Enhanced spectrum clustering for molecular networking
Creators: name: Heirman, Janne
name: Bittremieux, Wout
Publisher: name: Zenodo
Year: 2024

description: Introduction

 

Tandem mass spectrometry (MS/MS) generates massive amounts of data, making data interpretation challenging, especially in the context of small molecules. Molecular networking addresses this problem by representing spectra as nodes and connecting them based on their spectral similarity, identifying groups of similar compounds. Clustering is often employed as a preprocessing step to reduce redundancy in datasets, thereby increasing the interpretability of the resulting network. Here we introduce enhanced spectrum clustering approaches to optimize the interpretability and scalability of molecular networking for small molecule discovery. These methods are integrated into falcon, an efficient spectrum clustering tool, which is available for easy community use via GNPS.

 

Methods

 

After omitting low-quality spectra and removing noise peaks, a two-step strategy including clustering using the cosine similarity followed by molecular networking using the modified cosine similarity was performed. A novel consensus algorithm using noise rejection was used to generate representative spectra after the clustering step. Various clustering methods were evaluated based on the cluster completeness, the proportion of clustered spectra, and the proportion of incorrectly clustered spectra. The molecular networks were evaluated based on the number of nodes and the average degree. Our methodology was evaluated on a dataset of 1423 acylcarnitine MS/MS spectra extracted from GNPS using MassQL. Among these, 621 spectra were annotated as 22 unique compounds using spectral library searching on GNPS.

 

Preliminary Data

 

Several clustering algorithms were evaluated, including hierarchical clustering with single linkage, average linkage, and complete linkage, and DBSCAN density-based clustering. A major advantage of these clustering methods is that the number of clusters does not need to be explicitly set, but that clusters are rather determined based on a maximum similarity threshold. The falcon clustering tool was initially introduced using density-based clustering, whereas MS-Cluster uses a greedy clustering approach that approximates hierarchical clustering using single linkage.



 

When evaluating the different clustering methods on the acylcarnitine data, hierarchical clustering with complete linkage achieved the best performance, clustering 93.11% of the spectra in non-singleton clusters with only 0.66% incorrectly clustered, achieving a 0.9474 completeness score. It outperformed DBSCAN, which clustered 83.84% of the spectra with 0.68% incorrectly clustered and a 0.8950 completeness score. At the level of 1% incorrectly clustered spectra, hierarchical clustering with single linkage matched DBSCAN's performance. Hierarchical clustering with average linkage was slightly less effective than complete linkage, clustering 90.79% of the spectra at the expense of 0.66% incorrectly clustered spectra, achieving a completeness score of 0.9415.

 

Molecular networking following hierarchical clustering with complete linkage reduced the nodes in the network to 216 (118 clusters and 98 singletons), with an average degree of 19.26, while DBSCAN yielded 336 nodes (106 clusters and 230 singletons), with an average degree of 31.49. Thus, hierarchical clustering with complete linkage resulted in a strong reduction in terms of the duplicated information in the molecular network, making it significantly easier to interpret. Consequently, the falcon clustering tool was updated to use hierarchical clustering instead. Additionally, efforts are ongoing to integrate this updated functionality into GNPS to power its clustering and molecular networking workflows for easy community use.

 

Novel Aspect

 

Optimized spectrum clustering significantly reduces data redundancy in molecular networks and improves their interpretability, facilitating molecular discovery.