DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data
T Gong, JD Szustakowski - Bioinformatics, 2013 - academic.oup.com
For heterogeneous tissues, measurements of gene expression through mRNA-Seq data are
confounded by relative proportions of cell types involved. In this note, we introduce an …
confounded by relative proportions of cell types involved. In this note, we introduce an …
A benchmark for RNA-seq deconvolution analysis under dynamic testing environments
Background Deconvolution analyses have been widely used to track compositional
alterations of cell types in gene expression data. Although a large number of novel methods …
alterations of cell types in gene expression data. Although a large number of novel methods …
CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data
Quantifying cell-type proportions and their corresponding gene expression profiles in tissue
samples would enhance understanding of the contributions of individual cell types to the …
samples would enhance understanding of the contributions of individual cell types to the …
Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets
Deconvolution of cell mixtures in “bulk” transcriptomic samples from homogenate human
tissue is important for understanding disease pathologies. However, several experimental …
tissue is important for understanding disease pathologies. However, several experimental …
CellMix: a comprehensive toolbox for gene expression deconvolution
R Gaujoux, C Seoighe - Bioinformatics, 2013 - academic.oup.com
Gene expression data are typically generated from heterogeneous biological samples that
are composed of multiple cell or tissue types, in varying proportions, each contributing to …
are composed of multiple cell or tissue types, in varying proportions, each contributing to …
AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution
H Aliee, FJ Theis - Cell Systems, 2021 - cell.com
Knowing cell-type proportions in a tissue is very important to identify which cells or cell types
are targeted by a disease or perturbation. Hence, several deconvolution methods have been …
are targeted by a disease or perturbation. Hence, several deconvolution methods have been …
SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references
Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of
transcriptomic profiles with single-cell resolution and circumvent averaging artifacts …
transcriptomic profiles with single-cell resolution and circumvent averaging artifacts …
MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data
Cell-type composition of intact bulk tissues can vary across samples. Deciphering cell-type
composition and its changes during disease progression is an important step toward …
composition and its changes during disease progression is an important step toward …
Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes
Background 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 …
single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can …
Benchmarking and integration of methods for deconvoluting spatial transcriptomic data
L Yan, X Sun - Bioinformatics, 2023 - academic.oup.com
Motivation The rapid development of spatial transcriptomics (ST) approaches has provided
new insights into understanding tissue architecture and function. However, the gene …
new insights into understanding tissue architecture and function. However, the gene …