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1,133
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1,133 results for “single-cell analysis”
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>
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>
Single-cell Sequencing Analysis of Resectable/Borderline Resectable Pancreatic Cancer Patients
ClinicalTrials.gov study NCT06310902. IPD Sharing: NO. Countries: 1. Publications: 3.
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>
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>
Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma
<p>Here, we performed an integrative analysis of scRNA-seq and scATAC-seq data from four ccRCC patients and aimed to identify the key regulatory molecules that mediate tumor development and manipulate the function of immune cells.</p>
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (Invitro ArchR Object)
<p>This repo contains invitro ArchR object</p>
Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease (Invivo ArchR Object)
<p>This repo contains invivo ArchR object</p>
Cellular and molecular heterogeneities and signatures, and pathological trajectories of fatal COVID-19 lungs defined by spatial single-cell transcriptome analysis
<p>Spatial in-situ data analysis.</p>
BRACE: A Bayesian-based dimension reduction approach for single-cell alternative splicing analysis
<p>Datasets to demonstrate the utility of our BRACE method.</p>
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
<p>Collection of auxiliary data to reproduce the results from "Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease".</p> <p>Source code is available at: https://github.com/saezlab/MOFAcell</p> <p>Exceptions: Spatial data is excluded</p> <p>This folder contains processed data of the following publications, when using the data cite accordingly:<br> 1) Kuppe C, Ramirez Flores RO, Li Z, Hayat S, Levinson RT, Liao X, Hannani MT, Tanevski J, Wünnemann F, Nagai JS, et al (2022) Spatial multi-omic map of human myocardial infarction. Nature 608: 766–777<br> 2) Ramirez Flores RO, Lanzer JD, Holland CH, Leuschner F, Most P, Schultz J-H, Levinson RT & Saez-Rodriguez J (2021) Consensus Transcriptional Landscape of Human End-Stage Heart Failure. J Am Heart Assoc 10: e019667<br> 3) Reichart D, Lindberg EL, Maatz H, Miranda AMA, Viveiros A, Shvetsov N, Gärtner A, Nadelmann ER, Lee M, Kanemaru K, et al (2022) Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies. Science 377: eabo1984<br> 4) Chaffin M, Papangeli I, Simonson B, Akkad A-D, Hill MC, Arduini A, Fleming SJ, Melanson M, Hayat S, Kost-Alimova M, et al (2022) Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy. Nature 608: 174–180 </p>
Data accompanying "Standardised workflow for mass spectrometry-based single-cell proteomics data analysis using the scp package"
<p>Data and scripts accompanying the paper <em>Standardised workflow for mass spectrometry-based single-cell proteomics data analysis using scp</em>.</p> <ul> <li>d.zip contains raw MS data from samples run on timsTOF SCP.</li> <li>raw.zip contains raw MS data from samples run on orbitrap mass spectrometers (Orbitrap Fusion Lumos Tribrid and Exploris 240).</li> <li>mzML.zip contains raw MS data in mzML format from all samples.</li> <li>sage.zip contains output results from the sage software (results.sage.tsv and quant.tsv) as well as configuration files (results.json) for both orbitrap (cbio) and timsTOF (giga) data.</li> <li>sample_annotation.zip contains csv files with samples annotation for each acquisition batch and used to build the colData.</li> <li>example_subset.zip contains csv files for short example datasets displayed in the paper.</li> <li>scp.rds file contains the initial QFeatures object of the full dataset with 56 PSM sets corresponding to the 56 MS runs.</li> <li>build_QF_dataset.Rmd file is the script used to build the scp.rds file described above from sage outputs and sample annotation.</li> </ul> <p>These file descriptions are also available in the README.txt file.</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.