LipidSig 2.0: Integrating Lipid Characteristic Insights into Advanced Lipidomics Data Analysis

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  • LipidSigR

Enrichment

Our platform supports two enrichment approaches: Over Representation Analysis (ORA) and Lipid Set Enrichment Analysis (LSEA).
ORA identifies lipid categories (e.g., classes or subclasses) that are overrepresented among significantly changed lipid species. It is suitable when you have a defined list of differentially expressed lipids.
LSEA evaluates whether predefined lipid sets are enriched at the top or bottom of a ranked lipid list, without applying a strict significance cutoff. This method is useful for detecting subtle but coordinated changes in lipid characteristics.

Demo dataset source (two-group): Adipose tissue ATGL modifies the cardiac lipidome in pressure-overload-induced left Ventricular failure (PLoS Genet. 2018)
Demo dataset source (multi-group): Lipidomic and biophysical homeostasis of mammalian membranes counteracts dietary lipid perturbations to maintain cellular fitness (Nat Commun 11, 1339. 2020)

Data Source

Download example

Upload your data table in .csv/.tsv/.xlsx
Download DE .zip

How to prepare your dataset?
How to use this function?

: Successfully uploaded.   : Error happaned. Please check your dataset.   : Warning message.


Uploaded data


  • Processed abundance data
  • Group information
  • Lipid characteristics
  • Lipid id
  • Differential expression analysis result
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Result

  • Over Representation Analysis
  • Lipid set enrichment analysis

Over Representation Analysis

The Over Representation Analysis (ORA) identifies whether significantly altered lipid species are enriched in specific lipid-related categories, such as classes, chain lengths, or physicochemical properties. This helps identify biological patterns within the differentially expressed lipids.

The analysis output includes:

    Summary Bar Plot: This plot displays the top 10 significant up- and down-regulated terms for comparisons involving two groups, and the top 20 terms for comparisons involving multiple groups. Terms are ranked by statistical significance (−log10 p-value), and colored by lipid aspect (e.g., class, function, cellular component).

    Characteristic-Specific Bar Plot: Based on the selected lipid characteristic from the dropdown menu (e.g., class, saturation, chain length), this plot shows the enrichment results for all terms under that characteristic. Red bars represent up-regulated terms, blue bars indicate down-regulated terms, and grey bars denote non-significant terms.

    Enrichment Table: A detailed, downloadable table listing all terms, p-values, adjusted p-values, and the associated lipid characteristics.

  • Configure your conditions by the control panel below, then click the 'Submit' button.
  • For input data preparation, please refer to FAQ. You can download inputs directly from the Differential expression analysis to ensure format accuracy.



The bar plot shows the top 10 significant up-regulated and down-regulated terms for datasets involving two groups and the top 20 for multiple groups.

  • Hover the mouse on a specific bar to view the corresponding detailed information.
  • For figure manipulation, please refer to FAQ.

Download
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The bar plot classifies significant lipid species into 'up-regulated' or 'down-regulated' categories based on their log2 fold change, according to a selected characteristic. Red bars indicate up-regulated, blue bars represent down-regulated, and grey bars signify non-significant.

  • Select a characteristic of interest from the drop-down list above the plot.
  • Hover the mouse on a specific bar to view the corresponding detailed information.
  • For figure manipulation, please refer to FAQ.

Download
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Lipid set enrichment analysis

Lipid Set Enrichment Analysis (LSEA) evaluates whether predefined sets of lipids show statistically significant and coordinated differences between biological states (e.g., phenotypes). Unlike ORA, LSEA uses the full ranked list of lipid species and calculates enrichment based on Normalized Enrichment Score (NES).

The analysis output includes:

    Summary Bar Plot: This plot displays the top 10 significant up- and down-regulated terms for comparisons involving two groups, and the top 20 terms for comparisons involving multiple groups. Terms are colored based on lipid aspect and ranked by NES.

    Characteristic-Specific Bar Plot: Based on the selected lipid characteristic from the dropdown menu (e.g., class, saturation, chain length), this plot shows the enrichment results for all terms under that characteristic. Red bars represent up-regulated terms, blue bars indicate down-regulated terms, and grey bars denote non-significant terms.

    Enrichment Plot: Shows the enrichment score curve of a selected term, highlighting how the lipid set is distributed across the ranked list.

    Enrichment Table: A detailed, downloadable table listing lipid set name, p-values, adjusted p-values, and enrichment scores.

  • Configure your conditions by the control panel below, then click the 'Submit' button.
  • For input data preparation, please refer to FAQ. You can download inputs directly from the Differential expression analysis to ensure format accuracy.




Download
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The bar plot shows the top 10 significant up-regulated and down-regulated terms for datasets involving two groups and the top 20 for multiple groups.

  • Click on a specific bar to display the corresponding enrichment plot.
  • Hover the mouse on a specific bar to view the corresponding detailed information.
  • For figure manipulation, please refer to FAQ.


Loading...


Download
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The bar plot classifies significant lipid species into 'up-regulated' or 'down-regulated' categories based on their log2 fold change, according to a selected characteristic. Red bars indicate up-regulated, blue bars represent down-regulated, and grey bars signify non-significant.

  • Select a characteristic of interest from the drop-down list above the plot.
  • Click on a specific bar to display the corresponding enrichment plot.
  • Hover the mouse on a specific bar to view the corresponding detailed information.
  • For figure manipulation, please refer to FAQ.


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