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

Paul Tar, Neil Thacker, Somrudee Deepaisarn, James O'Connor, Adam McMahon

Research output: Contribution to journalArticlepeer-review


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 (LPM) advances pLSA, giving covariances onmodel 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 mass spectra, using 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 tumors 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.
Original languageEnglish
JournalBioinformatics (Oxford, England)
Publication statusAccepted/In press - 6 Apr 2020


Dive into the research topics of 'A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging'. Together they form a unique fingerprint.

Cite this