A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data.

ABSTRACT:

Rationale

Non-negative matrix factorization (NMF) has been used extensively for the analysis of mass spectrometry imaging (MSI) data, visualizing simultaneously the spatial and spectral distributions present in a slice of tissue. The statistical framework offers two related NMF methods: probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), which is a generative model. This work offers a mathematical comparison between NMF, PLSA, and LDA, and includes a detailed evaluation of Kullback-Leibler NMF (KL-NMF) for MSI for the first time. We will inspect the results for MSI data analysis as these different mathematical approaches impose different characteristics on the data and the resulting decomposition.

Methods

The four methods (NMF, KL-NMF, PLSA,

SUBMITTER: Nijs M 

PROVIDER: S-EPMC9285509 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature