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152 results for “Single-cell multiome”
Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics
<p>The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics</p> <p>Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.</p>
Additional data: Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring
<p>This repository provides additional data for the manuscript titled "Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring", currently under revision at Nature Genetics. The primary data cohort has been deposited in the HTAN data portal. This repository includes processed 10x Xenium spatial transcriptomic data for six TH-MYCN mice (three chemotherapy-treated and three treatment-naive) as well as processed scRNA-seq data for CHLA15 and CHLA20 neuroblastoma (NBL) cells. The scRNA-seq data includes mono-cultured, co-cultured cells with THP-1 macrophages, and co-culture cells treated with Afatinib/CRM197. </p>
Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance
<p>The below data are associated with our paper entitled "Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance."</p> <p>1) SNP-gene link predictions generated by pgBoost and existing methods SCENT (Sakaue et al. 2024 <em>Nat Genet</em>), Signac (Stuart et al. 2021 <em>Nat Methods</em>), ArchR (Granja et al. 2021 <em>Nat Genet</em>), and Cicero (Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><strong>pgBoost_scores.tsv.gz </strong>contains linking predictions made by pgBoost.</p> <p><strong>constituent_method_scores.tsv.gz</strong> contains linking predictions made by constituent methods.</p> <p><em><span>**NOTE: promoters (+/- 1kb from TSS) and candidate links >500kb are excluded from linking predictions (see manuscript)**</span></em></p> <p>Linking scores and percentiles are reported for each method (pgBoost score, SCENT FDR, Signac correlation, ArchR correlation, Cicero co-accessibility). Rank percentiles are computed as: 1 - (rank / n). When multiple links receive the same score, they are assigned the percentile of the top rank. Links unscored by each method (denoted by zeros* in the linking score column) are assigned a percentile equivalent to the percent of links unscored by the focal method. See the Methods section of the paper for further details on computing linking scores and summarizing scores across cell types and data sets.</p> <p>*Candidate links tested and assigned a co-accessibility of zero by the Cicero method are given a score of 1e-100 in the "Cicero" column to distinguish between unscored candidate links and candidate links assigned a partial correlation of zero (see Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><em>NOTE: The predictions associated with this release (version 2) were generated using an expanded set of data sets, an expanded training set, and corrected TSS coordinates.</em></p> <p>2) GWAS-derived evaluation SNP-gene link evaluation set.</p> <p><strong>gwas_evaluation.tsv</strong>: GWAS-derived evaluation SNP-gene link evaluation set. Column 1 provides SNP coordinates in the format <chr-start-end>. This evaluation framework was proposed by Weeks et al. 2024 <em>Nature Genetics</em> based on fine-mapping results from Kanai et al. <em>medrxiv</em> (see Methods: <em>Evaluation data sets</em> of Dorans et al.). "True" links (gold = 1) are non-coding variants fine-mapped to a focal trait (PIP > 0.1) with a coding variant for exactly one candidate gene within 1 Mb attaining PIP > 0.5 for the same trait. "False" links (gold = 0) are candidate SNP-gene pairs involving a SNP with a "true" link. This file of SNP-gene links was adapted from credible set-gene links <a href="https://github.com/Deylab999/GWAS_benchmark_IGVF/blob/bb91d08cc02d59cdd829eb1430569057ac26c5fe/V2G/ENCODE_E2G_2023/UKBiobank.ABCGene.anyabc.tsv">here</a> (the "truth" column defines true/false links) by identifying SNPs with PIP > 0.1 within each credible set-gene link.</p>
Source data for paper "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data"
<p>Sample-paired scRNA-seq and scATAC-seq data collected from human peripheral blood mononuclear cells with <em>Staphylococcus aureus</em><em> </em>infection. ScATAC-seq data collected from human peripheral blood mononuclear cells with <em>COVID-19</em> infection. </p>
Single-cell multiomics of neuronal activation reveals context-dependent genetic control of brain disorders
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scMEGA: Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference
<p>The increasing availability of single-cell multi-omics data allow to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) to infer gene regulatory network by combining single cell gene expression and chromatin accessibility profiles. This allows to study complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease development. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction.</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>
Unified Cross-modality Integration and Inference of Single-Cell Multiomics Data with Deep Contrastive Learning
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Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases
<p>BPNet models for scoring SNPs in cell types of the human retina</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>
Aligned Cross-modal Integration and Regulatory Heterogeneity Characterization of Single-Cell Multiomic Data with Deep Contrastive Learning
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Single-cell multiome profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression [multiome]
GEO Series GSE273594. Homo sapiens. 8 samples. Type: Other.
Single-Cell Epigenomics Uncovers Heterochromatin Instability and Transcription Factor Dysfunction during Mouse Brain Aging [snATAC-Seq] [10X Multiome]
GEO Series GSE294772. Mus musculus. 142 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Third-party reanalysis.
Single-cell multiome (ATAC and RNA) landscapes of human islets in normoglycemia, prediabetes and type 2 diabetes
GEO Series GSE200044. Homo sapiens. 40 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Single-cell multiomic analysis of thymocyte development reveals drivers of CD4+ T cell and CD8+ T cell lineage commitment
GEO Series GSE186078. Mus musculus. 19 samples. Type: Expression profiling by high throughput sequencing; Other.
Comparative single-cell multiome identifies evolutionary changes in neural progenitor cells during primate brain development
GEO Series GSE241429. Macaca mulatta; Mus musculus. 33 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Single-cell Multiomic profiling of soybean cotyledon-stage seeds
GEO Series GSE243174. Glycine max. 2 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina [10x multiome]
GEO Series GSE277326. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Joint single-cell multiomic analysis identifies Aebp2 as a key regulator in Wnt3a induced asymmetric stem cell division
GEO Series GSE168637. Mus musculus. 134 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Single-cell MultiOmics and spatial transcriptomics demonstrate neuroblastoma developmental plasticity
GEO Series GSE183729. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
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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)
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DANDI Archive for NWB datasets
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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.