Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systematic comparative analysis of single cell RNA-sequencing methods

ABSTRACT: Systematic comparative analysis of single cell RNA-sequencing methods

PROVIDER: PRJNA545730 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR9167440.fastq.gz	Fastqsanger.gz
	SRR9167450.fastq.gz	Fastqsanger.gz
	SRR9167460.fastq.gz	Fastqsanger.gz
	SRR9167470.fastq.gz	Fastqsanger.gz
	SRR9167480.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 3994

Similar Datasets

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

Project description:Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods and provides a framework for benchmarking further improvements of scRNA-seq protocols.

2016-09-09 | GSE75790 | GEO

Systematic comparative analysis of single-nucleotide variants detection methods from single-cell RNA sequencing data

Project description:Systematic interrogation of single nucleotide variations (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single cell level. While SNV detection from abundant single cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Here, we performed a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, using both simulation and scRNA-seq datasets, and identified multiple elements influencing their performance. Our study provided the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

| EGAS00001003883 | EGA

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

Systematic comparative analysis of strand-specific, cost-effective, RNA-seq library preparation methods for low input samples

Project description:Systematic comparative analysis of strand-specific, cost-effective, RNA-seq library preparation methods for low input samples

| PRJNA704106 | ENA

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

Project description:Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

| PRJNA687535 | ENA

Comparative analysis of human brain tissue preprocessing methods for single-nucleus chromatin accessibility profiling

Project description:Comparative analysis of human brain tissue preprocessing methods for single-nucleus chromatin accessibility profiling

| PRJNA698982 | ENA

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data.

Project description:BACKGROUND:Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. RESULTS:Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. CONCLUSIONS:We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

| S-EPMC6862814 | biostudies-literature

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

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

| PRJNA687523 | ENA

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

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

| PRJNA687528 | ENA