Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Separation, characterization, and identification of individuals from multi-person blood mixtures with single cell transcriptome sequencing and a novel bioinformatics pipeline

ABSTRACT: We report a deconvolution and identification strategy of scRNA-seq datasets using mixed PMBCs data. After sequencing the data were processed with the de-goulash pipeline and analyzed with aim to identify and aquire biogeogrpahical ancestry of the involved individuals. The study includes samples of biological mixtures and in silico mixtures.

PROVIDER: EGAS00001006202 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

Proteomics informed deconvolution of 3 cell lines

Project description:The project aimed to investigate the possibility to use proteomics data to deconvolute cell line proportions in mixed samples. Samples containing either HEK 293, Caco-2, or A549 cells and mixtures of the three cell lines was analysed using the total protein approach. This was then used for proteomics informed deconvolution. The results show that proteome deconvolution provides an effective tool for investigating cellular composition in mixed samples. This was later applied also to in silico mixtures of primary human liver cells and liver tissue. However, those data are presented elsewhere.

2023-10-24 | PXD027282 | Pride

A novel computational complete deconvolution method using RNA-seq data

Project description:The cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our method’s performance using several synthetic and experimental mixtures of varied but known cell-type composition and compared its performance to the performance of two state-of-the-art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-type-specific gene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue .

2018-12-12 | GSE123604 | GEO

Transcription profiling of human immune cell line mixtures to test deconvolution

Project description:Microarray deconvolution is a technique for quantifying the relative abundance of constituent cells in a mixture based on that mixture's microarray signature and the signatures of the purified constituents. Its ability to discriminate related human cells is unknown. Here we test the ability of this technique to determine the fractions of transformed cells of immune origin in mixed samples. Experiment Overall Design: Four immune cell lines were grown and run on microarrays either by themselves or in mixtures of various relative proportions. Mixtures of cells were performed in triplicate. Experiment Overall Design: MixA (Jurkat: 2.5, IM-9: 1.25, Raji: 2.5, THP-1: 3.75) Experiment Overall Design: MixB (Jurkat: 0.5, IM-9: 3.17, Raji: 4.75, THP-1: 1.58) Experiment Overall Design: MixC (Jurkat: 0.1, IM-9: 4.95, Raji: 1.65, THP-1: 3.3) Experiment Overall Design: MixD (Jurkat: 0.02, IM-9: 3.33, Raji: 3.33, THP-1: 3.33)

2009-07-19 | E-GEOD-11058 | biostudies-arrayexpress

GeoMx Cancer Transcriptome Atlas (CTA) mRNA assay on an FFPE cell pellet array of mixed HEK293T and CCRF-CEM cell lines.

Project description:Gene expression profiling of mixed cell lines, used to evaluate the performance of deconvolution methods. This study was part of Danaher & Kim et al., "Advances in mixed cell deconvolution enable quantification of cell types in spatially resolved gene expression data"

2021-06-10 | GSE174746 | GEO

Multi-cell type deconvolution using a probabilistic model of single-molecule DNA methylation haplotypes

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

DNA methylation profiling of whole blood and reconstructed mixtures of purified leukocytes isolated from human adult blood

Project description:Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogeneous biospecimens such as whole blood, offer a promising solution. However, their performance depends entirely on the library of DNA methylation markers being used as the basis for deconvolution. The objective of this study was to train and validate an algorithm for the identification of optimal DNA methylation libraries for the deconvolution of adult human whole blood. Purified granulocytes, monocytes, CD4T, CD8T, natural killer cells, and B cells from normal human subjects were purchased from AllCells LLC (Emeryville, CA). DNA extracted from purified leukocyte subtypes were mixed in predetermined proportions to reconstruct two distinct sets of white blood cell (WBC) mixtures, each consisting of six samples. An additional six whole blood (WB) samples from disease-free adult donors with available immune cell profiling data from flow cytometry were purchased from All-Cells LLC and were included in this investigation. All DNA samples were bisulfite modified using the Zymo EZ DNA Methylation kit (Irvine, CA) and profiled for DNA methylation using the Illumina HumanMethylation450 array platform.

2016-02-12 | E-GEOD-77797 | biostudies-arrayexpress

Comprehensive Evaluation of Human Brain Gene Expression Deconvolution Methods

Project description:We benchmarked deconvolution algorithms on human brain gene expression data. The data deposited here include A) mixtures on which algorithms were benchmarked, as well as B) signatures of pure brain cell-type expression

2021-08-28 | GSE175772 | GEO

Identification of novel susceptibility factors in Arabidopsis towards necreotrophic fungal pathogens.

Project description:Transcriptional profiles were carried out for four transcription factors and two receptor-like kinases mutants, corresponding to genes that are co-expressed in silico with the CESA7 gene in Arabidopsis. These mutants show enhanced disease susceptibility to the necrotrophic fungal pathogen Plecthosphaerella cucmerina. Several gene families of small secreted peptides, some of them with known hormone or signalling implications, were commonly up-regulated in all of the 6 genotypes, unveiling a functional implication of these secreted peptides in the defence pathway against fungi. Transcriptomics of 6 mutant plants, including four transcription factors and two receptor-like kinases, were compared to their background Col-0 accession. For this porpoise, plants were grown as described and leaves from three plants of each genotype were mixed and extracted as one single replicate. Three replicates from three different sets of plants were analysed for each genotype.

2015-01-20 | E-GEOD-36308 | biostudies-arrayexpress

Identification and characterization of a novel multi-stress responsive gene in Arabidopsis.

Project description:Abiotic stresses especially salinity, drought and high temperature result in considerable reduction of crop productivity. In this study, we identified AT4G18280 annotated as a glycine-rich cell wall protein-like (hereafter refer to as GRPL1) protein as a potential multistress-responsive gene. Analysis of public transcriptome data and GUS assay of pGRPL1::GUS showed a strong induction of GRPL1 under drought, salinity and heat stresses. Transgenic plants overexpressing GRPL1-3HA showed significantly higher germination, root elongation and survival rate under salt stress. Moreover, the 35S::GRPL1-3HA transgenic lines also showed higher survival rates under drought and heat stresses. GRPL1 showed similar expression patterns with Abscisic acid (ABA)-pathway genes under different growth and stress conditions, suggesting a possibility that GRPL1 might act in the ABA pathway that is further supported by the inability of ABA-deficient mutant (aba2-1) to induce GRPL1 under drought stress. Taken together, our data presents GRPL1 as a potential multi-stress responsive gene working downstream of ABA.

| S-EPMC7746274 | biostudies-literature

quanTIseq: quantifying immune contexture of human tumors

Project description:We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.

2019-02-06 | GSE107572 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data