Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Community assessment of methods to deconvolve cellular composition from bulk gene expression

ABSTRACT: Community assessment of methods to deconvolve cellular composition from bulk gene expression

PROVIDER: PRJNA819397 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR18463600_1.fastq.gz	Fastqsanger.gz
	SRR18463600_2.fastq.gz	Fastqsanger.gz
	SRR18463601_1.fastq.gz	Fastqsanger.gz
	SRR18463601_2.fastq.gz	Fastqsanger.gz
	SRR18463602_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 236

Similar Datasets

Community assessment of methods to deconvolve cellular composition from bulk gene expression

Project description:We generated in vitro and in silico admixtures of tumor, immune, and stromal cells and sequenced them along with the purified populations from which they were derived. We used the admixtures as ground truth in a community-wide DREAM Challenge aimed at providing an objective, unbiased assessment of published deconvolution methods and at promoting development of new approaches. We benchmarked seven published methods and found that they could predict most cell types well, though dissecting functional states of T cells remains difficult: few methods predict memory CD4+ T cells or the memory versus naïve compartments of CD8T+ cells and, those that do, show large performance gaps relative to other cell types. We spurred development of 21 novel methods, some of which were able to reliably infer these cellular functional states.

2022-06-06 | GSE199324 | GEO

Community assessment of methods to deconvolve cellular composition from bulk gene expression.

Project description:We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

| S-EPMC11350143 | biostudies-literature

Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes [AML Bulk]

Project description:Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes [AML Bulk]

| PRJNA910521 | ENA

Comprehensive Assessment of Isoform Detection Methods for Third-Generation Sequencing Data

Project description:Comprehensive Assessment of Isoform Detection Methods for Third-Generation Sequencing Data

| PRJNA947488 | ENA

Quantitative assessment of single-cell RNA sequencing methods

Project description:We generated single-cell transcriptomes from a large number of single cells using several commercially available platforms, in both microliter and nanoliter volumes, and compared performance between them. We benchmarked each method to conventional RNA-seq of the same sample using bulk total RNA, as well as to multiplexed qPCR, which is the current gold standard for quantitative single-cell gene expression analysis. In doing so, we were able to systematically evaluate the sensitivity, precision, and accuracy of various approaches to single-cell RNA-seq. Our results show that it is possible to use single-cell RNA-seq to perform quantitative transcriptome measurements of individual cells, that it is possible to obtain quantitative and accurate gene expression measurements with a relatively small number of sequencing reads, and that when such measurements are performed on large numbers of cells, one can recapitulate the bulk transcriptome complexity, and the distributions of gene expression levels found by single-cell qPCR. 109 single-cell human transcriptomes were analyzed in total; 96 using nanoliter volume sample processing on a microfluidic platform, Nextera library prep (biological replicates); 3 using the SMARTer cDNA synthesis kit, Nextera library prep (biological replicates); 3 using the Transplex cDNA synthesis kit, Nextera library prep (biological replicates); 7 using the Ovation Nugen cDNA synthesis kit (biological replicates) where 3 used Nextera library prep and 4 used NEBNext library prep. In addition, 4 bulk RNA samples were sequenced: bulk RNA generated using ~1 million pooled cells was used to make bulk libraries, 2 of which were made using SMARTer cDNA synthesis kit (technical replicates) and 2 made using Superscript RT kit with no amplification (technical replicates). All 4 bulk samples were made into libraries using Nextera.

2013-10-20 | E-GEOD-51254 | biostudies-arrayexpress

Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes [cell mixtures bulk]

Project description:Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes [cell mixtures bulk]

| PRJNA910517 | ENA

Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes

Project description:Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes

| PRJNA910513 | ENA

Assessment of different enrichment methods revealed the optimal approach to identify bovine circRNAs

Project description:Assessment of different enrichment methods revealed the optimal approach to identify bovine circRNAs

| PRJNA941278 | ENA

One-carbon metabolites control cellular identity [Bulk RNA-seq_myoblast]

Project description:One-carbon metabolites control cellular identity [Bulk RNA-seq_myoblast]