Quartet
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB46123 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The Quartet Project aims to provide resources for QC of multiple types of omic technologies and the effective integration of diverse datasets from various scenarios. Large quantities of multi-omics materials, datasets, and best practices for their QC utilities were developed for whole process QC of large-scale, multi-center, and longitudinal multi-omics profiling.
2023-09-04 | GSE241900 | GEO
Project description:We generated Multiome RNA+ATAC data from the same cell from human PBMC. This served as a gold benchmark for a novel integration method for multi-omics data that we developed.
2021-12-02 | E-MTAB-11225 | biostudies-arrayexpress
Project description:We generated Multiome RNA+ATAC data from the same cell from human PBMC. This served as a gold benchmark for a novel integration method for multi-omics data that we developed.
2021-12-02 | E-MTAB-11226 | biostudies-arrayexpress
Project description:In this study we used single cell multi-omics profiling to create an atlas of the human YS to gain insights into its haematopoietic, metabolic and nutritive functions during early embryonic development. This contains CITE-seq data (surface protein and cytosolic RNA content) data from two biological replicates. Pooled lanes were demultiplexed using SoupOrCell (for alignment and demultiplexing software and version numbers, please see accompanying manuscript and protocols within this accession). Raw count files provided are directly as output by alignment software, without any quality control applied. Quality control is described in accompanying manuscript methods. Metadata by barcode are provided as supplementary tables in accompanying manuscript.
2022-12-08 | E-MTAB-11549 | biostudies-arrayexpress
Project description:Spatial multi-omics technologies enable simultaneous measurements of multiple omics modalities. Integration across spatial omics modalities followed by multi-omic spatial domain and cell-type annotation are two fundamental tasks for downstream analysis. We present Domain Invariant Representation through Adversarial Calibration (DIRAC), a geometric deep learning model that unifies both tasks by treating horizontal integration (different cells/spots, same omic modality) and vertical integration (same cells/spots, different omics modalities) under a generalized domain adaptation framework. DIRAC uses an adversarial domain discriminator to integrate multiple spatial omics modalities into a unified domain-invariant embedding space and to automate cell-type annotation by transferring labels from reference multi-omic data. DIRAC delineated more biologically meaningful spatial domains and improved clustering and cell-type annotation performance across omics modalities (histone marks, chromatin accessibility, RNA, and protein) and technology platforms (sequencing and imaging-based). We used DIRAC to build cellularly resolved spatial multi-omics atlases of mouse spleen and thymus, revealing the spatial migratory patterns of T cells in the thymus and the spatial organization of finely-resolved immune cell types in the spleen. DIRAC is substantially faster than existing multi-omic integration methods and scales to millions of cells.
2026-07-01 | GSE284032 | GEO
Project description:Integration of multiple data modalities in a spatially informed manner remains an unmet need for exploiting spatial multi-omics data. Here, we introduce SpatialGlue, a novel graph neural network with dual-attention mechanism, to decipher spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrate that SpatialGlue can more accurately resolve spatial domains at a higher resolution across different tissue types and technology platforms, to enable biological insights into cross-modality spatial correlations.
2024-06-07 | GSE263617 | GEO
Project description:Genome-wide methylation profiles were generated as part of a multi-omics characterization (SNP array, WES, RNA-seq, cDNA microarray) of a panel of gliomasphere cell lines and matched parental tumors. See https://www.ncbi.nlm.nih.gov/pubmed/27571888 about the GlioTeX panel. Methylation profiling data in this record come from tumour samples. SNP array (ArrayExpress E-MTAB-4804), cDNA microarray (ArrayExpress E-MTAB-4803), WES and RNAseq (European Genome-Phenome Archive EGAS00001001871) have been published before. In addition, for the current project we compare 450K methylation data to nanopore sequencing based methylation profiles. These sequencing data will be accessible via European Genome-Phenome Archive (EGAS00001002213). Please also refer to https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5795/files/E-MTAB05795.additional.1.zip for further information on the background of this multi-omics study.
2017-06-24 | E-MTAB-5797 | biostudies-arrayexpress
Project description:In this study we used single cell multi-omics profiling to create an atlas of the human YS to gain insights into its haematopoietic, metabolic and nutritive functions during early embryonic development. This contains embryonic liver CITE-seq (surface protein and cytosolic RNA content) data from three biological replicates. Pooled lanes were demultiplexed using SoupOrCell (for alignment and demultiplexing software and version numbers, please see accompanying manuscript and protocols within this accession). Raw count files provided are directly as output by alignment software, without any quality control applied. Quality control is described in accompanying manuscript methods. Metadata by barcode are provided as supplementary tables in accompanying manuscript.
2022-12-08 | E-MTAB-11618 | biostudies-arrayexpress
Project description:In this study we used single cell multi-omics profiling to create an atlas of the human YS to gain insights into its haematopoietic, metabolic and nutritive functions during early embryonic development. This contains fetal liver CITE-seq (surface protein and cytosolic RNA content) data from six biological replicates. Pooled lanes were demultiplexed using SoupOrCell (for alignment and demultiplexing software and version numbers, please see accompanying manuscript and protocols within this accession). Raw count files provided are directly as output by alignment software, without any quality control applied. Quality control is described in accompanying manuscript methods. Metadata by barcode are provided as supplementary tables in accompanying manuscript.
2022-12-08 | E-MTAB-11613 | biostudies-arrayexpress