Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The covariance environment defines cellular niches for spatial inference

ABSTRACT: The covariance environment defines cellular niches for spatial inference

PROVIDER: PRJNA1032867 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR26544139.fastq.gz	Fastqsanger.gz
	SRR26544140.fastq.gz	Fastqsanger.gz

Items per page:

1 - 2 of 2

Similar Datasets

The covariance environment defines cellular niches for spatial inference

Project description:The tsunami of new multiplexed spatial profiling technologies has opened a range of computational challenges focused on leveraging these powerful data for biological discovery. A key challenge underlying computation is a suitable representation for features of cellular niches. Here, we develop the covariance environment (COVET), a representation that can capture the rich, continuous multivariate nature of cellular niches by capturing the gene-gene covariate structure across cells in the niche, which can reflect the cell-cell communication between them. We define a principled optimal transport-based distance metric between COVET niches and develop a computationally efficient approximation to this metric that can scale to millions of cells. Using COVET to encode spatial context, we develop environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA-seq data into a latent space. Two distinct decoders either impute gene expression across spatial modality, or project spatial information onto dissociated single-cell data. We show that ENVI is not only superior in the imputation of gene expression but is also able to infer spatial context to disassociated single-cell genomics data.

2024-03-08 | GSE246395 | GEO

The covariance environment defines cellular niches for spatial inference.

Project description:A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.

| S-EPMC11445396 | biostudies-literature

Topological velocity inference from spatial transcriptomic data

Project description:Topological velocity inference from spatial transcriptomic data

| PRJNA1232194 | ENA

Topological velocity inference from spatial transcriptomic data

Project description:Cell fate transition is a spatiotemporal process, however, previous work has largely neglected the spatial dimension. Incorporating space and time into models of cell fate transition would be a key step toward characterizing how interactions among neighboring cells, local niche factors, and cell migration contribute to tissue development. Here, we developed topological velocity inference (TopoVelo), a computational tool to infer spatial and temporal dynamics of cell fate transition from spatial transcriptomic data. We show that TopoVelo significantly improves the accuracy and spatial coherence of inferred cell ordering compared to previous methods. TopoVelo also reveals spatial cell state dependencies of ligand-receptor genes, spatial signatures of mouse neural tubes, and patterns of early differentiation in 3D cell culture.

2025-04-18 | GSE291200 | GEO

Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches (spatial)

Project description:Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches (spatial)

| PRJNA793008 | ENA

Spatial reconstruction of immune niches by combining photoactivatable reporter and single-cell RNA-seq

Project description:Cellular function is strongly dependent on surrounding cells and environmental factors. Current technologies are limited in characterizing the spatial location and unique gene-programs of cells in less structured and dynamic niches. Here we developed a method (NICHE-seq) that combines photoactivatable fluorescent reporters, two-photon microscopy and single-cell RNA-seq to infer the cellular and molecular composition of niches. We applied NICHE-seq to examine the high-order assembly of immune cell networks. NICHE-seq is highly reproducible in spatial tissue reconstruction, enabling identification of rare niche-specific immune subpopulations and unique gene-programs, including natural killer cells within infected B cell follicles and distinct myeloid states in the marginal zone. This study establishes NICHE-seq as a broadly applicable method for elucidating high-order spatial organization of cell types and their molecular pathways.

2017-12-22 | GSE104054 | GEO

Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches

Project description:Spatial proteogenomics reveals distinct and evolutionarily-conserved hepatic macrophage niches

| PRJNA793005 | ENA

Systematic inference of super-resolution cell spatial profiles from histology images

Project description:Systematic inference of super-resolution cell spatial profiles from histology images

| PRJNA1215397 | ENA

Cell type mapping reveals tissue niches and interactions in subcortical multiple sclerosis lesions [spatial transcriptomics]

Project description:We generated a paired snRNA-seq (n= 15) and spatial transcriptomics (n=19) dataset from subcortical chronic active and chronic inactive MS lesions, identifying spatial niches and key cell interactions driving inflammation and disease progression at the lesion rim. This repository offers access to all the trancriptomics data that was used in the paper. It includes, all FASTQ files for both transcriptomics, along with the necessary files for running spatial transcriptomic samples (H&E images and JSON files), as well as the curated atlas, all derived cell subtype atlases from the main atlas and all curated ST slides.

2024-10-21 | GSE279181 | GEO

SARS-CoV-2 Niches in Human Placenta Revealed by Spatial Transcriptomics

Project description:SARS-CoV-2 Niches in Human Placenta Revealed by Spatial Transcriptomics

| PRJNA924338 | ENA