A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging | Zendy

P. Tar | Zendy; N. A. Thacker | Zendy; Somrudee Deepaisarn | Zendy; James P.B. O’Connor | Zendy; Adam McMahon | Zendy

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A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging

Author(s) -

P. Tar,

N. A. Thacker,

Somrudee Deepaisarn,

James P.B. O’Connor,

Adam McMahon

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa270

Subject(s) - computer science , probabilistic latent semantic analysis , statistical hypothesis testing , artificial intelligence , probabilistic logic , pattern recognition (psychology) , machine learning , multiple comparisons problem , statistical model , sensitivity (control systems) , data mining , statistics , mathematics , electronic engineering , engineering

Probabilistic latent semantic analysis (pLSA) is commonly applied to describe mass spectra (MS) images. However, the method does not provide certain outputs necessary for the quantitative scientific interpretation of data. In particular, it lacks assessment of statistical uncertainty and the ability to perform hypothesis testing. We show how linear Poisson modelling advances pLSA, giving covariances on model parameters and supporting χ2 testing for the presence/absence of MS signal components. As an example, this is useful for the identification of pathology in MALDI biological samples. We also show potential wider applicability, beyond MS, using magnetic resonance imaging (MRI) data from colorectal xenograft models.

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