{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:29:08Z","timestamp":1759206548428,"version":"3.37.3"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T00:00:00Z","timestamp":1588118400000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000289","name":"CRUK","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EPSRC Imaging Centre","award":["C8742\/A18097"],"award-info":[{"award-number":["C8742\/A18097"]}]},{"name":"CRUK advanced clinician scientist fellowship","award":["C19221","A22746"],"award-info":[{"award-number":["C19221","A22746"]}]},{"name":"DPST scholarship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>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 \u03c72 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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 \u03c72 and degrees-of-freedom are computed, allowing null-hypothesis thresholding at high levels of confidence.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Open-source image analysis software available from TINA Vision, www.tina-vision.net.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa270","type":"journal-article","created":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T11:12:27Z","timestamp":1587553947000},"page":"4080-4087","source":"Crossref","is-referenced-by-count":3,"title":["A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging"],"prefix":"10.1093","volume":"36","author":[{"given":"P D","family":"Tar","sequence":"first","affiliation":[{"name":"Division of Informatics , Imaging and Data Sciences"},{"name":"Division of Cancer Sciences , The University of Manchester, M13 9PG Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N A","family":"Thacker","sequence":"additional","affiliation":[{"name":"Division of Informatics , Imaging and Data Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S","family":"Deepaisarn","sequence":"additional","affiliation":[{"name":"Division of Informatics , Imaging and Data Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J P B","family":"O\u2019Connor","sequence":"additional","affiliation":[{"name":"Division of Cancer Sciences , The University of Manchester, M13 9PG Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A W","family":"McMahon","sequence":"additional","affiliation":[{"name":"Division of Informatics , Imaging and Data Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,4,23]]},"reference":[{"key":"2023062312040374400_btaa270-B1","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1093\/biomet\/35.3-4.246","article-title":"The transformation of Poisson, binomial and negative-binomial data","volume":"35","author":"Anscombe","year":"1948","journal-title":"Biometrika"},{"key":"2023062312040374400_btaa270-B2","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/0168-9002(90)91334-8","article-title":"Extended maximum likelihood","volume":"297","author":"Barlow","year":"1990","journal-title":"Nucl. 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