Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference.

ABSTRACT: Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, here, we quantify the quality of a given normalization method according to how wel

SUBMITTER: Benedetti E 

PROVIDER: S-EPMC7408386 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature