Cargando…

The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data

BACKGROUND: The cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g., cell flow cytometry) are costly, time consuming and have potential to introduce bias. Co...

Descripción completa

Detalles Bibliográficos
Autores principales:	Nadel, Brian B, Lopez, David, Montoya, Dennis J, Ma, Feiyang, Waddel, Hannah, Khan, Misha M, Mangul, Serghei, Pellegrini, Matteo
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Oxford University Press 2021
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931818/ https://www.ncbi.nlm.nih.gov/pubmed/33590863 http://dx.doi.org/10.1093/gigascience/giab002

Ejemplares similares

Gene expression deconvolution in clinical samples
por: Zhao, Yingdong, et al.
Publicado: (2010)

Gene expression distribution deconvolution in single-cell RNA sequencing
por: Wang, Jingshu, et al.
Publicado: (2018)

Highly accurate quantification of allelic gene expression for population and disease genetics
por: Saukkonen, Anna, et al.
Publicado: (2022)

Accurate viral population assembly from ultra-deep sequencing data
por: Mangul, Serghei, et al.
Publicado: (2014)

Spatial Analysis of Single Fiber Cells of the Developing Ocular Lens Reveals Regulated Heterogeneity of Gene Expression
por: Gangalum, Rajendra K., et al.
Publicado: (2018)

Exploiting Gene-Expression Deconvolution to Probe the Genetics of the Immune System
por: Steuerman, Yael, et al.
Publicado: (2016)

A sequential Monte Carlo approach to gene expression deconvolution
por: Ogundijo, Oyetunji E., et al.
Publicado: (2017)

Comprehensive evaluation of deconvolution methods for human brain gene expression
por: Sutton, Gavin J., et al.
Publicado: (2022)

SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles
por: Lopez, David, et al.
Publicado: (2017)

Disease-specific classification using deconvoluted whole blood gene expression
por: Wang, Li, et al.
Publicado: (2016)

Transcriptome assembly and quantification from Ion Torrent RNA-Seq data
por: Mangul, Serghei, et al.
Publicado: (2014)

Deconvolution of gene expression from cell populations across the C. elegans lineage
por: Burdick, Joshua T, et al.
Publicado: (2013)

Revisit linear regression-based deconvolution methods for tumor gene expression data
por: Li, Bo, et al.
Publicado: (2017)

Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data
por: Torroja, Carlos, et al.
Publicado: (2019)

Telescope: an interactive tool for managing large-scale analysis from mobile devices
por: Brito, Jaqueline J, et al.
Publicado: (2020)

SpatialDWLS: accurate deconvolution of spatial transcriptomic data
por: Dong, Rui, et al.
Publicado: (2021)

Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling
por: Pellegrini, Marco
Publicado: (2021)

Characterization of heterotypic interaction effects in vitro to deconvolute global gene expression profiles in cancer
por: Buess, Martin, et al.
Publicado: (2007)

Gene Expression Deconvolution for Uncovering Molecular Signatures in Response to Therapy in Juvenile Idiopathic Arthritis
por: Cui, Ang, et al.
Publicado: (2016)

Corrigendum: Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data
por: Torroja, Carlos, et al.
Publicado: (2020)

SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references
por: Dong, Meichen, et al.
Publicado: (2020)

Computational deconvolution to estimate cell type-specific gene expression from bulk data
por: Jaakkola, Maria K, et al.
Publicado: (2021)

Likelihood-based deconvolution of bulk gene expression data using single-cell references
por: Erdmann-Pham, Dan D., et al.
Publicado: (2021)

Bulk Gene Expression Deconvolution Reveals Infiltration of M2 Macrophages in Retinal Neovascularization
por: Wang, Jiang-Hui, et al.
Publicado: (2021)

Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data
por: Andrade Barbosa, Bárbara, et al.
Publicado: (2021)

CDSeqR: fast complete deconvolution for gene expression data from bulk tissues
por: Kang, Kai, et al.
Publicado: (2021)

Uncertainty quantification of reference-based cellular deconvolution algorithms
por: Vellame, Dorothea Seiler, et al.
Publicado: (2022)

DEBay: A computational tool for deconvolution of quantitative PCR data for estimation of cell type-specific gene expression in a mixed population
por: Devaraj, Vimalathithan, et al.
Publicado: (2020)

Gene Meter: Accurate abundance calculations of gene expression
por: Pozhitkov, Alexander E., et al.
Publicado: (2017)

Normalization with genes encoding ribosomal proteins but not GAPDH provides an accurate quantification of gene expressions in neuronal differentiation of PC12 cells
por: Zhou, Lihan, et al.
Publicado: (2010)

Placental cell type deconvolution reveals that cell proportions drive preeclampsia gene expression differences
por: Campbell, Kyle A., et al.
Publicado: (2023)

Resolving deconvolution ambiguity in gene alternative splicing
por: She, Yiyuan, et al.
Publicado: (2009)

Accurate and highly interpretable prediction of gene expression from histone modifications
por: Frasca, Fabrizio, et al.
Publicado: (2022)

CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data
por: Kang, Kai, et al.
Publicado: (2019)

Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells
por: Chiu, Yen-Jung, et al.
Publicado: (2019)

BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures
por: Ghaffari, Saba, et al.
Publicado: (2023)

Systematic benchmarking of omics computational tools
por: Mangul, Serghei, et al.
Publicado: (2019)

PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution
por: Hu, Yu, et al.
Publicado: (2014)

Computational expression deconvolution in a complex mammalian organ
por: Wang, Min, et al.
Publicado: (2006)

Statistical expression deconvolution from mixed tissue samples
por: Clarke, Jennifer, et al.
Publicado: (2010)

Cannot write session to /tmp/vufind_sessions/sess_r461ag01t5kghj74mtp333b3ng