ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells

Abstract

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.

Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials

“…This spillover, combined with biological considerations, led to post hoc combination of several deconvolution estimates, resulting in the 18 cell types presented in this analysis. More details about signature matrix construction, including a usable version of MGSM27, are available in the Automated Deconvolution Augmentation of Profiles for Tissue Specific cells [19] package available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ADAPTS/index.html).…”

CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer

“…10 a) with a 62.9% survival rate compared to a 53.9% survival rate in the high vs. low group, respectively, whereas the TCGA 7 year survival rates were 96.4% and 71.9%, respectively. Moreover, since the start of this study, additional deconvolution methods have been developed 81 , 82 , some of which have identified possible “spillover” between different cell types, such that there may be limitations in CIBERSORT’s ability to differentiate between similar cell types such as CD8 T cells and gamma delta T cells 83 . While most gamma delta T cells do not express CD8 or CD4, up to 30% can express CD8 84 .…”

Genetic variation of putative myokine signaling is dominated by biological sex and sex hormones

“…To determine the potential impact of muscle composition on other tissues, we next surveyed muscle cellular proportions in the context of genetics and sex. Single-cell sequencing of human skeletal muscle ( Rubenstein et al, 2020 ) was integrated using cellular deconvolution ( Danziger et al, 2019 ) to roughly estimate cellular composition in the population ( Figure 3A ). Here, a proportion in admixture approach ( Langfelder and Horvath, 2008 ) outperformed other methods ( Figure 3—figure supplement 1 ) to capture a majority of established cell populations across individuals ( Supplementary file 2 ).…”