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

In this section, we provide a comprehensive correlation analysis to assist researchers to interrogate the clinical features that connect to lipids species and other mechanistically relevant lipid characteristics. Correlation analysis between lipids and clinical features is broadly used in many fields of study, such as Bowler RP et al. discovering that sphingomyelins are strongly associated with emphysema and glycosphingolipids are associated with COPD exacerbations. Hence, continuous clinical data can be uploaded here, and diverse correlation analyses are offered. For instance, the Correlation Coefficient and Linear Regression are supported for continuous clinical data. Moreover, lipids can be classified either by lipid species or by lipid categories when conducting these correlation analyses.

Demo dataset source: Plasma sphingolipids associated with chronic obstructive pulmonary disease phenotypes (Am J Respir Crit Care Med. 2015)

Data Source

Data source

Example dataset (Am J Respir Crit Care Med. 2015)

Upload your data!

Download example

Lipid expression data:

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Condition table:

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Adjusted table (optional):

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Upload your data table in .csv/.tsv/.xlsx

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

Uploaded data

Lipid abundance data
Condition table (clinical factor)
Adjusted table

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

Data processing

Remove features with many missing values

More than % missing values

Fill missing value with:

Multiply by minimum

Tune sigma

nPCs

The number of neighbors

nPcs

Data Normalization

Normalization with:

Data Transformation

Transformation with:

Processed data

Processed abundance data: User-uploaded abundance data after data processing.

Condition table (clinical factor): User-uploaded condition table.

Adjusted table (if provided): User-uploaded adjusted table.

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.

Processed abundance data
Condition table (clinical factor)
Adjusted table
Lipid characteristics
Lipid id
Data quality

Download

Result

Lipid species analysis
Lipid characteristics analysis

Correlation
Linear regression

Correlation

The Correlation Coefficient gives a summary view that tells researchers whether a relationship exists between clinical features and lipid species, how strong that relationship is and whether the relationship is positive or negative. Here we provide three types of correlations, Pearson, Spearman, and Kendall, and adjusted by Benjamini & Hochberg methods. The cut-offs for correlation coefficient and the p-value can be decided by users.

A heatmap will show after users inputting cut-offs and choosing a value for clustering/methods for clustering. Users can use either correlation coefficient between clinical features (e.g. genes) and lipid species or choose their statistic instead.

Correlation method:

Multiple testing correction:

Benjamini & Hochberg

Identify significant lipids:

p-value:

Correlation coefficient cutoff:

Value for clustering:

correlation coefficient

statistics

Distance measure:

Clustering method:

Linear regression

Linear regression is a statistical technique that uses several explanatory variables to predict the outcome of a continuous response variable, allowing researchers to estimate the associations between lipid levels and clinical features. For multiple linear regression analysis, additional variables in ‘adjusted table’ will be added into the algorithm and used to adjust the confounding effect. Once calculation completes, each lipid species will be assigned a beta coefficient and t statistic (p-value), which can be chosen for clustering.

Multiple testing correction:

Benjamini & Hochberg

Identify significant lipids:

p-value:

Value for clustering:

beta coefficient t statistics

Distance measure:

Clustering method:

The heatmap demonstrates a correlation between clinical features and lipid species. Clinical features are displayed along the heatmap rows, while the columns represent various lipid species. The color of each cell corresponds to the coefficient value, ranging from positive (red) to negative (blue) in a gradient.

If the lipid/sample number exceeds 50, their names will not be displayed on the heatmap.

Hover on a specific heatmap cell to view more corresponding information.

Lipid species-clinical features correlation heatmap

Download matrix

The heatmap demonstrates a correlation between clinical features and lipid species. Clinical features are displayed along the heatmap rows, while the columns represent various lipid species. The color of each cell corresponds to the coefficient value, ranging from positive (red) to negative (blue) in a gradient.

If the lipid/sample number exceeds 50, their names will not be displayed on the heatmap.

Hover on a specific heatmap cell to view more corresponding information.

Lipid species-clinical features correlation heatmap

Download matrix

Correlation
Linear regression

Correlation

The Correlation Coefficient gives a summary view that tells researchers whether a relationship exists between clinical features and user-defined lipid characteristics, how strong that relationship is and whether the relationship is positive or negative. Here we provide three types of correlations, Pearson, Spearman, and Kendall, and adjusted by Benjamini & Hochberg methods. The cut-offs for correlation coefficient and the p-value can be decided by users.

A heatmap will show after users inputting cut-offs and choosing a value for clustering/methods for clustering. Users can use either correlation coefficient between clinical features (e.g. genes) and lipid characteristics or choose their statistic instead.

Correlation method:

Multiple testing correction:

Benjamini & Hochberg

Identify significant lipids:

p-value:

Correlation coefficient cutoff:

Value for clustering:

correlation coefficient

statistics

Distance measure:

Clustering method:

Linear regression

Multiple linear regression is a statistical technique that uses several explanatory variables to predict the outcome of a response variable, allowing researchers to estimate the associations between lipid levels and clinical features (i.e., genetic polymorphisms). In this page, the lipids will be classified by the user-selected lipid characteristics (e.g. class), then implementing multiple linear regression analysis. Each variable (the pair of lipid characteristics and clinical features) will be assigned a beta coefficient and t statistic (p-value), which can be chosen for clustering.

Multiple testing correction:

Benjamini & Hochberg

Identify significant lipids:

p-value:

Value for clustering:

beta coefficient t statistics

Distance measure:

Clustering method:

The heatmap demonstrates a correlation between clinical features user-defined lipid characteristics. Clinical features are displayed along the heatmap rows, while the columns represent lipid characteristics. The color of each cell corresponds to the coefficient value, ranging from positive (red) to negative (blue) in a gradient.

If the lipid/sample number exceeds 50, their names will not be displayed on the heatmap.

Hover on a specific heatmap cell to view more corresponding information.

Lipid characteristics-clinical features correlation heatmap

Download matrix

The heatmap demonstrates a correlation between clinical features user-defined lipid characteristics. Clinical features are displayed along the heatmap rows, while the columns represent lipid characteristics. The color of each cell corresponds to the coefficient value, ranging from positive (red) to negative (blue) in a gradient.

If the lipid/sample number exceeds 50, their names will not be displayed on the heatmap.

Hover on a specific heatmap cell to view more corresponding information.

Lipid characteristics-clinical features correlation heatmap

Download matrix