Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data.
Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data.
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DOI：
10.1101/gr.267047.120
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发表时间：
2021-01
期刊：
影响因子：
7
通讯作者：
Tan K
中科院分区：
文献类型：
--
作者：
Uzun Y;Wu H;Tan K
单细胞DNA甲基化数据变得越来越丰富，并且已经发现了许多表达与启动子甲基化之间呈正相关的基因，挑战了基于批量数据的常见教条。然而，用于分析单细胞甲基化组数据的计算工具远远落后。许多任务，包括细胞类型调用和与转录组数据的整合，需要构建一个强大的基因活性矩阵作为先决条件，但具有挑战性的任务。多组学数据的出现使得能够测量相同单细胞的DNA甲基化和基因表达。虽然这些数据相当稀疏，但它们足以训练监督模型，捕获DNA甲基化和基因表达之间的复杂关系，并在单细胞水平上预测基因活性。在这里，我们提出了甲基化关联预测连锁表达（MAPLE），一个计算框架，学习DNA甲基化和表达之间的关联使用基因和细胞依赖的统计特征。使用不同实验方案生成的多个数据集，我们表明，使用预测的基因活性值显着提高了几个分析任务，包括聚类，细胞类型识别，并与转录组数据的整合。MAPLE的应用揭示了甲基化与基因表达之间关系的一些有趣的生物学见解，包括转录起始位点周围的甲基化信号对预测基因表达的不对称重要性，以及位于CpG岛和岸外的启动子中的甲基化信号的预测能力增加。随着单细胞表观基因组学数据的快速积累，MAPLE提供了一个将这些数据与转录组数据整合的通用框架。
Single-cell DNA methylation data has become increasingly abundant and has uncovered many genes with a positive correlation between expression and promoter methylation, challenging the common dogma based on bulk data. However, computational tools for analyzing single-cell methylome data are lagging far behind. A number of tasks, including cell type calling and integration with transcriptome data, requires the construction of a robust gene activity matrix as the prerequisite but challenging task. The advent of multi-omics data enables measurement of both DNA methylation and gene expression for the same single cells. Although such data is rather sparse, they are sufficient to train supervised models that capture the complex relationship between DNA methylation and gene expression and predict gene activities at single-cell level. Here, we present methylome association by predictive linkage to expression (MAPLE), a computational framework that learns the association between DNA methylation and expression using both gene- and cell-dependent statistical features. Using multiple data sets generated with different experimental protocols, we show that using predicted gene activity values significantly improves several analysis tasks, including clustering, cell type identification, and integration with transcriptome data. Application of MAPLE revealed several interesting biological insights into the relationship between methylation and gene expression, including asymmetric importance of methylation signals around transcription start site for predicting gene expression, and increased predictive power of methylation signals in promoters located outside CpG islands and shores. With the rapid accumulation of single-cell epigenomics data, MAPLE provides a general framework for integrating such data with transcriptome data.
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同时分析单个细胞的转录组和 DNA 甲基化组。
影响因子：
12.3
作者：
Hu Y;Huang K;An Q;Du G;Hu G;Xue J;Zhu X;Wang CY;Xue Z;Fan G
通讯作者：
Fan G
影响因子：
30.8
作者：
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DOI：
10.1073/pnas.1320308111
发表时间：
2014-05-27
影响因子：
11.1
作者：
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通讯作者：
Tan, Kai
影响因子：
48
作者：
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通讯作者：
Reik W
影响因子：
48
作者：
Lee, Dong-Sung;Luo, Chongyuan;Ecker, Joseph R.
通讯作者：
Ecker, Joseph R.