Multi-cell type deconvolution using a probabilistic model of single-molecule DNA methylation haplotypes
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1000495 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Deconvolution methods infer quantitative cell type estimates from bulk measurement of mixed samples including blood and tissue. DNA methylation sequencing measures multiple CpGs per read, but few existing deconvolution methods leverage this within-read information. We develop CelFiE-ISH, which extends an existing method (CelFiE) to use within-read haplotype information. CelFiE-ISH outperforms CelFiE and other existing methods, achieving 30% better accuracy and more sensitive detection of rare cell types. We also demonstrate the importance of marker selection and tailoring markers for haplotype-aware methods. While here we use gold-standard short-read sequencing data, haplotype-aware methods will be well-suited for long-read sequencing.
2023-08-02 | GSE239605 | GEO
Project description:Continuous-trait probabilistic model for comparing multi-species functional genomic data
| PRJNA437983 | ENA
Project description:We developed a probabilistic method to simultaneously infer genome-wide heterogeneous evolutionary states. We applied the proposed method to analyze a cross-species DNA replication timing (RT) dataset from the same cell type in five primate species. We demonstrated that the proposed model enables discovery of genomic regions with distinct evolutionary patterns of RT.
2018-06-20 | GSE111733 | GEO
Project description:Multi-omics has the promise to provide a detailed molecular picture for biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimum structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to associate with a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30°C and 37°C, and ob-tained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofa-ciens at 37°C.
2023-08-18 | PXD042954 | Pride
Project description:Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation
| PRJNA726995 | ENA
Project description:The function of mammalian cells is largely influenced by their tissue microenvironment and by inter- celllular interactions. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function, by enabling a transcriptome wide evaluation of gene expression in-situ. A critical limitation of the current technologies, however, is that their resolution is limited to regions (spots) of sizes well beyond that of a single cell, thus providing measurements for cell-aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyzes are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states, which can not be clearly demarcated and may reflect important differences in the cells’ function and interaction with their surrounding. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi- resolution analysis for spatial transcriptomics, which explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods, and that it is also the first method to enable an estimate of gene expression by every cell type inside every spot. We then introduce an automated pipeline for analysis of a tissue, as well as comparison between tissues. We apply DestVI and this pipeline to study the response of lymph nodes to a pathogen and to explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a reliable view of the organization of these tissues, and that it is capable of identifying important cell-type specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software in the scvi-tools codebase (https://scvi-tools.org).
2021-05-05 | GSE173774 | GEO
Project description:The function of mammalian cells is largely influenced by their tissue microenvironment and by inter- celllular interactions. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function, by enabling a transcriptome wide evaluation of gene expression in-situ. A critical limitation of the current technologies, however, is that their resolution is limited to regions (spots) of sizes well beyond that of a single cell, thus providing measurements for cell-aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyzes are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states, which can not be clearly demarcated and may reflect important differences in the cells’ function and interaction with their surrounding. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi- resolution analysis for spatial transcriptomics, which explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods, and that it is also the first method to enable an estimate of gene expression by every cell type inside every spot. We then introduce an automated pipeline for analysis of a tissue, as well as comparison between tissues. We apply DestVI and this pipeline to study the response of lymph nodes to a pathogen and to explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a reliable view of the organization of these tissues, and that it is capable of identifying important cell-type specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software in the scvi-tools codebase (https://scvi-tools.org).
2021-05-05 | GSE173773 | GEO
Project description:The function of mammalian cells is largely influenced by their tissue microenvironment and by inter- celllular interactions. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function, by enabling a transcriptome wide evaluation of gene expression in-situ. A critical limitation of the current technologies, however, is that their resolution is limited to regions (spots) of sizes well beyond that of a single cell, thus providing measurements for cell-aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyzes are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states, which can not be clearly demarcated and may reflect important differences in the cells’ function and interaction with their surrounding. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi- resolution analysis for spatial transcriptomics, which explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods, and that it is also the first method to enable an estimate of gene expression by every cell type inside every spot. We then introduce an automated pipeline for analysis of a tissue, as well as comparison between tissues. We apply DestVI and this pipeline to study the response of lymph nodes to a pathogen and to explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a reliable view of the organization of these tissues, and that it is capable of identifying important cell-type specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software in the scvi-tools codebase (https://scvi-tools.org).
2021-05-05 | GSE173776 | GEO
Project description:Total mRNA seq was perfomed on human fetal gut tissue at CS22 on 3 samples having different RET haplotype which progressively reduce expression of RET. R haplotypes are resistant and S haplotypes are susceptible to Hirschsprung disease
2018-10-30 | GSE111341 | GEO
Project description:Apis mellifera mitochondrial haplotypes
| PRJEB67526 | ENA