LipidSig 2.0: Integrating Lipid Characteristic Insights into Advanced Lipidomics Data Analysis
Profiling analysis
Lipidomics technology provides a fast and high-throughput screening to identify thousands of lipid species in cells,
tissues or other biological samples and has been broadly used in several areas of studies.
In this page, we present an overview that gathers comprehensive analyses that allow researchers to explore the quality and the clustering of samples,
correlation between lipids and samples, and the lipid abundance and composition.
Processed abundance data: User-uploaded abundance data after data processing.
Lipid characteristics: Lipid characteristics converted according to the uploaded lipids in the abundance data. Detailed information about the converted characteristics can be found in the FAQ.
Lipid id: Links to the LION ID, LIPID MAPS ID, and other resource IDs for the uploaded lipids.
Data quality: Box and density plots of the abundance data before and after data processing.
In this page, three types of distribution plot provide a simple view of sample variability. The first histogram depicts the numbers of lipids expressed in each sample.
The second histogram illustrates the total amount of lipid in each sample. The last density plot visualizes the underlying probability distribution of the lipid abundance in each sample (line).
Through these plots, users can easily compare the amount/abundance difference of lipid between samples (i.e., patients vs. control).
Histogram of expressed lipid numbers
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Histogram of lipid amount
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Density plot of lipid abundance distribution
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Dimensionality reduction
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.
Three dimensionality reduction methods are provided in this page, PCA, t-SNE, UMAP.
NOTE: Scaling and Centering are on by default. Centering is strongly recommended for pre-processing steps.
The PCA plot visually simplifies and discerns patterns within complex lipidomic data, effectively reducing multidimensional variables to principal components.
The distinct separation or overlap of the groups reflects the underlying differences or similarities.
The scree plot is a common method for determining the number of PCs to be retained.
The "elbow" of the graph indicates all components to the left of this point can explain most variability of the samples.
Hover the mouse on the plot to view the corresponding detailed information.
The correlation circle plot illustrates the relationship between top N individual features (lipid species) and principal components (PCs).
It displays how all the variables are interrelated, with those positively correlated positioned in the same quadrant and negatively correlated ones located diametrically across the origin of the plot.
The feature contribution histogram offers an in-depth view of how individual features (lipid species) contribute to a user-selected principal component, such as PC1, PC2, or a combination thereof (PC1+PC2).
It allows users to identify which features influence the chosen principal component more.
Adjust the slider above each plot to choose the desired number of top features to display.
Hover the mouse on the plot to view the corresponding detailed information.
The t-SNE plot visually simplifies and discerns patterns within complex lipidomic data, effectively reducing multidimensional variables to principal components.
The distinct separation or overlap of the groups reflects the underlying differences or similarities.
Hover the mouse on the plot to view the corresponding detailed information.
The UMAP plot visually simplifies and discerns patterns within complex lipidomic data, effectively reducing multidimensional variables to principal components.
The distinct separation or overlap of the groups reflects the underlying differences or similarities.
Hover the mouse on the plot to view the corresponding detailed information.
Correlation heatmaps illustrate the correlation between lipid samples or characteristics and depict the patterns in each group. The correlation can be calculated by Pearson or Spearman. The correlation coefficient is clustered depending on the user-defined method and distance. Furthermore, users have to select a lipid characteristic to display the heatmap. Two heatmaps will be shown by lipid samples or by characteristics.
In this page, users can discover lipid abundance over specific lipid characteristics by scrolling dropdown menu.
Lipids will be firstly classified by the selected characteristics from ‘Lipid characteristics’ table uploaded by users. Next,
the lipid abundance will be shown in bar plot, which depicts the lipid abundance level of each sample within each group (e.g., PE, PC) of selected characteristics (e.g., class).
Additionally, a stacked horizontal bar chart reveals the percentage of characteristics in each sample.
For instance, if users select class as lipid characteristics from the dropdown menu, the stacked bar chart will tell users the percentage of TAG, ST, SM etc. of each sample,
the variability of percentage between samples can also be obtained from this plot.
Bar plot classified by selected characteristic
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Stacked horizontal bar chart of lipid class composition