Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method

TL;DR

This paper introduces DANA, a new method for evaluating and selecting the most appropriate normalization technique for microRNA sequencing data, ensuring removal of artifacts while preserving biological signals.

Contribution

The paper presents DANA, a novel assessment approach that uses biological and data-driven metrics to guide normalization method selection in microRNA sequencing analysis.

Findings

Normalization methods vary widely in performance across datasets.

DANA effectively evaluates normalization methods for microRNA data.

Routine use of DANA improves data preprocessing accuracy.

Abstract

Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for "normalizing" sequencing data to remove unwanted between-sample variations due to experimental handling, there is no consensus on which normalization is the most suitable for a given data set. To address this problem, we developed "DANA" - an approach for assessing the performance of normalization methods for microRNA sequencing data based on biology-motivated and data-driven metrics. Our approach takes advantage of well-known biological features of microRNAs for their expression pattern and chromosomal clustering to simultaneously assess (1) how effectively normalization removes handling artifacts, and (2) how aptly normalization preserves biological signals. With DANA, we confirm that the performance of eight commonly used normalization methods vary widely across different data sets and provide guidance for selecting a suitable method for the data at hand. Hence, it should be adopted as a routine preprocessing step (preceding normalization) for microRNA sequencing data analysis. DANA is implemented in R and publicly available at https://github.com/LXQin/DANA.