How to Make Sense of Lipidomics Data | with Mathias Gerl | The Lipidomics Webinar

Описание: The amount and complexity of lipidomics data sets can be intimidating initially - but this does not need to be the case.

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OUTLINE

Lipidomics provides unprecedented phenotypic details by accumulating large amounts of data. While these are potentially of great value, the amount and complexity of the data can be intimidating initially.

This webinar is designed to show the basics of lipidomics analysis. It will cover the characteristics of the datasets, e.g. their lipid substructure and multicollinearity, and how to deal with them. We will go through data preparation, how to calculate new features and how to use principal component analysis as a first impression. Then we will focus on univariate and correlation analysis combined with enrichment analysis.

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CHAPTERS

00:00 - From lipid data to lipidomics data analysis
06:10 - How to handle missing values in lipidomics data sets
08:55 - Checking and understanding total lipid amounts
11:25 - Principal component analysis for lipidomics data overview
16:26 - Choosing a statistical test for lipidomics data analysis
18:35 - Volcano plots to compare lipid species
19:29 - Correction for multiple testing with Benjamini-Hochberg
20:32 - Feature analysis: aggregations of lipid species & fatty acids
24:45 - Selecting relevant correlation types
26:11 - Scatter plots and forest plots in lipidomics data analysis
29:09 - What is enrichment analysis?
30:12 - Feature and pathway enrichment in lipid data analysis
34:08 - Why to apply statistics in lipidomics data analysis

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