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1,353 results for “Single-cell transcriptomics”
Gleaning Euglenozoa-specific DNA polymerases in public single-cell transcriptome data
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Single-cell spatial transcriptomics and proteomics of APOE Christchurch in 5xFAD and PS19 mice
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Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
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Data from: Three-dimensional single-cell transcriptome imaging of thick tissues
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Single-cell spatial transcriptomics of ACAN cKO in WT and 5xFAD mice
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Data from: Single-cell transcriptomic analysis of tumor-derived fibroblasts and normal tissue-resident fibroblasts reveals fibroblast heterogeneity in breast cancer
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Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
<p>This repository contains input files from the synthetic, curated, and processed experimental single-cell gene expression datasets used in BEELINE.</p> <p>New in version 3:<br> 1) Ground-truth networks used for analysis of experimental scRNA-seq datasets for mouse and human datasets<br> 2) Changed license to CC BY-NC 4.0 from GPL v3.0 to account for the non-commercial clause for the network data</p>
Single-cell Transcriptomic Analysis Identifies Extensive Heterogeneity in the Cellular Composition of Mouse Achilles Tendons
<p>Tendon is a dense connective tissue that stores and transmits forces between muscles and bones. Cellular heterogeneity is increasingly recognized as an important factor in the biological basis of tissue homeostasis and disease, yet little is known about the diversity of cell types that populate tendon. To address this, we determined the heterogeneity of cell populations within mouse Achilles tendons using single-cell RNA sequencing. In assembling a transcriptomic atlas of Achilles tendons, we identified 11 distinct types of cells, including 3 previously undescribed populations of tendon fibroblasts. This table contains differential gene expression for specific genes identified in distinct populations of cells within tendon tissue. </p>
Data from: Division of functional roles for termite gut protists revealed by single-cell transcriptomes
<p>The microbiome in the hindgut of wood-feeding termites comprises various species of bacteria, archaea, and protists. This gut community is indispensable for the termite, which thrives solely on recalcitrant and nitrogen-poor wood. However, the difficulty in culturing these microorganisms has hindered our understanding of the function of each species in the gut. Although protists predominate in the termite gut microbiome and play a major role in wood digestion, very few culture-independent studies have explored the contribution of each species to digestion. Here, we report single-cell transcriptomes of four protists species comprising the protist population in worldwide pest <em>Coptotermes formosanus</em>. Comparative transcriptomic analysis revealed that the expression patterns of the genes involved in wood digestion were different among species, reinforcing their division of roles in wood degradation. Transcriptomes, together with enzyme assays, also suggested that one of the protists, <em>Cononympha leidyi</em>, actively degrades chitin and assimilates it into amino acids. We propose that C. leidyi contributes to nitrogen recycling and inhibiting infection from entomopathogenic fungi through chitin degradation. Two of the genes for chitin degradation were further revealed to be acquired via lateral gene transfer (LGT) implying the importance of LGT in the evolution of symbiosis. Our single-cell-based approach successfully characterized the function of each protist in termite hindgut and explained why the gut community includes multiple species.</p>
Processed datasets used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics"
<p>These are the processed anndata objects used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics".</p> <p>These datasets are stored as .h5ad files and are intended to be used with the <a href="https://scanpy.readthedocs.io/en/stable/api.html">Scanpy</a> package in Python. They contain all relevant cell type annotation, unnormalized and transformed gene expression counts, as well as the inferred intercellular fow networks inferred by FlowSig.</p>
Single-cell and spatial transcriptomics of stricturing Crohn's disease
<p>This folder contains the spatial transcriptomics data + code. This code was generated by members of the Smillie Lab @ MGH and Harvard Medical School.</p> <ul> <li><strong>github.tar.gz: </strong>spatial analysis code and data</li> <li><strong>anndata.h5ad:</strong> anndata object (scanpy)</li> <li><strong>V*tar.gz:</strong> raw spatial transcriptomics files</li> </ul> <p>The <strong>github.tar.gz</strong> folder contains everything you need to reproduce the spatial transcriptomics figures. It is structured as follows:</p> <ul> <li><strong>1.BayesPrism:</strong> code for running BayesPrism on spatial data</li> <li><strong>2.SparCC:</strong> code for running SparCC on spatial data</li> <li><strong>3.Lasso: </strong>code for running lasso regression on spatial data</li> <li><strong>4.Analysis: </strong>code for reproducing all figures in the paper</li> <li><strong>4.Analysis/1.analysis.r</strong><strong>: </strong>script to reproduce all figures in the paper ***</li> <li><strong>code:</strong> code library containing all necessary functions</li> <li><strong>load_data.r: </strong>code to load the single-cell and spatial datasets</li> <li><strong>sco.rds:</strong> single-cell analysis object (10X Chromium) formatted as an R list</li> <li><strong>vis.rds:</strong> spatial analysis object (10X Visium) formatted as an R list</li> </ul> <p>All scripts are numbered. You need to run everything in order. For convenience, we include the output files for <strong>1.BayesPrism</strong>, <strong>2.SparCC</strong>, and <strong>3.Lasso</strong>, allowing you to skip straight to the analysis code in <strong>4.Analysis.</strong></p> <p>To reproduce all figures in the paper, you need to do the following:</p> <ol> <li>Edit your PROJECT_FOLDER in the header of <strong>load_data.r</strong></li> <li>Install the packages listed at the top of <strong>load_data.r</strong></li> <li>Go to the <strong>4.Analysis</strong> directory, start an interactive R session, and type:<br>> source('1.analysis.r')</li> </ol> <p>This will load the beginning of the <strong>1.analysis.r</strong> script (until the stop() statement on line 68). You can run the code in two different ways:</p> <ol> <li>You can step through the code line by line in your interactive R session (starting at line 68)</li> <li>Alternatively, remove the stop() statement from the script, then run the code start to finish</li> </ol> <p>If you encounter any errors, try to debug them using a combination of Google+ChatGPT. If you still have trouble, please contact the Smillie Lab.</p> <p><strong>Note: </strong>the single-cell and spatial code are also available on GitHub. However, the spatial analysis requires large files that cannot be hosted on GitHub. Therefore, it is better to download the code + files from Zenodo. The GitHub link is provided below:</p> <p><a href="https://github.com/LJ-Kong/fibrosis_scRNA_stRNA">https://github.com/LJ-Kong/fibrosis_scRNA_stRNA</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.
<p>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133</p>
Inferring causal cell-cell communication from single-cell transcriptomics
<p>Data used to produce the analysis in "Inferring causal cell-cell communication from single-cell transcriptomics" by Almet and Nie (2022)</p>
Single-cell transcriptomics data set for plasmodium falciparum 3D7 asexual and sexual stages
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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>
Spatial reconstruction of the early hepatic transcriptomic landscape after an acetaminophen overdose using single-cell RNA sequencing
<p>We leveraged single-cell RNA sequencing to understand the early molecular events that define the hepatocyte response to acetaminophen exposure at a subpopulation level. We spatially assigned hepatocytes along the portol-central vein axis by using established landmark genes. By spatially assigning the hepatocytes were were able to account for innate differences in gene expression that existed along this gradient. The excel files herein provide the full list of differentially expressed genes between key subpopulations of interest. Additionally, we classified genes as either pericentral zonated, periportal zonated, or non-zonated, the full list of genes and their spatial assignments are included in the appropriate excel file. </p>
Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation
<p>Raw microscopy images underlying the publication "Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation"</p>
Single-cell and spatial transcriptomics of cardiac neural crest reveal a dual role of vinculin in Tgf beta signaling and cell-extracellular matrix interaction during cardiac outflow tract development
<p>single-cell RNA-seq data analysis:</p> <p>all.cncc.combined.EMBO.mapped.Rdata: public CNCC single-cell RNA-seq data integration</p> <p>E13.5_CNCC_merged_updated.Rdata: single-cell RNA-seq data of E13.5 CNCC generated in Elly lab</p> <p>all.seurat.GFP.Rdata: scRNA-seq data with GFP detected</p> <p>visium.merge_AB.control.Rdata: R processed ST data for slice A and B</p> <p>visium.merge_CD.mutant.Rdata: R processed ST data for slice A and B</p> <p> </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 Transcriptomics Reveals Pre-existing COVID-19 Vulnerability Factors in Lung Cancer Patients
<p>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.</p> <p>covid_cancer.rds: the processed scRNA-seq data saved as a Seurat object.</p> <p>notebooks.zip: Jupyter notebooks containing code for data analysis.</p>
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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.