Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Dataset from: A lightweight framework for chromatin loop detection at the single-cell level

This dataset includes the following components: <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>

opencc-by-4.0May 2023View details →

The contribution of multiplexing single cell RNA sequencing in acute myeloid leukemia

Figures and supplimentary data of article : " The contribution of multiplexing single cell RNA sequencing in acute myeloid leukemia"

opennotspecifiedMar 2023View details →

Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects. in A global approach for natural history museum collections

Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects.

Immune single-cell RNA-seq data from PyMT-M tumor and its peripheral blood samples

Immune single-cell RNA-seq data collection and preprocessing Blood and tumor samples were harvested from PyMT-M tumor-bearing mice. Blood samples (n=3) are collected retro-orbitally using caliper tubes and processed with red blood cell lysis buffer (Tonbo Biosciences) before library preparation. A tumor sample are dissociated with Tumor Dissociation Kit following the manufacturer’s instructions (Miltenyi Biotec). After isolation and filtering through a 70µm filter, CD45+DAPI- cells were sorted using FACSAria cell sorter (BD Biosciences) at the Cytometry and Cell Sorting Core. The single-cell libraries were prepared using Chromium Controller (10X Genomics) at the Single Cell Genomics Core and sequenced using NovaSeq 6000 at the Genomics and RNA Profiling Core of Baylor College of Medicine. The FASTQ files were processed using Cell Ranger pipelines (10X Genomics) to generate feature-barcode matrices. Integrating immune single-cell RNA-seq data from the blood and tumor of PyMT-M mouse We followed the Seurat tutorial on single-cell RNA integration from <a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>. Specifically, both datasets were library-size normalized and log-scaled. Then, variable genes from both datasets were extracted, and overlapped variable genes were used as anchors to integrate the two datasets to generate a combined immune single-cell RNA-seq dataset.   Immune cell type annotation using SingleR After having the integrated immune single-cell RNA-sequencing data, we performed the standard pipeline for clustering, including scaling the expression data, performing dimension reduction using PCA and UMAP, and finding clusters by a shared nearest neighbor (SNN) modularity optimization (<a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>). Then, we used the R package SingleR to assign cell-type labels to each identified cluster using the ImmGen reference data from the Immunological Genome Project.

Endothelial single-cell Obesity Atlas

Ref to paper DOI: <a href="https://doi.org/10.1038/s42255-022-00674-x">10.1038/s42255-022-00674-x</a> <ul> <li>Single cell RNA-seq objects for 7 mouse organs, combined time points.</li> <li>Combined organs, 3 months time point object.</li> </ul> Format: .h5ad  Package used for transformation from/ to Seurat object: sceasy

(Preprocessing) Single-Cell Multi-Omics Identifies Chronic Inflammation as a Driver of TP53 mutant Leukaemic Evolution

Files and scripts for preprocessing of dataset related to our publication titled "Single-Cell Multi-Omics Identifies Chronic Inflammation as a Driver of TP53 mutant Leukaemic Evolution".

Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning

The benchmark datasets used to evaluate Gaussian noise augmentation-based scRNA-seq contrastive learning (GsRCL) against scRNA-seq cell-type identification tasks.

Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves

Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves

Profiling single cancer cell metabolism via high-content SRS imaging with chemical sparsity

Raw data for Profiling single cancer cell metabolism via high-content SRS imaging with chemical sparsity

opencc-by-4.0May 2023View details →

Supporting data for "Studying stochastic systems biology of the cell with single-cell genomics data"

The dataset tar.gz files contain the raw unspliced and spliced count matrices generated by kallisto|bustools 0.26.0 from six datasets. The GVP_2023...zip file is a mirror of the related GitHub repository.

Integration of imaging modalities with lipidomic characterization to investigate single cell MSCs potency metrics

This dataset contains images used in the analysis for the publication mentioned above.

opencc-by-4.0Aug 2023View details →

Single-Cell Transcriptomics Reveals Pre-existing COVID-19 Vulnerability Factors in Lung Cancer Patients

This dataset contains the processed scRNA-seq data and code used to investigate the association between lung cancers and COVID-19. Please refer to the article 'Single-Cell Transcriptomics Reveals Pre-existing COVID-19 Vulnerability Factors in Lung Cancer Patients' for the detailed data and method description. covid_cancer.rds: the processed scRNA-seq data saved as a Seurat object. notebooks.zip: Jupyter notebooks containing code for data analysis.

opencc-by-nd-4.0Aug 2023View details →

Single-cell somatic copy number variants in brain using different amplification methods and reference genomes

Variable and constant sized bins for GRCh38 and T2T-Chm13 were generated using the buildGenome scripts provided with Ginkgo (<a href="https://github.com/robertaboukhalil/ginkgo/tree/master/genomes/scripts">https://github.com/robertaboukhalil/ginkgo/tree/master/genomes/scripts</a>).

opencc-by-4.0Apr 2023View details →

Example spatial single-cell data

This is an example data set of spatially resolved single cell RNA-seq from the anterior region of sagittal mouse brain, which is generated by 10X Genomics Visium. It is stored in the Seurat class and contains clustering information generated by the Seurat package.

opencc-by-4.0Aug 2023View details →

Single-Cell Imaging Dataset: Hela FUCCI Cell Fluorescence Analysis

Hela FUCCI Cell Dataset: Fluorescence Intensity and Segmentation 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. Contents: 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. Purpose: 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. Data Collection: The data were collected using fluorescence microscopy techniques, capturing the distinct fluorescence signals emitted by Hela FUCCI cells.  Usage: Researchers can use this dataset to: Investigate fluorescence patterns and intensities of Hela FUCCI cells. Develop and validate machine learning algorithms for cell segmentation and detection. Explore cellular behaviors and dynamics under various experimental conditions. Citation: If you use this dataset in your research, please cite the original source to acknowledge its contribution. Access and Availability: 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.

openother-openSep 2023View details →

Using LLM Models and Explainable ML to Analyse Biomarkers at Single Cell Level

Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features for inferring a cell type’s disease state based on its transcriptomic profile. We ascertain the versatility of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types. Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals and those suffering from ulcerative colitis (UC). This demonstrates our approach’s efficacy in bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to elucidate underlying molecular disease mechanisms using these models.

opencc-by-4.0Oct 2023View details →

Processed Single-cell RNA-sequencing data from adult recurrent respiratory papillomatosis

.rds file (read in R) that contains processed single-cell RNA-sequencing data from 13 untreated adult recurrent respiratory papillomatosis clinical samples. The associated raw RNA sequencing data is available through The Database of Genotypes and Phenotypes (dbGaP), accession number phs003349.v1.p1. Sequences were also alsigned to a, HPV reference to allow quantification of HPV 6 or 11 gene expression.

Multi-omics data analysis for rare population inference using single-cell graph transformer

## 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. ## Data Collection The data was collected using GEO Database. ## Data Format The data is stored as  TSV file and MTX file where each row represents a gene and each column represents a sample.  ## Variables - Gene IDs: Gene Symbols (e.g., MALAT1) - Sample IDs: Sample identifiers (e.g., AAACATGCAAATTCGT-1) - Expression level: Row gene expression level.