A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging.
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Date
2020-07-01ICR Author
Author
Tar, PD
Thacker, NA
Deepaisarn, S
O'Connor, JPB
McMahon, AW
Type
Journal Article
Metadata
Show full item recordAbstract
MOTIVATION: 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. RESULTS: Simulations and MALDI spectra of a stroke-damaged rat brain show MS signals from pathological tissue can be quantified. MRI diffusion data of control and radiotherapy-treated tumours further show high sensitivity hypothesis testing for treatment effects. Successful χ2 and degrees-of-freedom are computed, allowing null-hypothesis thresholding at high levels of confidence. AVAILABILITY AND IMPLEMENTATION: Open-source image analysis software available from TINA Vision, www.tina-vision.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Research team
Quantitative Biomedical Imaging
Language
eng
Date accepted
2020-04-22
License start date
2020-07
Citation
Bioinformatics (Oxford, England), 2020, 36 (13), pp. 4080 - 4087
Publisher
OXFORD UNIV PRESS