Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies

TL;DR

This paper reviews and compares data transformation, normalization, and batch effect correction methods in mass spectrometry-based omics data analysis, providing a systematic overview and critical assessment of key approaches.

Contribution

It offers a comprehensive review of existing methods for data transformation, normalization, and batch effect correction in mass spectrometry omics studies, highlighting their differences and applications.

Findings

Provides a systematic overview of key approaches

Critically reviews common procedures

Highlights applicability across spectrometric methods

Abstract

Data transformation, normalization and handling of batch effect are a key part of data analysis for almost all spectrometry-based omics data. This paper reviews and contrasts these three distinct aspects. We present a systematic overview of the key approaches and critically review some common procedures. Much of this paper is inspired by mass spectrometry based experimentation, but most of our discussion carries over to omics data using distinct spectrometric approaches generally.