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表观基因组关联研究中的细胞类型反卷积:综述与建议

Cell-type deconvolution in epigenome-wide association studies: a review and recommendations.

作者信息

Teschendorff Andrew E, Zheng Shijie C

机构信息

CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

Department of Women's Cancer, University College London, 74 Huntley Street, London WC1E 6AU, UK.

出版信息

Epigenomics. 2017 May;9(5):757-768. doi: 10.2217/epi-2016-0153. Epub 2017 Mar 14.

Abstract

A major challenge faced by epigenome-wide association studies (EWAS) is cell-type heterogeneity. As many EWAS have already demonstrated, adjusting for changes in cell-type composition can be critical when analyzing and interpreting findings from such studies. Because of their importance, a great number of different statistical algorithms, which adjust for cell-type composition, have been proposed. Some of the methods are 'reference based' in that they require a priori defined reference DNA methylation profiles of cell types that are present in the tissue of interest, while other algorithms are 'reference free.' At present, however, it is unclear how best to adjust for cell-type heterogeneity, as this may also largely depend on the type of tissue and phenotype being considered. Here, we provide a critical review of the major existing algorithms for correcting cell-type composition in the context of Illumina Infinium Methylation Beadarrays, with the aim of providing useful recommendations to the EWAS community.

摘要

全表观基因组关联研究(EWAS)面临的一个主要挑战是细胞类型的异质性。正如许多EWAS已经表明的那样,在分析和解释此类研究的结果时,针对细胞类型组成的变化进行调整可能至关重要。由于其重要性,已经提出了大量针对细胞类型组成进行调整的不同统计算法。其中一些方法是“基于参考”的,因为它们需要预先定义感兴趣组织中存在的细胞类型的参考DNA甲基化谱,而其他算法是“无参考”的。然而,目前尚不清楚如何最好地针对细胞类型异质性进行调整,因为这在很大程度上可能还取决于所考虑的组织类型和表型。在这里,我们对Illumina Infinium甲基化微珠阵列背景下校正细胞类型组成的主要现有算法进行了批判性综述,旨在为EWAS社区提供有用的建议。

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