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485 results for “ScRNAseq”
Seurat object subset of mouse liver scRNAseq data (Guilliams et al., Cell 2022)
<p>Seurat object containing a subset of the mouse liver scRNAseq data (Guilliams et al., Cell 2022)</p> <p>Data used only for demonstration purpose. Namely, to demonstrate the Differential NicheNet pipeline: https://github.com/saeyslab/nichenetr/blob/master/vignettes/differential_nichenet.md</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>
Processed Seurat Object of scRNAseq data from wildtype and CaMKK2 KO immune infiltrate of CT2a preclinical murine glioma
<p>This repository contains the processed Seurat objects generated from the raw data deposited at the Gene Expression Omnibus (GEO) under GSE197879.</p> <p>Details about the experiment and sequencing are available under GSE197879.</p> <p>Information on how the Seurat objects were created can be found in this GitHub repository https://github.com/wht10/CT2A_scRNAseq_CaMKK2KOvWT .</p> <p>Notable metadata within each Seurat object:</p> <p>1. Processed_CD45_Live_Fig2b.rds</p> <ul> <li>Genotype - whether the cell is from a WT or CaMKK2 KO mouse</li> <li>HTO_maxID - The biological replicate that the cell came from (4 biological replicates per genotype)</li> <li>MouseID - A concatenation between the genotype and HTO_maxID, providing a unique identifier for each biological replicate</li> <li>Cell.Type - The cell type annotations for each cell. Can be assigned to "Idents()" to change the name of the cell identities.</li> <li>Geno.Ident - A concatenation between Genotype and Cell.Type. By re-assigning this to "Idents()" "FindMarkers()" can be used to investigate differentially expressed genes within a cell-type between genotypes. </li> </ul> <p>2. Reclustered_TILs_Fig3a.rds</p> <ul> <li>Genotype - whether the cell is from a WT or CaMKK2 KO mouse</li> <li>HTO_maxID - The biological replicate that the cell came from (4 biological replicates per genotype)</li> <li>MouseID - A concatenation between the genotype and HTO_maxID, providing a unique identifier for each biological replicate</li> <li>Celltype - The cell type annotations for each cell. Can be assigned to "Idents()" to change the name of the cell identities.</li> <li>Geno_Ident - A concatenation between Genotype and cell-type. By re-assigning this to "Idents()" "FindMarkers()" can be used to investigate differentially expressed genes within a cell-type between genotypes. </li> </ul>
CSF1R scRNAseq Data
<p>CSF1R Data for mutant and controls</p>
sample2_scRNAseq of T cells from the small intestine of hCom1 colonized mice_raw sequence data
<p>sample2_scRNAseq of T cells from the small intestine of hCom1 colonized mice_raw sequence data</p>
sample4_scRNAseq of T cells from the large intestine of germ-free mice_raw sequence data
<p>sample4_scRNAseq of T cells from the large intestine of germ-free mice_raw sequence data</p>
sample3_scRNaseq of T cells from the small intestine of hCom2 colonized mice_raw sequence data
<p>sample3_scRNaseq of T cells from the small intestine of hCom2 colonized mice_raw sequence data</p>
sample6_scRNAseq of T cells from the large intestine of hCom2 colonized mice_raw sequence data
<p>sample6_scRNAseq of T cells from the large intestine of hCom2 colonized mice_raw sequence data</p>
sample5_scRNAseq of T cells from the large intestine of hCom1 colonized mice_raw sequence data
<p>sample5_scRNAseq of T cells from the large intestine of hCom1 colonized mice_raw sequence data</p>
sample1_scRNAseq of T cells from the small intestine of germ-free mice_raw sequence data
<p>sample1_scRNAseq of T cells from the small intestine of germ-free mice_raw sequence data</p>
Single cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis
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Slideseq cerebellum and scRNAseq cerebellum
<p>Contains the slideseq cerebellum dataset (Science paper), and the single-cell RNAseq L1 cerebellum dataset (from mouse-brain.org).</p> <p>For use with Giotto, and Giotto dataset tutorials.</p>
Raw count data with annotation in h5ad format of scRNAseq of blood and matched temporal artery in Giant Cell Arteritis
<p>Raw count data with annotation in h5ad format of scRNAseq of blood and matched temporal artery in Giant Cell Arteritis</p>
scRNAseq datasets of cranial myogenic progenitors using Mesp1 and Myf5 lineages
<p><span><span>How distinct cell fates are manifested by direct lineage ancestry from bipotent progenitors, or by specification of individual cell types is a key question for understanding the emergence of tissues. The interplay between skeletal muscle progenitors and associated connective tissue cells provides a model for examining how muscle functional units are established. Most craniofacial structures originate from the vertebrate-specific neural crest cells except in the dorsal portion of the head, where they arise from cranial mesoderm. </span><span>Here, using multiple lineage-tracing strategies combined with single cell RNAseq and in situ analyses</span><span>, we identify bipotent progenitors expressing <em>Myf5 </em>(an upstream regulator of myogenic fate)</span><span> that give rise to both muscle and juxtaposed connective tissue. Following this </span><span>bifurcation</span><span>, muscle and connective tissue cells retain complementary signalling features and maintain spatial proximity. </span><span>Disrupting myogenic identity shifts muscle progenitors to a connective tissue fate. The emergence of <em>Myf5</em>-derived connective tissue is associated with the activity of several transcription factors, including <em>Foxp2</em>. Interestingly, this unexpected bifurcation in cell fate was not observed in craniofacial regions that are colonised by neural crest cells. Therefore, we propose that an ancestral bi-fated program gives rise to muscle and connective tissue cells in skeletal muscles that are deprived of neural crest cells.</span></span></p>
scRNAseq with drug treatment
<p>scRNAseq with drug treatment etc.</p>
Example ScRNAseq Dataset 3 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 treated sample (active agent and carrier). </p>
Assembled scRNAseq data for Urothelial cancer
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scRNAseq datasets of cranial myogenic progenitors using Mesp1 and Myf5 lineages
Open the record for dataset details and reuse information.
Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics [10X_scRNAseq]
GEO Series GSE141589. Drosophila melanogaster. 1 samples. Type: Expression profiling by high throughput sequencing.
Olfactory Sensory Neuron Diversity Beyond OR Genes in mice [scRNAseq]
GEO Series GSE224603. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
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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.