From raw signals to identified metabolites: recent tools for enhancing LC–MS untargeted metabolomic data processing

2015 ◽  
pp. 36-47
Author(s):  
David Tonoli ◽  
Fabienne Jeanneret ◽  
Julien Boccard ◽  
Serge Rudaz
Keyword(s):  
Data Processing ◽  
Metabolomic Data

Profiling and comparison of toxicant metabolites in hair and urine using a mass spectrometry-based metabolomic data processing method

2019 ◽  
Vol 1052 ◽  
pp. 84-95 ◽  
Author(s):  
Chia-Lung Shih ◽  
Hsin-Yi Wu ◽  
Pao-Mei Liao ◽  
Jen-Yi Hsu ◽  
Chia-Yun Tsao ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Processing ◽  
Processing Method ◽  
Data Processing Method ◽  
Metabolomic Data

Optimization of metabolomic data processing using NOREVA

2021 ◽  
Author(s):  
Jianbo Fu ◽  
Ying Zhang ◽  
Yunxia Wang ◽  
Hongning Zhang ◽  
Jin Liu ◽  
...  
Keyword(s):  
Data Processing ◽  
Metabolomic Data

A protocol for LC-MS metabolomic data processing using chemometric tools

2015 ◽  
Author(s):  
Roma Tauler ◽  
Eva Gorrochategui ◽  
Joaquim Jaumot ◽  
Romà Tauler
Keyword(s):  
Data Processing ◽  
Metabolomic Data ◽  
Chemometric Tools

scholarly journals Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn’s disease with a publicly available untargeted metabolomics dataset

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255240
Author(s):  
Shoaib Bin Masud ◽  
Conor Jenkins ◽  
Erika Hussey ◽  
Seth Elkin-Frankston ◽  
Phillip Mach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Data Analysis ◽  
Data Processing ◽  
Untargeted Metabolomics ◽  
Statistical Techniques ◽  
P Value ◽  
Metabolomic Analysis ◽  
Metabolomic Data ◽  
False Discoveries

Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers’ attention for further analysis.

scholarly journals Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses

2014 ◽  
Vol 86 (14) ◽  
pp. 6931-6939 ◽  
Author(s):  
Harsha Gowda ◽  
Julijana Ivanisevic ◽  
Caroline H. Johnson ◽  
Michael E. Kurczy ◽  
H. Paul Benton ◽  
...  
Keyword(s):  
Data Processing ◽  
Statistical Analyses ◽  
Metabolomic Data
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