NormExpression: An R Package to Normalize Gene Expression Data Using Evaluated Methods.

Zhenfeng Wu,Shan Gao,Jishou Ruan,Lin Liu,Gustavo Glusman,Hua Wang,Max Robinson,Xiufeng Jin,Weixiang Liu,Haishuo Ji

doi:10.3389/fgene.2019.00400

Abstract

Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the existing normalization methods, different metrics or different datasets by the same metric yield inconsistent results, particularly for the single-cell RNA sequencing (scRNA-seq) data. The worst situations could be that one method evaluated as the best by one metric is evaluated as the poorest by another metric, or one method evaluated as the best using one dataset is evaluated as the poorest using another dataset. Here raises an open question: principles need to be established to guide the evaluation of normalization methods. In this study, we propose a principle that one normalization method evaluated as the best by one metric should also be evaluated as the best by another metric (the consistency of metrics) and one method evaluated as the best using scRNA-seq data should also be evaluated as the best using bulk RNA-seq data or microarray data (the consistency of datasets). Then, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods using both scRNA-seq data and bulk RNA-seq data, satisfying the consistency of metrics and the consistency of datasets. Our findings paved the way to guide future studies in the normalization of gene expression data with its evaluation. The raw gene expression data, normalization methods, and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to select the best method for the normalization of their gene expression data based on the evaluation of different methods (particularly some data-driven methods or their own methods) in the principle of the consistency of metrics and the consistency of datasets.

Highlights

Global gene expression analysis provides quantitative information about the population of RNA species in cells and tissues (Lovén et al, 2012)
The definitions of normalization factor, scaling factor and size factor are inconsistent and need to be explained here. Both the normalization factor defined in the package NormExpression and the scaling factor defined in a previous study (Glusman et al, 2013) are the global normalization factors (Figure 1A)
As the library size methods, Total Read Number (TN), Total Read Count (TC), Cellular RNA (CR), or Nuclear RNA (NR) can be used to estimate a library size, which represents the amount of total RNA in a cDNA library from a sample

Summary

INTRODUCTION

Global gene expression analysis provides quantitative information about the population of RNA species in cells and tissues (Lovén et al, 2012). Glusman et al (2013) proposed that a successful normalization method should simultaneously maximize the number of uniform genes and minimize the correlation between the expression profiles of gene pairs Based on this criterion, they presented two novel and mutually independent metrics to evaluate 15 normalization methods and achieved consistent results using bulk RNA-seq data (Glusman et al, 2013). As many new normalization methods are being developed, researchers need a fast and simple way to evaluate different methods, some data-driven methods or their own methods, rather than obtain information from published evaluation results, which could have biases or mistakes, e.g., misunderstanding of RLE, UQ and TMM (see section “Results”) To satisfy this demand, we developed an R package NormExpression including the raw gene expression data, normalization methods and evaluation metrics used in this study. This tool provides a framework for researchers to select the best method for the normalization of their gene expression data based on the evaluation of different methods in the principle proposed in this study

RESULTS

MATERIALS AND METHODS

Normalization Methods