Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
Institute of Biomedical Research, Yunnan University, Kunming, China.
Bioinformatics. 2020 Jun 1;36(11):3431-3438. doi: 10.1093/bioinformatics/btaa184.
In the analysis of high-throughput omics data from tissue samples, estimating and accounting for cell composition have been recognized as important steps. High cost, intensive labor requirements and technical limitations hinder the cell composition quantification using cell-sorting or single-cell technologies. Computational methods for cell composition estimation are available, but they are either limited by the availability of a reference panel or suffer from low accuracy.
We introduce TOols for the Analysis of heterogeneouS Tissues TOAST/-P and TOAST/+P, two partial reference-free algorithms for estimating cell composition of heterogeneous tissues based on their gene expression profiles. TOAST/-P and TOAST/+P incorporate additional biological information, including cell-type-specific markers and prior knowledge of compositions, in the estimation procedure. Extensive simulation studies and real data analyses demonstrate that the proposed methods provide more accurate and robust cell composition estimation than existing methods.
The proposed methods TOAST/-P and TOAST/+P are implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST.
ziyi.li@emory.edu or hao.wu@emory.edu.
Supplementary data are available at Bioinformatics online.
在分析组织样本的高通量组学数据时，估计和考虑细胞组成已被认为是重要步骤。细胞分选或单细胞技术的高成本、高劳动需求和技术限制阻碍了细胞组成的量化。用于细胞组成估计的计算方法是可用的，但它们要么受到参考面板可用性的限制，要么准确性较低。
我们介绍了用于分析异质组织的工具（TOols for the Analysis of heterogeneouS Tissues TOAST/-P 和 TOAST/+P），这是两种基于基因表达谱估计异质组织细胞组成的部分无参考算法。TOAST/-P 和 TOAST/+P 在估计过程中纳入了额外的生物学信息，包括细胞类型特异性标志物和组成的先验知识。广泛的模拟研究和真实数据分析表明，与现有方法相比，所提出的方法提供了更准确和稳健的细胞组成估计。
所提出的方法 TOAST/-P 和 TOAST/+P 作为 R/Bioconductor 包 TOAST 的一部分实现，网址为 https://bioconductor.org/packages/TOAST。
ziyi.li@emory.edu 或 hao.wu@emory.edu。
补充数据可在 Bioinformatics 在线获取。
