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

被引：0

作者：

Alanna C. Cote

Hannah E. Young

Laura M. Huckins

机构：

[1] Icahn School of Medicine at Mount Sinai,Pamela Sklar Division of Psychiatric Genomics

[2] Icahn School of Medicine at Mount Sinai,Department of Psychiatry

[3] Icahn School of Medicine at Mount Sinai,Department of Genetics and Genomics

[4] Icahn School of Medicine at Mount Sinai,Icahn Institute for Genomics and Multiscale Biology

[5] Icahn School of Medicine at Mount Sinai,Seaver Autism Center for Research and Treatment

[6] James J. Peters Department of Veterans Affairs Medical Center,Mental Illness Research, Education and Clinical Centers

来源：

Genome Biology | / 23卷

关键词：

Co-expression; Confound; Covariate; Batch effects; RNA-seq; Normalization; Module discovery; Complex traits;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

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