RT Journal Article
SR Electronic
T1 ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 633958
DO 10.1101/633958
A1 Samuel A Danziger
A1 David L Gibbs
A1 Ilya Shmulevich
A1 Mark McConnell
A1 Matthew WB Trotter
A1 Frank Schmitz
A1 David J Reiss
A1 Alexander V Ratushny
YR 2019
UL http://biorxiv.org/content/early/2019/07/11/633958.abstract
AB Immune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type 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 hard 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.