Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Robust enumeration of cell subsets from tissue expression profiles (HGU133A)

ABSTRACT: We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA specimens for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).

ORGANISM(S): Homo sapiens

PROVIDER: GSE65134 | GEO | 2015/03/29

SECONDARY ACCESSION(S): PRJNA273242

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Robust enumeration of cell subsets from tissue expression profiles (HGU133Plus2)

2015-03-29 | GSE65135 | GEO

Robust enumeration of cell subsets from tissue expression profiles (BeadChip)

2015-03-29 | GSE65133 | GEO

Robust enumeration of cell subsets from tissue expression profiles (HGU133A)

Project description:We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA specimens for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu). To evaluate the performance of CIBERSORT for enumerating Tregs, RNA was extracted from PBMC samples from 6 healthy normal controls and 1 follicular lymphoma (FL) patient. All PBMC samples were also interrogated by flow cytometry for FOXP3+ Tregs.

2015-03-29 | E-GEOD-65134 | biostudies-arrayexpress

Robust enumeration of cell subsets from tissue expression profiles (BeadChip)

Project description:We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA specimens for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu). To evaluate the performance of CIBERSORT against flow cytometry, gene expression profiling was performed on a set of 20 PBMC samples comprised of adults of varying ages receiving influenza immunization (NCT01827462). These samples were analyzed by flow cytometry to enumerate several leukocyte subsets. Normalized gene expression data and accompanying flow cytometry data are available at the CIBERSORT website (http://cibersort.stanford.edu/download.php).

2015-03-29 | E-GEOD-65133 | biostudies-arrayexpress

Robust enumeration of cell subsets from tissue expression profiles (HGU133Plus2)

Project description:We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA specimens for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu). To evaluate the performance of CIBERSORT, RNA was extracted from the following primary human samples: (i) 14 disaggregated lymph node biopsies from patients with follicular lymphoma (FL), (ii) pre- and/or post-immunotherapy PBMC samples from 3 patients with extranodal marginal zone lymphoma (EMZL) or diffuse large B cell lymphoma (DLBCL), and (iii) B or T cells purified from the tonsils of 5 healthy normal controls.

2015-03-29 | E-GEOD-65135 | biostudies-arrayexpress

Detection of allele-specific expression in spatial transcriptomics with spASE

Project description:Spatial transcriptomics technologies permit the study of the spatial distribution of RNA at near-single-cell resolution genome-wide. However, the feasibility of studying spatial allele-specific expression (ASE) from these data remains uncharacterized. Here, we introduce spASE, a computational framework for detecting and estimating spatial ASE. To tackle the challenges presented by cell type mixtures and a low signal to noise ratio, we implement a hierarchical model involving additive mixtures of spatial smoothing splines. We apply our method to allele-resolved Visium and Slide-seq from the mouse cerebellum and hippocampus and report new insight into the landscape of spatial and cell type-specific ASE therein.

2024-05-31 | GSE268519 | GEO

Robust enumeration of cell subsets from tissue expression profiles

Project description:This SuperSeries is composed of the SubSeries listed below.

2015-03-29 | GSE65136 | GEO

Robust enumeration of cell subsets from tissue expression profiles

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2015-03-29 | E-GEOD-65136 | biostudies-arrayexpress

Robust enumeration of cell subsets from tissue expression profiles.

| S-EPMC4739640 | biostudies-literature

A mixture of endocrine disrupting chemicals associated with lower birth weight in children induces adipogenesis and DNA methylation changes in human mesenchymal stem cells

Project description:Endocrine disrupting chemicals (EDCs) are man-made compounds that alter functions of the endocrine system. Environmental mixtures of EDCs might have adverse effects on human health, even though their individual concentrations are below regulatory levels of concerns. However, studies identifying and experimentally testing adverse effects of real-life mixtures are scarce. In this study, we aimed at evaluating an epidemiologically identified EDC mixture in an experi-mental setting to delineate its cellular and epigenetic effects. The mixture was established using data from Swedish Environmental Longitudinal Mother and child Asthma and allergy (SELMA) study where it was associated with lower birth weight, an early marker for prenatal metabolic programming. This mixture was then tested for its ability to change metabolic programming of human mesenchymal stem cells. In these cells, we assessed if the mixture induced adipogenesis and genome-wide DNA methylation changes. The mixture increased lipid droplet accumulation already at concentrations corresponding to levels measured in the pregnant women of the SELMA study. Furthermore, we identified differentially methylated regions in genes important for adipogenesis and thermogenesis. This study shows that a mixture reflecting human real-life exposure can induce molecular and cellular changes during development that could underlie adverse outcomes.

2022-03-02 | GSE196046 | 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