Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling [10x]

ABSTRACT: We systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’ v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE163781 | GEO | 2021/01/15

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling [PBMC_10X]

Project description:We systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’ v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.

2021-01-15 | GSE164402 | GEO

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling [icell8]

2021-01-15 | GSE163792 | GEO

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling [ddSEQ]

2021-01-15 | GSE163788 | GEO

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling [bulk]

2021-01-15 | GSE163787 | GEO

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling [DS]

2021-01-15 | GSE163785 | GEO

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

Vaginal secretory proteome dynamically changes across the early and mid-trimesters of pregnancy

Project description:This study investigates the dynamic alterations in high vaginal fluid (HVF) proteome and its correlation with physiological changes during progression of term pregnancy. The HVF samples were collected at three time points as defined as V1 (6-12 weeks), V2 (18-20 weeks) and V3 (26-28 weeks) and SWATH-MS strategy were applied to profile changes in protein expression at early and middle stage of pregnancy. Using in-house generated HVF-specific protein library, 61 proteins (>1.5 fold at V2/V1 or V3/V1, q-value <0.05) changed as a function of gestational age. The stage-specific expression pattern of these proteins was mainly associated with the biology of cervical remolding, fetal development and microbial defense.

2021-01-28 | PXD021811 | Pride

Vaginal secretory proteome dynamically changes across the early and mid-trimesters of pregnancy

2021-01-29 | PXD014846 | Pride

Systematic comparative analysis of single cell RNA-sequencing methods

Project description:A multitude of single-cell RNA sequencing methods have been developed in recent years, with dramatic advances in scale and power, and enabling major discoveries and large scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single cell and/or single nucleus profiling from three types of samples – cell lines, peripheral blood mononuclear cells and brain tissue – generating 36 libraries in six separate experiments in a single center. To analyze these datasets, we developed and applied scumi, a flexible computational pipeline that can be used for any scRNA-seq method. We evaluated the methods for both basic performance and for their ability to recover known biological information in the samples. Our study will help guide experiments with the methods in this study as well as serve as a benchmark for future studies and for computational algorithm development.

2019-06-01 | GSE132044 | GEO

Comparative analysis of single-cell RNA sequencing methods with and without sample multiplexing

Project description:Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10X Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10X data where samples are not multiplexed. Despite a relatively lower library and cell capture efficiencies, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.

2024-03-24 | GSE246624 | 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