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174 results for “single-cell omics”
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>
scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data
<p>This repository contains the training data and source code to reproduce the results of our paper:<br>scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data</p> <p>More description can be also found in GitHub (https://github.com/LPH-BIG/scGraph2Vec).</p>
Single-cell -omics datasets containing a trajectory
<p>Contains datasets to compare trajectory inference methods. See <a href="https://benchmark.dynverse.org">https://benchmark.dynverse.org</a></p> <p>These files can be opened in R using `readRDS` .</p> <p>Each file is a list containing at least:</p> <ul> <li>expression and counts: filtered log<sub>2</sub> normalised expression files and raw counts</li> <li>milestone_network, milestone_percentages and divergence_regions: the trajectory model</li> </ul>
Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data
<p>We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.</p>
(SNP Array) Single-Cell Multi-Omics Identifies Chronic Inflammation as a Driver of TP53 mutant Leukaemic Evolution
<p>Single nucleotide polymorphism (SNP) array data files related our publication titled "Single-Cell Multi-Omics Identifies Chronic Inflammation as a Driver of <em>TP53 </em>mutant Leukaemic Evolution".</p>
Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy
<p>This repository contains code used to produce the results in: Till Braun, Michael Rade and Maximilian Merz et al., Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy</p> <p>Contents:<br>- Instructions for using the singularity image and R packages can be found here: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity">https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity</a><br>- "seurat_harmony.Rds" and "seurat_harmony_t.Rds": These Seurat objects were used to produce Figure 2 in this publication. "seurat_tcell_obj.Rds" is a subset of "seurat_harmony.Rds" and contains only T-cells. In addition, the metadata was extended by the output of TCR-Seq (using the R package scRepertoire). The following script uses the objects: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R">https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R</a></p> <p> </p>
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
<p>Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"</p> <p>Code available at https://github.com/MTreppner/multiomics_dgms</p>
Seurat objects for "Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state" (https://doi.org/10.1038/s41467-023-42762-w)
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Data for: Single-cell multi-omics in the medicinal plant Catharanthus roseus
<p>Advances in omics technologies now permit the generation of highly contiguous genome assemblies, detection of transcripts and metabolites at the level of single cells and high-resolution determination of gene regulatory features. Here, using a complementary, multi-omics approach, we interrogated the monoterpene indole alkaloid (MIA) biosynthetic pathway in <em>Catharanthus roseus</em>, a source of leading anticancer drugs. We identified clusters of genes involved in MIA biosynthesis on the eight <em>C. roseus</em> chromosomes and extensive gene duplication of MIA pathway genes. Clustering was not limited to the linear genome, and through chromatin interaction data, MIA pathway genes were present within the same topologically associated domain, permitting the identification of a secologanin transporter. Single-cell RNA-sequencing revealed sequential cell-type-specific partitioning of the leaf MIA biosynthetic pathway that, when coupled with a single-cell metabolomics approach, permitted the identification of a reductase that yields the bis-indole alkaloid anhydrovinblastine. We also revealed cell-type-specific expression in the root MIA pathway.</p>
SMILE: mutual information learning for integration of single-cell omics data
<p>Processed PBMC data for integration tutorial in https://github.com/rpmccordlab/SMILE.</p>
(Preprocessing) Single-Cell Multi-Omics Identifies Chronic Inflammation as a Driver of TP53 mutant Leukaemic Evolution
<p>Files and scripts for preprocessing of dataset related to our publication titled "Single-Cell Multi-Omics Identifies Chronic Inflammation as a Driver of <em>TP53 </em>mutant Leukaemic Evolution".</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>
Data for: Single-cell multi-omics in the medicinal plant Catharanthus roseus
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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>
Deciphering cell states and genealogies of human hematopoiesis with single-cell multi-omics [Aged 1]
GEO Series GSE219014. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Spatially Resolved Multi-Omics Single-Cell Analyses Inform Mechanisms of Immune Dysfunction in Pancreatic Cancer
GEO Series GSE205049. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state [RNA-seq-invitro]
GEO Series GSE236735. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Single-Cell multi-omics reveals disrupted gene regulatory landscape and cell differentiation by Wilms tumor-associated ENL mutation in the developing kidney (snATAC-Seq)
GEO Series GSE243868. Mus musculus. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Single-cell multi-omics characterize colorectal tumors, adjacent healthy tissue and matched organoids identifying CRC-unique features
GEO Series GSE294559. Homo sapiens. 72 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Deciphering cell states and genealogies of human hematopoiesis with single-cell multi-omics
GEO Series GSE219015. Mus musculus; Homo sapiens. 56 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
ScienceDex guides
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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.