MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

doi:10.1186/s12859-021-04288-0

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	MUREN: a robust and multi-reference approach of RNA-seq transcript normalization
	Feng,Yance 1,2; Li,Lei M.1,2,3
	2021-07-28
发表期刊	BMC Bioinformatics
卷号	22 期号:1
摘要	AbstractBackgroundNormalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable.ResultsWe propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren.ConclusionsMUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.
关键词	RNA-seq Normalization Asymmetrically regulated transcription profiles (ART) Skewness Mode Multi-reference
DOI	10.1186/s12859-021-04288-0
语种	英语
WOS记录号	BMC:10.1186/s12859-021-04288-0
出版者	BioMed Central
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58512
专题	中国科学院数学与系统科学研究院
通讯作者	Li,Lei M.
作者单位	1.Chinese Academy of Sciences; National Center of Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science 2.University of Chinese Academy of Sciences 3.Chinese Academy of Sciences; Center for Excellence in Animal Evolution and Genetics
推荐引用方式 GB/T 7714	Feng,Yance,Li,Lei M.. MUREN: a robust and multi-reference approach of RNA-seq transcript normalization[J]. BMC Bioinformatics,2021,22(1).
APA	Feng,Yance,&Li,Lei M..(2021).MUREN: a robust and multi-reference approach of RNA-seq transcript normalization.BMC Bioinformatics,22(1).
MLA	Feng,Yance,et al."MUREN: a robust and multi-reference approach of RNA-seq transcript normalization".BMC Bioinformatics 22.1(2021).