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Ometa Labs
Home
Ometa Flow
Molecular Networking
MASST
Team
Analysis
Resources
Application Notes
Contact
Home
Ometa Flow
Molecular Networking
MASST
Team
Analysis
Resources
Application Notes
Contact
Classical vs. Feature-Based Molecular Networking
Metabolomics 101 Sydney Thomas 2024-02-09 Metabolomics 101 Sydney Thomas 2024-02-09

Classical vs. Feature-Based Molecular Networking

What is the difference between Classical Molecular Networking and Feature-Based Molecular Networking?

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Statistics: the basics
Why is this so hard? Sydney Thomas 2024-02-04 Why is this so hard? Sydney Thomas 2024-02-04

Statistics: the basics

Our statistics primer for metabolomics researchers

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Statistics: p values
Why is this so hard? Sydney Thomas 2024-02-04 Why is this so hard? Sydney Thomas 2024-02-04

Statistics: p values

What is a p value and why is p<0.05 considered statistically significant?

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Statistics: normalization
Why is this so hard? Sydney Thomas 2024-02-04 Why is this so hard? Sydney Thomas 2024-02-04

Statistics: normalization

What is non-biological variance and how can data normalization help correct it?

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Statistics: principal components analysis
Why is this so hard? Sydney Thomas 2024-02-03 Why is this so hard? Sydney Thomas 2024-02-03

Statistics: principal components analysis

What is principal components analysis (PCA) and how can I use it in untargeted metabolomics?

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Statistics: machine learning
Why is this so hard? Sydney Thomas 2024-02-03 Why is this so hard? Sydney Thomas 2024-02-03

Statistics: machine learning

What is machine learning (ML) and how can I use it in metabolomics data analysis?

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