PandaOmics allows combining the differential expression profile, pathway activation and metadata (sample annotations), to identify correlations between them in the Meta-Analysis tool
You can select a gene of interest and plot its differential expression changes against sample annotations (e.g. cancer stages if available for a given dataset), and/or select a specific pathway activation profile calculated by iPANDA method
An example of a gene expression versus predicted pathway activation
An example of a gene expression across samples sub-groups
(in this case cancer is staged)
If a dataset is annotated with the patient survival data, it is possible to build the Survival estimation analysis. You can define sample groups for the survival analysis based on sample annotation, specific gene expression or a pathway activity prediction
The analysis creates Kaplan-Meier survival curves
and Aalen's additive model plots
Another approach to estimate the correlations between gene expression, pathway activation and sample annotation is to run Cox regression Hazard Ratio analysis