Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind.1]

ABSTRACT: Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind.1]

PROVIDER: PRJNA503437 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR8146670.fastq.gz	Fastqsanger.gz
	SRR8146671.fastq.gz	Fastqsanger.gz
	SRR8146672.fastq.gz	Fastqsanger.gz
	SRR8146665.fastq.gz	Fastqsanger.gz
	SRR8146666.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 8

Similar Datasets

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind. 2]

Project description:Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind. 2]

| PRJNA547564 | ENA

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind.1]

Project description:During host-pathogen encounters, the complex interactions between different immune cell-types can determine the outcome of infection. Advances in single cell RNA-seq (scRNA-seq) allow to probe this complexity of immunity, and afforded the basis for deconvolution algorithms that infer cell-type compositions from bulk RNA-seq measurements. However, immune activation, an important aspect of immune surveillance, is not represented in current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we developed a novel deconvolution algorithm to infer dynamic immune states from bulk measurements. We applied our dynamic deconvolution algorithm both to cohorts of healthy individuals challenged ex vivo with Salmonella and to cohorts of tuberculosis patients during different stages of disease. We revealed cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and can be applied to comprehensively study human infection outcome.

2019-06-07 | GSE122083 | GEO

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind. 2]

2019-06-07 | GSE132300 | GEO

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [WT/TLR10 bulk RNA-seq]

2019-06-07 | GSE122058 | GEO

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [WB/PBMCs bulk RNA-seq]

2019-06-07 | GSE128627 | GEO

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [sorted population Bulk RNA-seq]

2019-06-07 | GSE128626 | GEO

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells

Project description:Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells

| PRJNA503412 | ENA

ncRNA expression-based, individualized outcome prediction for CRC patients

Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of ncRNAs in 165 CRC cases.

2016-12-28 | GSE92928 | GEO

Gene expression-based, individualized outcome prediction for autism

Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of 91 up-regulated and 67 down-regulated miRNAs in 5 autism spectrum disorder (ASD) cases and 5 controls.

2015-04-17 | GSE67979 | GEO

Comparison of IND induced vs. mock treated seedlings: Transcriptome profiling upon induction of the transcription factor IND

Project description:rs10-03_ind - comparison of ind induced vs mock treated seedlings - Identification of miRNAs regulated by IND - Transgenic seeds containing an IND transgene under the control of a dexamethasone inducible promoter were germinated and grown in liquid Murashige/Skoog medium under a 16h light/8h darkness light regime. After seven days of growth, seedlings were either mock-treated or treated with 10 micromolar dexamethasone for 24 hours.

2011-05-12 | GSE28898 | 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