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In lipid species analysis section, differentially expressed analysis is performed to find significant lipid species. In short, samples will be divided into two/multiple groups (independent) based on the Group Information of input. Two statistical methods are provided for different data types: the t-test and the Wilcoxon test (Wilcoxon rank-sum test)for two-group data, along with one-way ANOVA and the Kruskal-Wallis test for multi-group data. Additionally, the p-value will be adjusted using the Benjamini-Hochberg procedure. The condition and cut-offs for significant lipid species are also users selected.
.zip
file containing two folders:.rds
file (in SummarizedExperiment format) ready for upload to the enrichment module..rds
file, so you can proceed directly without manual upload..zip
file that also includes two folders:.rds
file, formatted as a SummarizedExperiment, which serves as the shared input for all three network analysis modules..rds
file, ready for immediate use.Dimensionality reduction is common when dealing with large numbers of observations and/or large numbers of variables in lipids analysis. It transforms data from a high-dimensional space into a low-dimensional space so that to retain vital properties of the original data and close to its intrinsic dimension.
Lipid species that derived from two/multiple groups will be clustered and visualised on heatmap using hierarchical clustering. Through heatmap, users may discover the difference between the two/multiple groups by observing the distribution of lipid species. This analysis provides an overview of lipid species differences between the control group and the experimental group.
Characteristics association
In this part, we categorize significant lipid species based on different lipid characteristics and visualise the difference between control and experimental groups by applying log2 Fold Change.
Differential expression
Dimensionality reduction in this section assists users to tackle with large numbers of variables in lipids analysis. The high-dimensional space is transformed into a low-dimensional space. Hence, the crucial properties of the lipid data are revealed and still close to its intrinsic characteristics. Here, we provide four types of dimensionality reduction approaches, PCA, PLS-DA, t-SNE, UMAP, and four clustering methods, K-means, partitioning around medoids (PAM), Hierarchical clustering, and DBSCAN.
New lipid abundance table summed up from species will be clustered and shown on the heatmap using hierarchical clustering. Through heatmap, users may discover the difference between the two/multiple groups by observing the distribution of lipid characteristic abundance. This analysis provides an overview of lipid characteristic abundance differences between two/multiple groups. Four distance measures can be chosen, Person, Spearman, or Kendall, and eight clustering methods can be selected by pulling down the menu.
The heatmaps provide the correlation between the double bond and chain length of lipid species. The color in the heatmaps is gradient according to different data types (log2FC for two group data and p-value for multi-group data).