for Cellxgene VIP. Dataset was the re-process from Schirmer et al Nature 2019 paper by using the raw fastq files. In order to reproduce the h5ad file, details could be found in https://github.com/interactivereport/cellxgene_VIP/blob/master/notebook/MS_Nature_Rowitch_snRNAseq.ipynb <br> Two rds files are also included here which are the input files for sample differential expression (DE) analysis scripts (glmmTMB and Nebula)</p>
Merged Adult Human Kidney sc & snRNA-seq dataset
<p>A merged human kidney scRNA-seq dataset made from the following scRNA-seq studies:</p> <p>Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019).</p> <p>Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020).</p> <p>Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020).</p> <p>Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018).</p> <p>Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).</p>
snATAC-seq and snRNA-seq in the chromatin landscape of healthy and injured cell types in the human kidney
<p>Isolated nuclei were immediately processed using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression (v1.0) kit, following the step-by-step protocol provided online [https://dx.doi.org/10.17504/protocols.io.5qpvoby69l4o/v2].<br>The RNA and ATAC libraries were separately sequenced on the NovaSeq 6000 system (Illumina) using the NovaSeq Control Software versions 1.7.0 and 1.7.5. Sample demultiplexing, barcode processing, gene expression quantification, and open chromatin peak quantification were performed using the Cell Ranger Arc software (version 2.0.0) with the GRCh38 (hg38) reference genome. </p> <p>Published in: <span><span>DOI: </span> <a href="https://doi.org/10.1038/s41467-023-44467-6" target="_blank" rel="noopener"> 10.1038/s41467-023-44467-6 </a> </span></p>
Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms
<p>Metadata files for our final analysis as part of the "<span><span>Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms" study.</span></span></p>
SnRNA-seq and mRNA hybridization indicate key bud events and LcFT1 and LcTFL1-2 mRNA transportability during floral transition in litchi
<p>In flowering plants, floral induction signals intersect at the shoot apex to modulate meristem determinacy and growth form. Herein, we reported a snRNA-seq analysis of litchi apical buds at different developmental stages. A total of 41,641 nuclei expressing 21,402 genes were analyzed, revealing 35 cell clusters corresponding to 12 broad populations. We signature genes associated with floral transition and propose a model that profiles the key events associated with litchi floral meristem identity by analyzing 567 identified floral meristem cells at single-cell resolution. Interestingly, snRNA-seq data indicated that all putative FT and TFL1 genes were not expressed in bud nuclei, but significant expressions of them were detected in bud samples using RT-PCR. Based on the expression patterns and gene silencing results, we highlight the critical role of LcTFL1-2 in inhibiting flowering and propose that LcFT1/LcTFL1-2 expression ratio may determine the success of flower transition. The transport of LcFT1 and LcTFL1-2 mRNA from the leaf to the shoot apical meristem was proposed based on in-situ and dot blot hybridization results. These findings allowed for a more comprehensive understanding of the molecular events that occur during the litchi floral transition, as well as the identification of new regulators.</p>
SnRNA-seq and mRNA hybridization indicate key bud events and LcFT1 and LcTFL1-2 mRNA transportability during floral transition in litchi
Open the record for dataset details and reuse information.
snRNA-seq of mouse GPe after cocaine or saline administration
<p>Data from snRNA-seq of the mouse GPe (seurat object).</p>
snRNA-seq from gastrocnemius (ENCSR771MOE)
GEO Series GSE286812. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
snRNA-seq from heart left ventricle (ENCSR204RHR)
GEO Series GSE286642. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Control of Striatal Circuit Development by the Chromatin Regulator Zswim6 [snRNA-Seq]
GEO Series GSE282825. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
single-nucleus RNA-sequencing (snRNA-seq) of ex-vivo-perfused pig kidney cortex tissue
GEO Series GSE218413. Sus scrofa. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Comprehensive gene expression analysis using iPS cells derived from patients with sleep bruxism [snRNA-seq]
GEO Series GSE278284. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
snRNA-seq from adrenal gland (ENCSR362YDM)
GEO Series GSE286686. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens [snRNA-seq]
GEO Series GSE307587. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Histone Lactylation Antagonizes Senescence and Skeletal Muscle Aging by Modulating Aging-Related Pathways [snRNA-seq]
GEO Series GSE226007. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
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.