Inequalities between hypergeometric tails

Phipps, Mary C.

doi:10.1155/s1173912603000154

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…The pathway analysis and inference were performed using Fisher’s exact tests, which evaluate whether two categorical variables are statistically independent 49 , 50 , with low P -values indicating a lack of independence. For the purposes of this analysis, we tested whether significant partial correlation coefficients accumulated at given pathway distances.…”

Section: Methodsmentioning

confidence: 99%

Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway

et al. 2017

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Immunoglobulin G (IgG) is a major effector molecule of the human immune response, and aberrations in IgG glycosylation are linked to various diseases. However, the molecular mechanisms underlying protein glycosylation are still poorly understood. We present a data-driven approach to infer reactions in the IgG glycosylation pathway using large-scale mass-spectrometry measurements. Gaussian graphical models are used to construct association networks from four cohorts. We find that glycan pairs with high partial correlations represent enzymatic reactions in the known glycosylation pathway, and then predict new biochemical reactions using a rule-based approach. Validation is performed using data from a GWAS and results from three in vitro experiments. We show that one predicted reaction is enzymatically feasible and that one rejected reaction does not occur in vitro. Moreover, in contrast to previous knowledge, enzymes involved in our predictions colocalize in the Golgi of two cell lines, further confirming the in silico predictions.

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Section: Methodsmentioning

confidence: 99%

Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway

et al. 2017

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“…The overlap between biological reference and correlation network was calculated using Fisher’s exact tests [ 55 , 56 ], which evaluate whether two categorical variables are statistically independent [ 57 ], with low p -values indicating a lack of independence. We classified all glycan pairs in a 2 × 2 contingency table, according to whether they were connected by an edge in both the data-driven GGM and the biochemical pathway (true positives), only in the GGM (false positives), only in the pathway (false negatives), or in neither (true negatives).…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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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 well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms: Liquid Chromatography – ElectroSpray Ionization - Mass Spectrometry (LC-ESI-MS), Ultra High Performance Liquid Chromatography with Fluorescence Detection (UHPLC-FLD), and Matrix Assisted Laser Desorption Ionization – Furier Transform Ion Cyclotron Resonance – Mass Spectrometry (MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the ‘Probabilistic Quotient’ method followed by log-transformation, irrespective of the measurement platform. This recommendation is further supported by an additional analysis, where we ranked normalization methods based on their statistical associations with age, a factor known to associate with glycomics measurements.

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