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Dataset results
36 results for “Seurat”
FIGURES 1–8 in New species of Syphacia (Syphacia) Seurat (Nematoda: Oxyuridae) from Pseudomys species (Rodentia: Muridae) from central Australia
FIGURES 1–8. Syphacia (Syphacia) brevicaudata sp. nov. 1. Male, lateral view. 2. Egg, lateral view. 3. Female, lateral view. 4. Female, anterior end, lateral view. 5. Female, apical extremity, lateral view. 6. Female, en face view. 7. Male, posterior end, ventral view. 8. Male, posterior end, lateral view. Scale bars: 5, 6, 7, 8 = 25 µm; 2 = 50 µm; 1, 4 = 100 µm; 3 = 500 µm.
FIGURES 9–16 in New species of Syphacia (Syphacia) Seurat (Nematoda: Oxyuridae) from Pseudomys species (Rodentia: Muridae) from central Australia
FIGURES 9–16. Syphacia (Syphacia) pseudomyos sp. nov. 9. Male, lateral view. 10. Egg, lateral view. 11. Female, lateral view. 12. Female, en face view. 13. Female, apical extremity, lateral view. 14. Male, posterior end, ventral view. 15. Spicule and gubernaculum with accessory piece, lateral view. 16. Female, anterior end, lateral view. Scale bars: 12, 13, 14, 15 = 25 µm; 10 = 50 µm; 9, 16 = 100 µm; 11 = 500 µm.
Puram et al HNSCC data SingleCellExperiment and seurat object for MultiNicheNet
<p>Puram et al HNSCC data SingleCellExperiment and seurat object for MultiNicheNet</p>
Seurat objects associated with the tonsil cell atlas
<p>In the context of the Human Cell Atlas, we have created a single-cell-driven taxonomy of cell types and states in human tonsils. This repository contains the Seurat objects derived from this effort. In particular, we have datasets for each modality (scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics), as well as cell type-specific datasets. Most importantly, this is the input that we used to create the HCATonsilData package, which allows programmatic access to all this datasets within R.</p> <p>Version 2 of this repository includes cells from 7 additional donors, which we used as a validation cohort to validate the cell types and states defined in the atlas. In addition, in this version we also provide the Seurat object associated with the spatial transcriptomics data (10X Visium), as well as the fragments files for scATAC-seq and Multiome</p>
Seurat object input file - Galaxy Training Material
<p>A Seurat input file for conversion to AnnData.</p>
Figure 1 from: Garduño-Montes de Oca EU, López-Caballero JD, Mata-López R (2017) New records of helminths of Sceloporus pyrocephalus Cope (Squamata, Phrynosomatidae) from Guerrero and Michoacán, Mexico, with the description of a new species of Thubunaea Seurat, 1914 (Nematoda, Physalopteridae). ZooKeys 716: 43-62. https://doi.org/10.3897/zookeys.716.13724
Figure 1 - Thubunaea leonregagnonae sp. n. A Anterior end, female, lateral view B Apical view, female C Anterior end, female, lateral view showing excretory pore and vulva D Embryonated egg, lateral view E Larvated egg, lateral view F Caudal end, male, ventral view G Caudal end, male, lateral view, caudal papillae and ornamentation not shown H Caudal end, female, lateral view. Scale bars: A 90 µm; B 20 µm; C 370 µm; D 25 µm; E 30µm; F 250 µm; G 50 µm; H 370 µm.
Figure 2 from: Garduño-Montes de Oca EU, López-Caballero JD, Mata-López R (2017) New records of helminths of Sceloporus pyrocephalus Cope (Squamata, Phrynosomatidae) from Guerrero and Michoacán, Mexico, with the description of a new species of Thubunaea Seurat, 1914 (Nematoda, Physalopteridae). ZooKeys 716: 43-62. https://doi.org/10.3897/zookeys.716.13724
Figure 2 - Thubunaea leonregagnonae sp. n. Scanning electron micrographs. A Anterior end, male, lateral view showing deirid (D) B Apical view, female, showing sub-median papillae (P) and amphids (A) C Deirid, lateral view D Posterior end, female, ventrolateral view E Posterior end, male, ventral view F Caudal end, male, ventral view. Scale bars: A 50 µm; B 10 µm; C 2.5 µm; D 50 µm; E 100 µm; F 50 µm.
Seurat output files for testing
<p>Seurat output files for workflow testing, too big for Github</p>
Example H5 Seurat Object
Open the record for dataset details and reuse information.
Test data for Galaxy IUC Seurat Integrate tool
Open the record for dataset details and reuse information.
seurat objects of unsorted endothelial and perivascular cells - individual entities
<p><strong>Seurat objects of individual entities of unsorted endothelial and perivascular cells<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><strong> </strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p><em>-i) Fetal CNS unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from fetal brain (Fetal CNS).</p> <p><br><em>-ii) Fetal periphery unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from fetal periphery (Fetal peripheral organs).</p> <p><br><em>- iii) Adult control brain (temporal lobe) unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from adult/control brains (temporal lobes).<br><br><em>- iv) AVM unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from brain arteriovenous malformations (AVM) (a brain vascular malformation).<br><br><em>- v) LGG unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from lower-grade gliomas (LGG) (a brain tumor).<br><br><em>- vi) GBM unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from high-grade gliomas (glioblastoma (GBM)) (a brain tumor).<br><br><em>- vii) MET unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from brain metastasis (MET) (a brain tumor).<br><br><em>- viii) MEN unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is of unsorted endothelial and perivascular cells isolated from brain meningioma (MEN) (a brain tumor).</p>
Filtered Brain Cortex scRNAseq Seurat Object
<p>Filtered Brain Cortex scRNAseq Seurat Object</p>
seurat objects for : "scDual-Seq of Toxoplasma gondii-infected mouse bone marrow-derived dendritic cells reveals host cell heterogeneity and differential infection dynamics"
<p><strong>Summary</strong></p> <p>Here, we utilize Dual-scSeq to parse out heterogeneous transcription of bone marrow-derived dendritic cells (BMDCs) infected with T. gondii type I, RH (LDM) or type II, ME49 (PTG) parasites, over multiple time points post-infection (3 and 12h post-infection).</p> <p><strong>Data</strong></p> <p>This repository contains two files, one for each organism investigated (mouse and tgondii), in ".RDS" format generated using Seurat v.4.3.: </p> <p><strong>1. BMDC_infected_mouse.RDS </strong>- object containing normalized read counts (SCT assay) and corresponding metadata for murine BMDCs. </p> <p><strong> metadata columns </strong>describe: </p> <p> - orig.ident: <em>plate identity from smartSeq setup</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nUMI: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nUMI:<em> sum of reads per cell for t.gondii</em></p> <p> - mouse_nUMI: <em>sum of reads per cell for mouse</em></p> <p> - nGene: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nGene: <em>sum of genes per cell for t.gondii</em></p> <p> - mouse_nGene: <em>sum of genes per cell for mouse</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - well:<em> well_ID of plate used for smartSeq2</em></p> <p> - condition: <em>treatment of cell (one of 8: LDM infection for 3h, LDM infection for 12h, PTG infection for 3h, PTG infection for 12h, LDM Lysate control, PTG Lysate control, uninfected control or LPS control)</em></p> <p> - percent.mt: <em>percentage of transcript mapped to the mitochondrial genome</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - nFeature_SCT: <em>Gene count after normalization</em></p> <p> - nCount_SCT: <em>UMI count after normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - seurat_clusters: <em>Clusters identified by shared-nearest-neighbor (SNN) inspired graph-based clustering </em></p> <p> - toxo_clusters: <em>Clusters of t.gondii dataset of the corresponding infected cell </em></p> <p> - cell type: <em>Annotated subpopulation of infected cells</em></p> <p> - condition_celltype: <em>condition (see above) combined with celltype (see above)</em></p> <p> - cluster_celltype: <em>seurat_clusters (see above) combined with celltype (see above)</em></p> <p> - cluster_condition: <em>seurat_clusters (see above) combined with condition (see above)</em></p> <p> - UMAP_1: <em>Umap embedding coordinates x-axis</em></p> <p> - UMAP_2: <em>Umap embedding coordinates y-axis</em></p> <p> - cell_cycle_phase: <em>predicted cell cycle phase of murine host cells </em></p> <p> - cycle_phase_t.gondii: <em>predicted cycling phase of t.gondii in the corresponding infected host cell </em></p> <p><strong>2. BMDC_infected_tgondii.RDS </strong>- object containing normalized read counts (SCT assay) and corresponding metadata for murine BMDCs. </p> <p><strong> metadata columns </strong>describe: </p> <p> - orig.ident: <em>plate identity from smartSeq setup</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nUMI: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nUMI:<em> sum of reads per cell for t.gondii</em></p> <p> - mouse_nUMI: <em>sum of reads per cell for mouse</em></p> <p> - nGene: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nGene: <em>sum of genes per cell for t.gondii</em></p> <p> - mouse_nGene: <em>sum of genes per cell for mouse</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - well:<em> well_ID of plate used for smartSeq2</em></p> <p> - condition: <em>treatment of cell (one of 8: LDM infection for 3h, LDM infection for 12h, PTG infection for 3h, PTG infection for 12h, LDM Lysate control, PTG Lysate control, uninfected control or LPS control)</em></p> <p> - percent.mt: <em>percentage of transcript mapped to the mitochondrial genome</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - nFeature_SCT: <em>Gene count after normalization</em></p> <p> - nCount_SCT: <em>UMI count after normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - seurat_clusters: <em>Clusters identified by shared-nearest-neighbor (SNN) inspired graph-based clustering </em></p> <p> - mouse_clusters: <em>Clusters of mouse dataset of the corresponding infected cell </em></p> <p> - mouse_celltype: <em>Annotated subpopulation if infected cells</em></p> <p> - condition_celltype: <em>condition (see above) combined with mouse_celltype (see above)</em></p> <p> - cluster_celltype: <em>seurat_clusters (see above) combined with mouse_celltype (see above)</em></p> <p> - cluster_condition: <em>seurat_clusters (see above) combined with condition (see above)</em></p> <p> - UMAP_1: <em>Umap embedding coordinates x-axis</em></p> <p> - UMAP_2: <em>Umap embedding coordinates y-axis</em></p> <p> - cell_cycle_phase_mouse: <em>predicted cell cycle phase of murine host cells </em></p> <p> - cycle_phase_t.gondii: <em>predicted cycling phase of t.gondii in the corresponding infected host cell </em></p>
Filtered seurat object with metadata and analysis for Figure 4-5
<p>Please see metadata clustering resolution of 0.4 for clusters in figure, and annotations in "NK2". Please use ADT_denoised_iso_quant for adt (protein) normalized data.</p>
Seurat-Daten zum Schrader-Sequenzierungsprojekt von Tobias Lautwein - Unprocessiert von ALL
<p>Seurat-Daten zum Schrader-Sequenzierungsprojekt von Tobias Lautwein - Unprocessiert von ALL</p>
Seurat-RDS-Files 20220310
<p>Various RDS file from scRNAseq data up to date 20220310</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.