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datasets available to search
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Dataset results
463 results for “cell detection”
The cell adhesion molecule Sdk1 shapes assembly of a retinal circuit that detects localized edges
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A longitudinal study of DNA and RNA viruses plasma detection in allogeneic hematopoietic stem cell transplant recipients
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Data from: ESCRT-III-dependent adhesive and mechanical changes are triggered by a mechanism detecting alteration of Septate Junction integrity in Drosophila epithelial cells
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Data from: Molecular analysis of carnivore Protoparvovirus detected in white blood cells of naturally infected cats
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Data from: The inhibitory SAPS3 – AMPK interaction detected in HEK293 cells is not detectable in muscle or liver from humans or mice
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Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands
<p>Common SNPs from gnomAD</p>
Robust detection of SARS-CoV-2 exposure in population using T-cell repertoire profiling
<p>The dataset contains processed T-cell receptor repertoire sequencing data from >1200 individuals of different sex and age. Note that only samples with good sequencing coverage are published (>10^5 reads per file). </p> <p>The main aim of our study is to find TCR sequence biomarkers and develop a bioinformatic pipeline that allows building an accurate and robust classifier that distinguishes COVID-19-convalescent donors from unexposed individuals. We performed immunosequencing of the rearranged TCR α and β regions for PBMCs. For the cohort described in this study (Cohort-I) we sequence both chains of the TCR heterodimer as both of these chains are required to properly predict antigen recognition26. We ran conventional T-cell repertoire data analysis and pre-processed data to remove low-coverage samples. </p> <p>Of samples in Cohort-I which passed read count threshold, 383/377 TCR α/β samples were from healthy donors (SARS-CoV-2 PCR test negative or obtained prior to pandemic) and 890/848 were from COVID-19-positive patients. The majority of samples were accompanied by information on HLA class I and II alleles. Samples were prepared and sequenced in nine batches.</p> <p>The metadata for both TCR alpha and beta repertoires contains the following information:</p> <div> <ul> <li>sequencing_date - date when seguencing was performed</li> <li>batch_name - one of the 9 unique batch identifiers</li> <li>sample_id, patient_id - information on sample identifier and donor identifier</li> <li>COVID_status, COVID_IgG, COVID_IgM, COVID_PCR - information on COVID-19 status</li> <li>HLA-A.1, HLA-A.2, HLA-B.1, HLA-B.2, HLA-C.1, HLA-C.2 - MHC class I alleles</li> <li>HLA-DPB1.1, HLA-DPB1.2, HLA-DQB1.1, HLA-DQB1.2, HLA-DRB1.1, HLA-DRB1.2 - MHC class II alleles</li> <li>file_name - name of the corresponding file in <em>fmba_clonotype_usage_tables.zip </em>archive</li> </ul> </div> <p>Each file in <em>fmba_clonotype_usage_tables.zip </em>archive stores the information on either TCR alpha or beta repertoire. Each line in a file corresponds to the unique clonotype and each clonotype is accompanied with the following information:</p> <ul> <li>count - number of reads where the clonotype was detected</li> <li>freq - count of reads with the clonotype divided by thw whole number of reads in a sample</li> <li>cdr3nt, cdr3aa - nucleotide and amino acid sequences of TCR's CDR3 sequence</li> <li>v, d, j - the V/D/J segment name which was used for the clonotype's rearrangement</li> <li>VEnd, DStart, DEnd, JStart - information on VDJ junction positions </li> </ul> <p>We proceed with selecting a set of CDR3 sequences that can serve as biomarkers and form a feature list for COVID-19 status classifier. We also validate the resulting set of clonotypes in several ways. Co-occurence of specific TCR α and β clonotypes can serve as an independent validation for biomarkers and their co-association with some specific pathogen. Additional information on donor HLAs is provided to filter the set of biomarkers based on HLA restriction: association with donor HLA serves as an additional evidence for TCR specificity to a specific set of antigens presented in a given donor and allows detecting the fingerprint of past and present infection. Furthermore, clonotypes with similar sequences can be aggregated into 'metaclonotype' biomarkers based on clonotype graph analysis.</p> <p>Finally, we train various COVID-19 status classifiers on selected batches from Cohort-I data using different algorithms and incorporating different feature sets. Verification of the robustness of our results was performed using independent batches of the Cohort-I and data from Cohort-II published previously.</p>
Data for for Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing
<p>Datasets accompanying the paper https://doi.org/10.1101/2021.10.18.464684</p>
Data for "A unified model-based framework for doublet or multiplet detection in single-cell multiomics data"
<p>This repository contains all the data necessary for replicating, interpreting, and extending the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet or Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.</p>
3D tumor reconstruction using self-supervised registration and cell-detection-based area delineation
<p>Breast cancer tissue sequentially sectioned for 3D alignment.</p> <ul> <li>ndpi files contain renamed (all) and re-oriented whole slide images (IU, rotated by 180°) originally scanned</li> <li>Downsampled zip folder contains overview images of the individual WSIs</li> </ul>
Source data of scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
<p>The source data of the manuscript: scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.</p>
Dataset from: A lightweight framework for chromatin loop detection at the single-cell level
<p>This dataset includes the following components:</p> <ul> <li>Loops identified by scGSLoop at the single-cell level on the mES dataset and the hPFC dataset</li> <li>Consensus loops of each cell type. These consensus loops are derived from the single-cell results and represent the common loops shared across the cells of a particular cell type.</li> </ul> <p> </p>
Benchmark datasets for "Detecting T-cell expansion and quantifying clone survival from deep profiling of immune repertoires"
<p>T-cell receptor repertoire sequencing datasets describing time courses obtained for vaccination, normal aging and blood transplant cases. Datasets reported here were previously published (except for Tem/Tcm data), this is just a compendium of selected samples that is properly pre-processed and formatted.</p>
The Detection of Circulating Tumor Cells (CTCs) in Patients With Breast Cancer Undergoing Cryosurgery Combined With DC-CIK Treatment
ClinicalTrials.gov study NCT02450357. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Sentinel Lymph Node Mapping in Detecting Cancer That Has Spread to Lymph Nodes in Patients With Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00089310. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Detection of Circulating Tumor Cells (CTCs) in Patients With Colorectal Cancer Undergoing Cryosurgery Combined With DC-CIK Treatment
ClinicalTrials.gov study NCT02450422. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Cell-free tsRNA Signature for Early Detection of Hepatocellular Carcinoma
ClinicalTrials.gov study NCT07265271. IPD Sharing: NO. Countries: 1. Publications: 11.
Detecting Autologous Transfusion by Measuring Alterations in the Dynamics of Red Blood Cell Maturation and Recycling
ClinicalTrials.gov study NCT02684747. IPD Sharing: NO. Countries: 1. Publications: 10.
The Detection of Circulating Tumor Cells (CTCs) in Patients With Liver Cancer Undergoing Cryosurgery Combined With DC-CIK Treatment
ClinicalTrials.gov study NCT02416635. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Liquid Biopsy With Immunomagnetic Beads Capture Technique for Malignant Cell Detection in Body Fluid
ClinicalTrials.gov study NCT02891642. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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.