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. 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.肿瘤的免疫细胞浸润（immune cell infiltration）与肿瘤微环境（tumor microenvironment）是决定患者预后的重要组成部分。例如，通过解卷积（deconvolution）分析患者基因表达数据以推断免疫细胞与基质细胞（stromal cell）的存在丰度，或可帮助识别高风险患者，亦可为治疗方案的选择提供参考依据。 一类极具应用价值的解卷积技术家族，依托源自经纯化单细胞类型基因表达数据得到的基因特征矩阵（signature matrix），可精准识别每一种细胞类型。近年来该技术家族涌现出诸多新方法，其中不少会针对特定场景（如针对某类特定肿瘤的研究）构建专属的基因特征矩阵。 R包ADAPTS为研究者充分利用这一不断扩充的知识库提供了标准化框架，用于开展细胞类型解卷积分析。ADAPTS内置模块化工具，支持针对全新组织类型自定义基因特征矩阵：既可以添加自定义细胞类型，也可从零开始（de novo）构建全新的特征矩阵，包括基于单细胞RNA测序（single cell RNAseq）数据进行构建。 该包提供了统一接口，可对接多款主流的、依托特征矩阵估算异质性样本中细胞类型占比的解卷积算法。 ADAPTS还实现了一项创新方法：先将难以通过解卷积区分的细胞类型聚为聚类簇，再通过分层解卷积对这些聚类簇进行二次拆分。我们通过盲法预测分析证实，ADAPTS所实现的技术方法，可有效提升对单细胞RNA测序数据集中存在的细胞类型的重构精度。 ADAPTS目前已可在R语言环境中使用，可从CRAN与GitHub平台获取。