Hi all,
I am trying to perform a functional analysis on a set of data comparing 15 control vs 10 LBW samples. I have tried both Mummichog and GSEA, but only GSEA produces any results of significance; Mummichog only produces results where p=1.0 (there will be a popup saying “Possible causes of error (last one being the most relevant): No significant features were found based on the current cutoff! Please adjust the p-value threshold.”
Is there a reason why there is such a huge difference between the two algorithms, and how do I fix my data so that Mummichog produces results that are not all p=1.0.
Test.csv (201.7 KB)
Thank you in advance!
------Functional analysis parameter------
Upload - Functional Analysis
A Peak Intensity Table
Ion Mode: Negative Mode
Mass Tolerance: 5.0
Retention time: Yes-minutes
Data Source: Generic
Data Format: Samples in columns
Data File: See file attached
Filtering: None
Sample normalisation: None
Data transformation: Log transformation
Data scaling: None
Mummichog
Algorithms: Mummichog 2.0, version 2.0
Visual analytics: Scatter plot
Pathway library: Homo sapiens (human) [MFN]
Only use pathways / metabolite sets containing at least 3 entries
GSEA
Algorithms: GSEA 2.0, version 2.0
Visual analytics: Scatter plot
Pathway library: Homo sapiens (human) [MFN]
Only use pathways / metabolite sets containing at least 3 entries