Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

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

PROVIDER: PRJNA910521 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR22587979_1.fastq.gz	Fastqsanger.gz
	SRR22587979_2.fastq.gz	Fastqsanger.gz
	SRR22587980_1.fastq.gz	Fastqsanger.gz
	SRR22587980_2.fastq.gz	Fastqsanger.gz
	SRR22587981_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 24

Similar Datasets

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

Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.

2023-04-28 | GSE220607 | GEO

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

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

2023-04-28 | GSE220605 | GEO

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

2023-04-28 | GSE220651 | GEO

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

2023-04-28 | GSE220606 | GEO

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 [AML single-cell]

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

| PRJNA910592 | ENA

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

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

| PRJNA910519 | ENA

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

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

2023-04-27 | GSE220608 | GEO

ProteoMixture: A Cell Deconvolution Tool for Bulk Tissue Proteomics Data

Project description:Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome (and transcriptome data) generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms ESTIMATE and ConsensusTME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

2024-01-12 | PXD044157 | Pride

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