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比较基因共表达网络构建中混杂因素调整方法。

Comparison of confound adjustment methods in the construction of gene co-expression networks.

机构信息

Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

出版信息

Genome Biol. 2022 Feb 3;23(1):44. doi: 10.1186/s13059-022-02606-0.

Abstract

Adjustment for confounding sources of expression variation is an important preprocessing step in large gene expression studies, but the effect of confound adjustment on co-expression network analysis has not been well-characterized. Here, we demonstrate that the choice of confound adjustment method can have a considerable effect on the architecture of the resulting co-expression network. We compare standard and alternative confound adjustment methods and provide recommendations for their use in the construction of gene co-expression networks from bulk tissue RNA-seq datasets.

摘要

调整表达变异的混杂来源是大型基因表达研究中的一个重要预处理步骤,但混杂调整对共表达网络分析的影响尚未得到很好的描述。在这里,我们证明了混杂调整方法的选择会对所得到的共表达网络的结构产生相当大的影响。我们比较了标准和替代的混杂调整方法,并为在从批量组织 RNA-seq 数据集中构建基因共表达网络时使用这些方法提供了建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5865/8812044/a31040d33ecd/13059_2022_2606_Fig1_HTML.jpg

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