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36 results for “Seurat”
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>
DrugToleranceHallmarks_data_breast_seurat
<p>A repository of data for analysis/processing generated for the study of Transcriptional Hallmarks of Drug Tolerance in Hormone-Dependent Cancers.</p> <p>The respective github repository with a description of the data and all the performed analyses is available under https://github.com/barozziLab/DrugToleranceHallmarks.</p> <p>For reproducing the analyses, download the code from the github repository and the two zenodo repositories (10.5281/zenodo.14179561 and 10.5281/zenodo.14179578) into the same directory. The folder structure is similar between them and combining them will allow to load all the intermediate files and annotations necessary to run the scripts.</p>
Dataset to demonstrate the use of NicheNet on a Seurat object
<p>Single-cell RNAseq dataset to demonstrate the use of NicheNet directly on a Seurat object. The data came from "Medaglia et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq, Science 2017". This data was generated via the NICHE-seq method to characterize immune cell composition in the T cell area of inguinal lymph nodes, both in steady-state and 72 hours after lymphocytic choriomeningitis virus (LCMV) infection. The Seurat objects contain the aggregated data after applying the Seurat alignment pipeline.</p> <p><a href="https://zenodo.org/api/files/316332c2-9718-4869-864f-ab3bcd69fe7a/seuratObj.rds">seuratObj.rds</a>: full dataset</p> <p><a href="https://zenodo.org/api/files/316332c2-9718-4869-864f-ab3bcd69fe7a/seuratObj_test.rds">seuratObj_test.rds</a>: dataset with reduced size (only highly variable genes and CD8 T cells and monocytes) </p> <p> </p>
Seurat objects for the manuscript Single-cell consequences of X-linked meiotic drive in stalk-eyed flies
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Brain Transcriptome Single-cell (BTS) Atlas: Anndata, Seurat Object, CellTypist model, and Disorder Risk Geneplot
<p>Brain Transcriptome Single-cell Atlas (BTS) Anndata, Seurat object, and Celltypist model for further use of the atlas. The Celltypist model can be utilized to accurately annotate cell types in new datasets based on the atlas. Plots illustrating the expression profile for 3,380 neurological disorder risk genes across the atlas are also uploaded. Further availability for the data can be requested by the corresponding author.<br><br>This dataset is published in Kim, S., Lee, J., Koh, I.G. <em>et al.</em> An integrative single-cell atlas for exploring the cellular and temporal specificity of genes related to neurological disorders during human brain development. <em>Exp Mol Med</em> <strong>56</strong>, 2271–2282 (2024). https://doi.org/10.1038/s12276-024-01328-6</p>
Test data for Galaxy IUC Seurat_v5 tools
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Test data for Galaxy IUC Seurat Inspect & Manipulate Tool (Merge)
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Seurat objects - conjunctiva single-cell atlas
<p>We provide here the Seurat objects for:</p> <p>1) the whole conjunctiva atlas</p> <p>2) Goblet cells subclusters</p> <p>3) tuft cells subclusters</p> <p>4) basal cells subclusters.</p> <p>All objects can be obtained with the code provided on Github.</p>
Processed, annotated, seurat object
<p>The dataset contains an integrated, annotated Seurat v4 object. One can load the dataset into the <em>R</em> environment using the code below:</p> <pre><code>seurat_obj <- readRDS('PATH/TO/DOWNLOAD/seurat.rds')</code></pre> <p>The object has three assays: (I) RNA, (II) SCT and (III) integrated.</p>
Seurat_object_merged_PBMCs
<p>This dataset includes a Seurat object consisting of the merged count data from PBMCs of patients included in the study "Genomic Insights into Pediatric Intestinal Inflammatory and Eosinophilic Disorders using Single-sell RNA-sequencing." This is the unfiltered, unmapped data after reading the CellRanger count data into Seurat. It contains singlets identified via demuxlet. Barcodes that were not mapped to a genotype or were found not to be singlets were removed.</p>
EBAII n1 scRNAseq : Training intermediate Seurat (v5) objects
<p>Intermediate Seurat (v5) objects produced for the EBAII n1 single cell RNAseq training.</p> <ul> <li><code>01_TD3A_S5_Metrics.Tech_31053.4587.RDS</code> : Technical metrics (nFeature, nCount, ...)</li> <li><code>02_TD3A_S5_Metrics.All_31053.4587.RDS</code> : Biological metrics (%mito, %ribo, %stress)</li> <li><code>03_TD3A_S5_CC_31053.4587.RDS</code> : Estimated cell cycles scores and phase</li> <li><code>04_TD3A_S5_Metrics.Filtered_12508.4278.RDS</code> : Cells and features filtered according to metrics</li> <li><code>05_TD3A_S5_Doublets.filtered_12508.4035.RDS</code> : Cell doublets removed</li> <li><code>06_TD3A_S5_LogNorm_12508.4035.RDS</code> : LogNormalization applied</li> <li><code>07_TD3A_S5_Scaled.2k_12508.4035.RDS</code> : Scaling using 2000 HVGs</li> <li><code>08_TD3A_S5_Scaled.2k_Reg.PCrb_12508.4035.RDS</code> : Same with regression for <code>percent_rb</code> (% ribo)</li> <li><code>09_TD3A_S5_DimRed.PCA_12508.4035.RDS</code> : PCA with 50 components</li> <li><code>10_TD3A_S5_Clustered.0.8_12508.4035.RDS</code> : Louvain clustering with resolution 0.8</li> <li><code>11_TD3A.TDCT_S5_Merged_12926.3886</code> : Merging of the pre-processed TD3A and TDCT samples (each halved for its cells, using random subsampling with the seed 1337)</li> <li><code>12_TD3A.TDCT_S5_Integrated_12926.3886.RDS</code> : Results of TD3A and TDCT samples integration using 4 different methods (Seurat's CCA, RPCA, Harmony ; and standalone Harmony), and Louvain clustering for each (res 0.8)</li> <li><code>13_TD3A.TDCT_S5_Integrated_Annotated.RDS</code> : Results of the automatic cell type annotation</li> </ul>
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>
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>
On following pages: 4. Golden Spiny Mouse (Acomys russatus); 5. Arabian Spiny Mouse (Acomys dimidiatus); 6. Seurat's Spiny Mouse (Acomys seurati); 7. Northeast African Spiny Mouse (Acomys cahirinus); 8. Louise's Spiny Mouse (Acomys louisae); 9. Mullah Spiny Mouse (Acomys mullah); 10. Chudeau''s Spiny Mouse (Acomys chudeaui); 11. John's Spiny Mouse (Acomys johannis); 12. Gray Spiny Mouse (Acomys cineraceus); 13. Percival's Spiny Mouse (Acomys percivali); 14. Wilson's Spiny Mouse (Acomys wilsoni); 15. Kemp's Spiny Mouse (Acomys kempi); 16. Fiery Spiny Mouse (Acomys ignitus); 17. Muze Spiny Mouse (Acomys muzel); 18. Nguru Spiny Mouse (Acomys ngurui); 19. Southern African Spiny Mouse (Acomys spinosissimus); 20. Selous's Spiny Mouse (Acomys selousi); 21. Cape Spiny Mouse (Acomys subspinosus); 22. Congo Forest Rat (Deomys ferrugineus). in Muridae
On following pages: 4. Golden Spiny Mouse (Acomys russatus); 5. Arabian Spiny Mouse (Acomys dimidiatus); 6. Seurat's Spiny Mouse (Acomys seurati); 7. Northeast African Spiny Mouse (Acomys cahirinus); 8. Louise's Spiny Mouse (Acomys louisae); 9. Mullah Spiny Mouse (Acomys mullah); 10. Chudeau''s Spiny Mouse (Acomys chudeaui); 11. John's Spiny Mouse (Acomys johannis); 12. Gray Spiny Mouse (Acomys cineraceus); 13. Percival's Spiny Mouse (Acomys percivali); 14. Wilson's Spiny Mouse (Acomys wilsoni); 15. Kemp's Spiny Mouse (Acomys kempi); 16. Fiery Spiny Mouse (Acomys ignitus); 17. Muze Spiny Mouse (Acomys muzel); 18. Nguru Spiny Mouse (Acomys ngurui); 19. Southern African Spiny Mouse (Acomys spinosissimus); 20. Selous's Spiny Mouse (Acomys selousi); 21. Cape Spiny Mouse (Acomys subspinosus); 22. Congo Forest Rat (Deomys ferrugineus).
Seurat objects for "Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state" (https://doi.org/10.1038/s41467-023-42762-w)
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Processed Seurat objects from scRNA-seq data of the aging subventricular zone (SVZ) neurogenic niche with partial reprogramming
<p>This repository contains the processed Seurat objects from the publication "Restoration of neuronal progenitors by partial reprogramming in the aged neurogenic niche" (https://doi.org/10.1038/s43587-024-00594-3).</p> <p>Raw sequencing data is available at the Gene Expression Omnibus (GEO) under accession number GSE224438. Code used to process and analyze the data is available on GitHub (https://github.com/gitlucyxu/SVZreprogramming). </p> <p>These Seurat objects are filtered to high-quality singlets for samples included in the publication. Descriptions and notable metadata:</p> <ul> <li>svz_iOSKM_cohort1_toshare.rds - SVZ after whole-body partial reprogramming, cohort 1 <ul> <li>Celltype - cell type annotation</li> <li>Treatment - condition <ul> <li>untr: old control</li> <li>2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_iOSKM_cohort2_toshare.rds - SVZ after whole-body partial reprogramming, cohort 2 <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_2Dox0: old+OSKM</li> </ul> </li> <li>hash.ID - mouse ID (biological replicate)</li> </ul> </li> <li>svz_ciOSKM_toshare.rds - SVZ after SVZ-targeted partial reprogramming <ul> <li>Celltype - cell type annotation</li> <li>Age_Treatment - condition <ul> <li>young_untr: young control</li> <li>old_untr: old control</li> <li>old_Dox: old+OSKM(SVZ)</li> </ul> </li> <li>MULTI_classification_rescued - mouse ID (biological replicate)</li> </ul> </li> </ul> <p> </p> <p><em>Updated 2024/07/01 (v2): replaced a corrupted file. </em></p>
seurat objects of unsorted endothelial and perivascular cells - overall merges
<p><strong>Seurat objects of overall merges 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) Overall merge of all unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of unsorted endothelial and perivascular cells isolated from fetal brain, adult/control brains (temporal lobes), brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). <br><br><em>-ii) Overall merge of pathological unsorted endothelial and perivascular cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of unsorted endothelial and perivascular cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). </p>
seurat objects of FACS-sorted (CD31+/CD45-) endothelial cells - individual entities
<p><strong>Seurat objects of individual entities of FACS-sorted (CD31+/CD45-) endothelial 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 sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain (Fetal CNS).<br><br><em>-ii) Fetal periphery sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal peripheral organs (Fetal periphery).<br><br><em>- iii) Adult control brain (temporal lobe) sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from adult/control brains (temporal lobes).<br><br><em>- iv) AVM sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain arteriovenous malformations (AVM) (a brain vascular malformation).<br><br><em>- v) LGG sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from lower-grade gliomas (LGG) (a brain tumor).<br><br><em>- vi) GBM sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from high-grade gliomas (glioblastoma (GBM)) (a brain tumor).<br><br><em>- vii) MET sorted endothelial cells_seurat object.rds:</em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain metastasis (MET) (a brain tumor).<br><br><em>- viii) MEN sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain meningioma (MEN) (a brain tumor).</p>
seurat objects of FACS-sorted (CD31+/CD45-) endothelial cells - overall merges
<p><strong>Seurat objects of overall merges of FACS-sorted (CD31+/CD45-) endothelial cells<br></strong><em>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</em><br><em>https://www.nature.com/articles/s41586-024-07493-y</em><strong><br></strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><em>-i) Overall merge of all brain sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain, adult/control brains (temporal lobes), brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). <br><br><em>-ii) Overall merge of all sorted brain and peripehry endothelial cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain, fetal peripheral organs, adult/control brains (temporal lobes), brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). <br><br><em>-iii) Overall merge of pathological sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). <br><br><em>-iv) Overall merge of tumor sorted endothelial cells_seurat object.rds: </em><br> -> this seurat object is the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas). </p>
FIGURES 17–22. 17 in New species of Syphacia (Syphacia) Seurat (Nematoda: Oxyuridae) from Pseudomys species (Rodentia: Muridae) from central Australia
FIGURES 17–22. 17. Syphacia sp. 1 anterior end, lateral view. 18. Syphacia sp. 1 en face view. 19. Syphacia sp. 1, en face view. 20. Syphacia sp. 1 Egg, lateral view. 21. Syphacia sp. 2 en face view. 22. Syphacia sp. 2 mid-body, lateral view. Scale bars: 18, 19, 21 = 25µm; 17, 20 = 50µm; 22 = 100µm.
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.