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1,921 results for “single cell analysis”
Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics (Data)
<p>Combined data for the manuscript `Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics` for all manuscript and supplemental information figures.</p> <p>Every folder contains the raw data and Jupyter notebook (python) for graph creation.</p> <p>Images are not enclosed but are shown in the manuscript.</p> <p> </p>
Single-cell transcriptome analysis reveals evolving tumor microenvironment induced by immunochemotherapy in nasopharyngeal carcinoma
<p>18 bulks and 11 single-cell RNA sequencing samples from paired before anti-PD-1 contained treatment and on treatment in patients with treatment-naive high-risk metastatic locally advanced NPCs were obtained. We aim to explore the mechanism of response heterogeneity for locally advanced NPCs underwent immunochemotherapy.</p>
PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian
<p>Datasets that PHLOWER used:</p> <ol> <li>benchmarking data</li> <li>multiome kidney organoid data</li> <li>xenium kidney orgnoid data</li> </ol>
Single-cell analysis reveals host S phase drives large T antigen expression during BK polyomavirus infection
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Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy
<p>scRNA-seq read count matrices from patient-derived PDAC organoids.</p>
Datasets used in Consensus Clustering Problem in Single-cell Transcriptome Data Analysis
<p>20 benchmark scRNA-seq datasets used in Consensus Clustering Problem in Single-cell Transcriptome Data Analysis. In every datasets .zip files, it provided raw data files, the processed R code and the corresponding R objects. The datasets.xlsx file provided the detailed information of 20 datasets.</p>
Single-Cell Imaging Dataset: Hela FUCCI Cell Fluorescence Analysis
<p>Hela FUCCI Cell Dataset: Fluorescence Intensity and Segmentation</p> <p>The "Hela FUCCI Cell Dataset" is a comprehensive collection of fluorescence microscopy data capturing the fluorescence intensity of Hela FUCCI cells. The dataset encompasses a diverse range of cellular images acquired through fluorescence imaging techniques, offering valuable insights into the cellular behavior and fluorescent signal patterns.</p> <p>Contents:</p> <p>Fluorescence Intensity Data: The dataset includes fluorescence images of Hela FUCCI cells captured in both red and green channels. These images represent the intensity levels of cellular fluorescence signals.</p> <p>Purpose:<br> The dataset serves as a resource for researchers and scientists interested in cellular fluorescence analysis. It supports investigations into cellular dynamics, cell cycle studies, and fluorescence signal patterns. Researchers can utilize this dataset to develop and evaluate image processing, analysis, and machine learning techniques for cell detection and fluorescence quantification.</p> <p>Data Collection:<br> The data were collected using fluorescence microscopy techniques, capturing the distinct fluorescence signals emitted by Hela FUCCI cells. </p> <p>Usage:<br> Researchers can use this dataset to:</p> <p>Investigate fluorescence patterns and intensities of Hela FUCCI cells.<br> Develop and validate machine learning algorithms for cell segmentation and detection.<br> Explore cellular behaviors and dynamics under various experimental conditions.</p> <p>Citation:<br> If you use this dataset in your research, please cite the original source to acknowledge its contribution.</p> <p>Access and Availability:<br> The dataset is openly available through Zendo, accessible via the following link: https://zenodo.org/. Researchers are encouraged to explore, analyze, and contribute to the dataset's applications and advancements in cellular fluorescence analysis.</p>
Multi-omics data analysis for rare population inference using single-cell graph transformer
<p>## MarsGT: For rare cell identification from matched scRNA-seq (snRNA-seq) and scATAC-seq (snATAC-seq),includes genes, enhancers, and cells in a heterogeneous graph to simultaneously identify major cell clusters and rare cell clusters based on eRegulon.</p> <p>## Data Collection The data was collected using GEO Database.</p> <p>## Data Format The data is stored as TSV file and MTX file where each row represents a gene and each column represents a sample. </p> <p>## Variables - Gene IDs: Gene Symbols (e.g., MALAT1) - Sample IDs: Sample identifiers (e.g., AAACATGCAAATTCGT-1) - Expression level: Row gene expression level.</p>
Host Response to Infection by Direct Analysis of Leukocyte Single Cell-type Gene Expression/transcript Abundance, Direct LS-TA
ClinicalTrials.gov study NCT06838780. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Host Response to Infection by Direct Analysis of Leukocyte Single Cell-type Gene Expression/transcript Abundance, Direct LS-TA. a Prospective Study Will Evaluate the Performance of Direct LS-TA in Tri
ClinicalTrials.gov study NCT06846645. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Single-cell Sequencing Analysis of Resectable/Borderline Resectable Pancreatic Cancer Patients
ClinicalTrials.gov study NCT06310902. IPD Sharing: NO. Countries: 1. Publications: 3.
Single cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis
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Data from: An experimental analysis of the molecular effects of trastuzumab (herceptin) and fulvestrant (falsodex), as single agents or in combination, on human HR+/HER2+ breast cancer cell lines and mouse tumor xenografts
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Single-cell glycomics analysis by CyTOF-Lec reveals glycan features defining cells differentially susceptible to HIV
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Link to dataset related to article "Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry"
<p>The interrogation of single cells is revolutionizing biology, especially our understanding of the immune system. Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus approaching the utility of cytometry by time of flight (CyTOF). However, flow cytometry suffers from autofluorescence and spreading error (SE) generated by errors in the measurement of photons mainly at red and far-red wavelengths, which limit barcoding and the detection of dim markers. Consequently, development of 28-color fluorescent antibody panels for flow cytometry is laborious and time consuming. Here, we describe the steps that are required to successfully achieve 28-color measurement capability. To do this, we provide a reference map of the fluorescence spreading errors in the 28-color space to simplify panel design and predict the success of fluorescent antibody combinations. Finally, we provide detailed instructions for the computational analysis of such complex data by existing, popular algorithms (PhenoGraph and FlowSOM). We exemplify our approach by designing a high-dimensional panel to characterize the immune system, but we anticipate that our approach can be used to design any high-dimensional flow cytometry panel of choice. The full protocol takes a few days to complete, depending on the time spent on panel design and data analysis.</p> <p> </p> <p> </p> <p>link related to dataset: https://flowrepository.org/id/FR-FCM-ZYV3</p>
Differential analysis of binarized single-cell RNA sequencing data captures biological variation
<p>Processed datasets used for binary differential analysis experiments.</p>
Long-Term Single Cell Analysis of S. pombe on a Microfluidic Microchemostat Array
<p>Although <em>Schyzosaccharomyces pombe</em> is one of the principal model organisms for studying the cell cycle, surprisingly few methods have characterized <em>S. pombe</em> growth on the single cell level, and no methods exist capable of analyzing thousands of cells and tens of thousands of cell division events. We developed an automated microfluidic platform permitting <em>S. pombe</em> to be grown on-chip for several days under defined and changeable conditions. We developed an image processing pipeline to extract and quantitate several physiological parameters including cell length, time to division, and elongation rate without requiring synchronization of the culture. Over a period of 50 hours our platform analyzed over 100000 cell division events and reconstructed single cell lineages up to 10 generations in length. We characterized cell lengths and division times in a temperature shift experiment in which cells were initially grown at 30°C and transitioned to 25°C. Although cell length was identical at both temperatures at steady-state, we observed transient changes in cell length if the temperature shift took place during a critical phase of the cell cycle. We further show that cells born with normal length do divide over a wide range of cell lengths and that cell length appears to be controlled in the second generation, were large newly born cells have a tendency to divide more rapidly and thus at a normalized cell size. The platform is thus applicable to measure fine-details in cell cycle dynamics, should be a useful tool to decipher the molecular mechanism underlying size homeostasis, and will be generally applicable to study processes on the single cell level that require large numbers of precision measurements and single cell lineages.</p>
Integration of Single-Cell Analysis and Mendelian Randomization Reveals NET1 as a Potential Key Player in Lung Cancer Pathogenesis
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Healthy woodchuck genome with viral sequences appended used for single-cell RNA-seq analysis
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Single-cell multi-omics analysis identifies context specific gene regulatory gates and mechanisms
<p>There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. <br>In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g., AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. <br>We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omic data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.</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.