Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions

TL;DR

This paper emphasizes the importance of understanding the assumptions behind RNA-Seq normalization methods, showing how assumption violations can lead to inaccurate gene expression measures and affect downstream analyses.

Contribution

It provides a detailed analysis of normalization methods based on their assumptions, guiding researchers in selecting appropriate methods for their data.

Findings

Assumption validity is crucial for normalization accuracy.

Violations of assumptions lead to poor normalization performance.

Understanding assumptions improves downstream analysis reliability.

Abstract

RNA-Seq is a widely-used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalization can have a significant impact on downstream analysis, such as inflated false positives in differential expression analysis. An under-emphasized feature of normalization is the assumptions upon which the methods rely and how the validity of these assumptions can have a substantial impact on the performance of the methods. In this paper, we explain how assumptions provide the link between raw RNA-Seq read counts and meaningful measures of gene expression. We examine normalization methods from the perspective of their assumptions, as an understanding of methodological assumptions is necessary for choosing methods appropriate for the data at hand. Furthermore, we discuss why normalization methods perform poorly when their assumptions are violated and how this causes problems in subsequent analysis. To analyze a biological experiment, researchers must select a normalization method with assumptions that are met and that produces a meaningful measure of expression for the given experiment.