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32 results for “tumor atlas”
Primary Breast Tumor Atlas
<p>Integrated scRNA-seq atlas of primary breast tumors containing 236,363 cells from 119 biopsy samples across eight original source datasets. <a title="Persistent link using digital object identifier" href="https://doi-org.foyer.swmed.edu/10.1016/j.xcrm.2024.101511" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.xcrm.2024.101511</span></a></p>
Additional data: Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring
<p>This repository provides additional data for the manuscript titled "Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring", currently under revision at Nature Genetics. The primary data cohort has been deposited in the HTAN data portal. This repository includes processed 10x Xenium spatial transcriptomic data for six TH-MYCN mice (three chemotherapy-treated and three treatment-naive) as well as processed scRNA-seq data for CHLA15 and CHLA20 neuroblastoma (NBL) cells. The scRNA-seq data includes mono-cultured, co-cultured cells with THP-1 macrophages, and co-culture cells treated with Afatinib/CRM197. </p>
A Single-Cell Tumor Immune Atlas for Precision Oncology
<p><strong>Publication version of the Single-Cell Tumor Immune Atlas</strong></p> <p>This upload contains:</p> <ul> <li><strong>TICAtlas.rds:</strong> an rds file containing a Seurat object with the whole Atlas</li> <li><strong>TICAtlas.h5ad:</strong> an h5ad file with the whole Atlas</li> <li><strong>TICAtlas_downsampled.rds:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_downsampled.h5ad:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_metadata.csv: </strong>a comma-separated text file with the metadata for each of the cells</li> </ul> <p>All the files contain the following patient/sample metadata variables:</p> <ul> <li>patient: assigned patient identifiers</li> <li>nCountRNA and nFeatureRNA: number of UMIs and genes per cell</li> <li>percent.mt: percentage of mitochondrial genes</li> <li>gender: the patient's gender (male/female/unknown)</li> <li>source: dataset of origin</li> <li>subtype: cancer type (abbreviations as indicated in the preprint)</li> <li>kmeans_cluster: patients clusters, NA if filtered out before clustering</li> <li>lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types)</li> </ul> <pre> </pre> <p>If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the <strong>TICAtlas_metadata.csv </strong>file.</p> <p>For more information, <a href="https://genome.cshlp.org/content/early/2021/09/21/gr.273300.120.">read our paper</a>, <a href="https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas">check our GitHub</a> and our <a href="https://singlecellgenomics-cnag-crg.shinyapps.io/TICA/">ShinyApp</a>.</p> <p>h5ad files can be read with Python using <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>, rds files can be read in R using <a href="https://satijalab.org/seurat/">Seurat</a>. For format conversion between AnnData and Seurat we recommend <a href="https://mojaveazure.github.io/seurat-disk/">SeuratDisk</a>. For other single-cell data formats you can use <a href="https://github.com/cellgeni/sceasy">sceasy</a>.</p>
Human pancreatic cancer single cell atlas reveals association of CXCL10+ fibroblasts and basal subtype tumor cells
Open the record for dataset details and reuse information.
Pan-Cancer T cell atlas from "The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells" (https://doi.org/10.1371/journal.pone.0315980)
<p>The scRNA-seq data were collected from previously published datasets (GSE140228, GSE139555, GSE155698, GSE121636, and GSE139324), adhering to the following selection criteria: 1) presence of T cells, 2) treatment-naïve patients, 3) solid tumors, and 4) inclusion of at least tumor and blood samples.<br>Each scRNA-seq dataset underwent separate preprocessing in R (v4.0.2). We filtered out cells from the original count matrices that had fewer than 200 genes detected or more than 10% mitochondrial UMI counts and we only kept genes detected in at least 3 cells. Then, we applied Seurat (v4.0.5) with default parameters for count data normalization and scaling. Each cell was assigned a cell cycle score using the CellCycleScoring function and we computed the difference between the G2M and S phase scores. This approach allows for the separation of non-cycling from cycling cells while minimizing the differences in cell cycle phase among proliferating cells. The SelectIntegrationFeatures function was ran with the nfeatures parameter set to 3,000 before merging all samples from each dataset. These integration features were then used for Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Clustering was performed using the Louvain algorithm with the resolution parameter set to 2.0 for all datasets. Finally, T cells were isolated based on CD3D and CD3G genes expression (CD3D or CD3G expression level > 0).</p> <p>To integrate heterogeneous data from different sources, a two-step procedure was applied. We first concatenated all datasets together and ran the scaling and PCA steps based on the top 3,000 highly variable genes identified by the FindVariableFeatures function with the “vst” method. Harmony was applied for batch effect correction then UMAP and clustering using the Louvain algorithm with the resolution parameter set to 2.0 were performed on the harmony reduction. Examining the result from the first clustering run, we identified contamination clusters and clusters that arose from unwanted factors: we removed the contamination clusters including low quality cells highly expressing marker genes associated with apoptosis and tissue dissociation operation, pancreatic acinar cells (expressing PRSS1, CLPS, PNLIP and CTRB1 among others), myeloid cells (expressing CD68) and B cells (expressing CD79A). Then, we performed the second run of integration and clustering excluding immunoglobulin, ribosome-protein-coding, and T cell receptor (TCR) genes (gene symbol with string pattern "^IGK|^IGH|^IGL|^IGJ|^IGS|^IGD|IGFN1", "^RP([0–9]+-|[LS])", and "^TRA|^TRB|^TRG" respectively) from the top 3,000 highly variable genes and regressing out the cell cycle difference effect as well as the percentage of mitochondrial UMI counts. Harmony (v0.1.0) was applied again for batch effect correction and UMAP was performed on the harmony reduction.<br>T cell subtypes identification and annotation was performed by clustering cells using the Louvain algorithm with the resolution parameter set to 4.1 after iterative testing from 3.5 to 5.0 by 0.1 (more granular than default), computing clusters signatures based on differential gene expression using the FindAllMarkers function with the “MAST” method and interrogating known gene markers expression. A resolution value of 4.1 was notably found to be the lowest resolution value enabling the correct separation of proliferating CD4+ T cells from proliferating CD8+ T cells.</p>
A single cell atlas of the human liver tumor microenvironment
GEO Series GSE146409. Homo sapiens. 151 samples. Type: Expression profiling by high throughput sequencing.
Atlas of Clinically Distinct Cell States and Cellular Ecosystems Across Human Solid Tumors
GEO Series GSE181817. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
A Single-Cell Tumor Immune Atlas for Precision Oncology
GEO Series GSE158803. Homo sapiens; Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Single-Cell Atlas of the Tumor Immune Microenvironment Across Syngeneic Murine Models
GEO Series GSE307143. Mus musculus. 30 samples. Type: Expression profiling by high throughput sequencing.
A single-cell atlas of the tumor, stromal and immune microenvironments in pancreatic cancer liver metastasis
GEO Series GSE197177. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
An RNA polymerase III tissue and tumor atlas uncovers context-specific activities linked to 3D epigenome regulatory mechanisms
GEO Series GSE306294. Homo sapiens. 24 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Single-cell atlas of dura cells from non-tumor or leptomeningeal metastasis mice
GEO Series GSE267322. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
A single-cell atlas of the mouse Multiple Myeloma immune microenvironment during tumor progression
GEO Series GSE271306. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
A Single-Cell Atlas of Liver Metastases of Colorectal Cancer Reveals the Reprogramming of the Tumor Microenvironment in Response to Preoperative Chemotherapy
GEO Series GSE178318. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
GeoMx Cancer Transcriptome Atlas (CTA) mRNA assay on 191 ROIs from a NSCLC tumor. [NSCLC_191_grid]
GEO Series GSE174749. Homo sapiens. 376 samples. Type: Expression profiling by high throughput sequencing; Other.
In Vivo Confocal Microscopy Tumor Atlas Study
ClinicalTrials.gov study NCT01012154. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Feasibility of Circulating Tumor DNA Based Minimal Residual Disease-Guided Adjuvant Therapy in Locally Advanced Gastric Cancer With Neoadjuvant Treatment: An Adaptive Trial (MRD-ATLAS)
ClinicalTrials.gov study NCT07257380. IPD Sharing: NO. Countries: 1. Publications: 0.
GeoMx Cancer Transcriptome Atlas (CTA) mRNA assay on 191 ROIs from a NSCLC tumor.
GEO Series GSE174743. Homo sapiens. 223 samples. Type: Expression profiling by high throughput sequencing; Other.
Single-cell atlas of lineage states, tumor microenvironment and subtype specific expression programs in gastric cancer
GEO Series GSE183904. Homo sapiens. 40 samples. Type: Expression profiling by high throughput sequencing.
Single-cell atlas of the developing brain to investigate the cellular origins of pediatric brain tumors
GEO Series GSE133531. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
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.