Cargando…

ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells

Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment....

Descripción completa

Detalles Bibliográficos
Autores principales: Danziger, Samuel A., Gibbs, David L., Shmulevich, Ilya, McConnell, Mark, Trotter, Matthew W. B., Schmitz, Frank, Reiss, David J., Ratushny, Alexander V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6863530/
https://www.ncbi.nlm.nih.gov/pubmed/31743345
http://dx.doi.org/10.1371/journal.pone.0224693
_version_ 1783471723410096128
author Danziger, Samuel A.
Gibbs, David L.
Shmulevich, Ilya
McConnell, Mark
Trotter, Matthew W. B.
Schmitz, Frank
Reiss, David J.
Ratushny, Alexander V.
author_facet Danziger, Samuel A.
Gibbs, David L.
Shmulevich, Ilya
McConnell, Mark
Trotter, Matthew W. B.
Schmitz, Frank
Reiss, David J.
Ratushny, Alexander V.
author_sort Danziger, Samuel A.
collection PubMed
description Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.
format Online
Article
Text
id pubmed-6863530
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-68635302019-12-07 ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells Danziger, Samuel A. Gibbs, David L. Shmulevich, Ilya McConnell, Mark Trotter, Matthew W. B. Schmitz, Frank Reiss, David J. Ratushny, Alexander V. PLoS One Research Article Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub. Public Library of Science 2019-11-19 /pmc/articles/PMC6863530/ /pubmed/31743345 http://dx.doi.org/10.1371/journal.pone.0224693 Text en © 2019 Danziger et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Danziger, Samuel A.
Gibbs, David L.
Shmulevich, Ilya
McConnell, Mark
Trotter, Matthew W. B.
Schmitz, Frank
Reiss, David J.
Ratushny, Alexander V.
ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells
title ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells
title_full ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells
title_fullStr ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells
title_full_unstemmed ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells
title_short ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells
title_sort adapts: automated deconvolution augmentation of profiles for tissue specific cells
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6863530/
https://www.ncbi.nlm.nih.gov/pubmed/31743345
http://dx.doi.org/10.1371/journal.pone.0224693
work_keys_str_mv AT danzigersamuela adaptsautomateddeconvolutionaugmentationofprofilesfortissuespecificcells
AT gibbsdavidl adaptsautomateddeconvolutionaugmentationofprofilesfortissuespecificcells
AT shmulevichilya adaptsautomateddeconvolutionaugmentationofprofilesfortissuespecificcells
AT mcconnellmark adaptsautomateddeconvolutionaugmentationofprofilesfortissuespecificcells
AT trottermatthewwb adaptsautomateddeconvolutionaugmentationofprofilesfortissuespecificcells
AT schmitzfrank adaptsautomateddeconvolutionaugmentationofprofilesfortissuespecificcells
AT reissdavidj adaptsautomateddeconvolutionaugmentationofprofilesfortissuespecificcells
AT ratushnyalexanderv adaptsautomateddeconvolutionaugmentationofprofilesfortissuespecificcells