Normalization and batch correction methods in high-throughput glycomics

Vučković, Frano (2016) Normalization and batch correction methods in high-throughput glycomics. Doctoral thesis, Faculty of Science > Department of Biology.

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Glycomics is a rapidly emerging field in high-throughput biology that aims to systematically study glycan structures of a given protein or organic system. It is characteristic of glycomics methods, as it is of any other high-throughput method, that its accuracy is highly affected by a complicated experimental procedure. Standard study includes 1000 to 3000 samples, the experiment can take several months and during that time many experimental conditions can vary. As differences in experimental procedure represent a huge source of variation the need for normalization and batch correction arises naturally. In this study we have compared four different methods (UPLC-FLR, xCGE-LIF, MALDI-TOF-MS, and LC-ESI-MS) for quantitative analysis of IgG N-glycosylation by analyzing the same 1201 IgG samples using all four methods. The results presented clearly show that all four methods generate glycan data of sufficiently high quality to be used to detect associations with genetic polymorphisms. We have also evaluated several normalization and batch correction methods on several glycomic datasets. The evaluation of methods have clearly shown that all used normalization and batch correction techniques significantly reduced experimental variation in all analyzed datasets, implying that difference in signal intensity between samples as well as variation arising from batch effects represent important sources of experimental variation in glycomics data.

Item Type: Thesis (Doctoral thesis)
Keywords: lycomics, high-throughput methods, N-glycans, experimental variation, normalization, batch correction
Supervisor: Lauc, Gordan
Date: 2016
Subjects: NATURAL SCIENCES > Biology
Divisions: Faculty of Science > Department of Biology
Depositing User: Grozdana Sirotic
Date Deposited: 11 Jan 2017 12:45
Last Modified: 11 Jan 2017 12:45

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