Comparing normalization methods and the impact of noise.

ABSTRACT:

Introduction

Failure to properly account for normal systematic variations in OMICS datasets may result in misleading biological conclusions. Accordingly, normalization is a necessary step in the proper preprocessing of OMICS datasets. In this regards, an optimal normalization method will effectively reduce unwanted biases and increase the accuracy of downstream quantitative analyses. But, it is currently unclear which normalization method is best since each algorithm addresses systematic noise in different ways.

Objective

Determine an optimal choice of a normalization method for the preprocessing of metabolomics datasets.

Methods

Nine MVAPACK normalization algorithms were compared with simulated and experimental NMR spectra modified with added Gaussian noise and rando

SUBMITTER: Vu T 

PROVIDER: S-EPMC6638559 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature