ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells


Autoři: Samuel A. Danziger aff001;  David L. Gibbs aff002;  Ilya Shmulevich aff002;  Mark McConnell aff001;  Matthew W. B. Trotter aff001;  Frank Schmitz aff001;  David J. Reiss aff001;  Alexander V. Ratushny aff001
Působiště autorů: Celgene Corporation, Seattle, Washington, United States of America aff001;  Institute for Systems Biology, Seattle, Washington, United States of America aff002;  Celgene Institute for Translational Research Europe, Seville, Sevilla, Spain aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224693

Souhrn

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.

Klíčová slova:

Adipocytes – Algorithms – Cell differentiation – Gene expression – Immune cells – Myelomas – Plasma cells


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Článek vyšel v časopise

PLOS One


2019 Číslo 11