Cell-type enrichment analysis of bulk transcriptomes using xcell

Dvir Aran

doi:https://doi.org/10.1007/978-1-0716-0327-7_19

Cell-type enrichment analysis of bulk transcriptomes using xcell

Research output: Contribution to journal › Review article › peer-review

Abstract

Tissues are a complex milieu of cell types of different lineages and subtypes, each with its own unique transcriptomic profile. Bulk transcriptome profiling is therefore the sum of the cell-type-specific gene expression weighted by cell-type proportion in the given sample. Deconvolution of gene expression profiles allows to reconstruct the cellular composition of tissues. xCell is a robust computational method that converts gene expression profiles to enrichment scores of 64 immune and stroma cell types across samples. Here, we described the method, discuss correct usage, and demonstrate an analysis of a cohort of peripheral blood mononuclear cells (PBMC).

Original language	English
Pages (from-to)	263-276
Number of pages	14
Journal	Methods in molecular biology
DOIs	https://doi.org/10.1007/978-1-0716-0327-7_19
State	Published - 2020
Externally published	Yes

Keywords

Cell types
Deconvolution
Gene expression
Gene signatures
Immune cells

All Science Journal Classification (ASJC) codes

Molecular Biology
Genetics

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https://doi.org/10.1007/978-1-0716-0327-7_19

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title = "Cell-type enrichment analysis of bulk transcriptomes using xcell",

abstract = "Tissues are a complex milieu of cell types of different lineages and subtypes, each with its own unique transcriptomic profile. Bulk transcriptome profiling is therefore the sum of the cell-type-specific gene expression weighted by cell-type proportion in the given sample. Deconvolution of gene expression profiles allows to reconstruct the cellular composition of tissues. xCell is a robust computational method that converts gene expression profiles to enrichment scores of 64 immune and stroma cell types across samples. Here, we described the method, discuss correct usage, and demonstrate an analysis of a cohort of peripheral blood mononuclear cells (PBMC).",

keywords = "Cell types, Deconvolution, Gene expression, Gene signatures, Immune cells",

author = "Dvir Aran",

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doi = "https://doi.org/10.1007/978-1-0716-0327-7_19",

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N1 - Publisher Copyright: © Springer Science+Business Media, LLC, part of Springer Nature 2020.

PY - 2020

Y1 - 2020

N2 - Tissues are a complex milieu of cell types of different lineages and subtypes, each with its own unique transcriptomic profile. Bulk transcriptome profiling is therefore the sum of the cell-type-specific gene expression weighted by cell-type proportion in the given sample. Deconvolution of gene expression profiles allows to reconstruct the cellular composition of tissues. xCell is a robust computational method that converts gene expression profiles to enrichment scores of 64 immune and stroma cell types across samples. Here, we described the method, discuss correct usage, and demonstrate an analysis of a cohort of peripheral blood mononuclear cells (PBMC).

AB - Tissues are a complex milieu of cell types of different lineages and subtypes, each with its own unique transcriptomic profile. Bulk transcriptome profiling is therefore the sum of the cell-type-specific gene expression weighted by cell-type proportion in the given sample. Deconvolution of gene expression profiles allows to reconstruct the cellular composition of tissues. xCell is a robust computational method that converts gene expression profiles to enrichment scores of 64 immune and stroma cell types across samples. Here, we described the method, discuss correct usage, and demonstrate an analysis of a cohort of peripheral blood mononuclear cells (PBMC).

KW - Cell types

KW - Deconvolution

KW - Gene expression

KW - Gene signatures

KW - Immune cells

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DO - https://doi.org/10.1007/978-1-0716-0327-7_19

M3 - مقالة مرجعية

C2 - 32124326

SN - 1064-3745

SP - 263

EP - 276

JO - Methods in molecular biology

JF - Methods in molecular biology

ER -

Cell-type enrichment analysis of bulk transcriptomes using xcell

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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