Welcome to LipidOne 2.0

DCBB Unipg
Vitality

User-friendly lipidomic data analysis tool
for a deeper interpretation
in a systems biology scenario

01

Here you can explore your lipidomic data, discover biomarkers, predict genes involved in transformations between control and experiment groups, and study lipidomic pathways.

02

Analyses are possible on three levels: Lipid Classes, Lipid Molecular Species, Lipid Building Blocks.

03

LipidOne 2.0 provides you with single and multivariate, supervised and unsupervised statistical analysis tools.

04

Furthermore, you can always select groups and classes and work on subsets of your data.

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For each group studied by LipidOne 2.0, it is possible to obtain a two-layer pie chart. The innermost layer shows the lipid categories, the outermost layer the lipid classes. Each element also shows the value of the percentage amount.
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For Principal Component Analysis, LipidOne 2.0 makes it possible to select any group and any lipid class for PCA. The score graph shows groupings with ellipsoids at the 95% confidence level.
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The PCA loading graph of lipid molecular species and lipid building blocks shows each feature with a colour corresponding to the lipid class to which it belongs.
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For each PCA it is possible to obtain the scree plot for the first five components.
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HeatMap of molecular species or lipid blocks is made by LipidOne 2.0 after selecting any group and any class of interest. It reports a number (decided by the user) of the most significant molecular species (t-test or ANOVA). Several grouping metrics of groups and features can be applied.
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LipidOne 2.0 can create numerous types of bar graphs, each accompanied by Experimental Error and significance asterisks (from t-test or ANOVA). For each of the three levels, lipid groups and classes can be selected for inclusion in the graph. It is possible to compare the abundances of lipid classes, molecular species of a lipid class, unsaturation number, lipid chain lengths, and more...
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LipidOne 2.0 helps discover possible lipid biomarkers. Top 20 Biomarkers of an experimental group compared to a control group is calculated for Lipid Classes, Lipid Molecular Species and Lipid Building Blocks. A table shows each of the top 20 features with the p-value, Area Under Curve (AUC) of an ROC analysis, Cohen's d-factor, and test power calculated for a p-value less than 0.001.
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LipidOne 2.0 offers the possibility to perform network analysis, demonstrating lipid transformations and interactions based on well-known biochemical pathways. This analysis is available at the three different levels: Lipid Classes, Molecular Species and Lipid Building Blocks. It is possible to study variations in Building Blocks within all or a selection of lipid classes. The user only needs to select the groups to be considered experiment and control and adjust the level of significance. Blue and red arcs connect the nodes indicating the direction and the status of the interaction (Blue: increased, Red: decreased).
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Although the user of LipidOne 2.0 can set a threshold significance level for lipid interactions, it is possible to estimate the probability of the degree of involvement of a particular Gene. A bar graph will indicate the most involved genes in order of probability.
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A detailed table with individual interactions, genes encoding for the enzymes involved and Z score are displayed.
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LidipOne 2.0 also enables the creation of Volcano Plots for lipid classes and lipid molecular species. The user only needs to select the two groups to be compared and set a significance level. The plot highlights overexpressed features in blue, underexpressed features in red, and insignificant features in grey.
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LipidOne 2.0 makes it possible to select experimental groups and lipids belonging to certain classes to perform a PLS-DA analysis. As with PCA, the loading plots are coloured by lipid class. The cross validation of the PLS model shows the Accuracy, R2Y and Q2Y values for the first five components in a bar graph.
Reference (please cite as:)
  • Alabed, H. B. R., Mancini, D. F., Buratta, S., Calzoni, E., Giacomo, D. D., Emiliani, C., Martino, S., Urbanelli, L., & Pellegrino, R. M. (2024). LipidOne 2.0: A web tool for discovering biological meanings hidden in lipidomic data. Current Protocols, 4, e70009. doi: 10.1002/cpz1.70009
  • Roberto Maria Pellegrino, Matteo Giulietti, Husam B.R. Alabed, Sandra Buratta, Lorena Urbanelli, Francesco Piva, Carla Emiliani, LipidOne: user-friendly lipidomic data analysis tool for a deeper interpretation in a systems biology scenario, Bioinformatics, Volume 38, Issue 6, March 2022, Pages 1767–1769, https://doi.org/10.1093/bioinformatics/btab867
NEW Unipg
Stay tuned, important new features coming soon!
  • new Systems Biology functions: from lipid phenotype to proteomics;
  • new functions for mono-, multivariate and clustering analysis;
  • new key organisation.

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