Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra

Deepaisarn, Somrudee; Tar, Paul; Thacker, Neil A.; Seepujak, A.; McMahon, Adam

doi:10.1093/bioinformatics/btx630

Cited by 9 publications

(7 citation statements)

References 26 publications

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“… Left: model selection curve showing goodness-of-fit (

) as a function of model-order N for rat brain image. Right: Bland–Altman analysis of MALDI MS, as corroborated in earlier work ( Deepaisarn et al , 2018 ) …”

Section: Resultssupporting

confidence: 77%

“…Simulated images (128×128 pixels) were generated by linearly weighting example spectra from previously published work ( Deepaisarn et al , 2018 , white and grey matter from lamb brain), and also simple ramps and top-hat distributions ( Fig. 2 ).…”

Section: Methodsmentioning

confidence: 99%

“…If a finite amount of each tissues’ chemicals are always present, it is more difficult to determine the location of zero loadings needed to identify each tissue uniquely. To mitigate this problem, PMFs are post-processed using an algorithm, we call MAX SEP ( Deepaisarn et al , 2018 ). MAX SEP attempts to subtract some quantity, α , of each component from every other, as far as possible, without generating any negative probabilities.…”

Section: Methodsmentioning

confidence: 99%

“…These characteristics have led to an industry standard approach for such MS modelling called probabilistic latent semantic analysis (pLSA), as found in Bruker’s SCiLs lab software. Our similar modelling method, linear Poisson modelling (LPM), has previously been used to make measurements of tissue mixtures from MALDI MS ( Deepaisarn et al , 2018 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Tar

Thacker²,

Deepaisarn³

et al. 2020

Bioinformatics

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Abstract 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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“… Left: model selection curve showing goodness-of-fit (

) as a function of model-order N for rat brain image. Right: Bland–Altman analysis of MALDI MS, as corroborated in earlier work ( Deepaisarn et al , 2018 ) …”

Section: Resultssupporting

confidence: 77%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Tar

Thacker²,

Deepaisarn³

et al. 2020

Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Given the properties of our target data (histograms of Poisson samples), we sought to use linear Poisson modeling (hereafter, LPM) ( Deepaisarn et al , 2017 ; Tar and Thacker, 2014 ; Tar et al , 2015 ) to quantify biological variation and to model uncertainties associated with data samples acquired in clinically relevant imaging methods. LPMs can be considered as an extension to Gaussian mixture modeling, Dempster (1977) , where the Gaussian sub-distributions are replaced with arbitrary non-parametric probability functions.…”

Section: Introductionmentioning

confidence: 99%

A new method for the high-precision assessment of tumor changes in response to treatment

Tar¹,

Thacker²,

Babur³

et al. 2018

Bioinformatics

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MotivationImaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both development and also in response to treatment. The large variations observed in control group, tumors can obscure detection of significant therapeutic effects due to the ambiguity in attributing causes of change. This can hinder development of effective therapies due to limitations in experimental design rather than due to therapeutic failure. An improved method to model biological variation and heterogeneity in imaging signals is described. Specifically, linear Poisson modeling (LPM) evaluates changes in apparent diffusion co-efficient between baseline and 72 h after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes is compared to those attainable using a conventional t-test analysis on basic apparent diffusion co-efficient distribution parameters.ResultsWhen LPMs were applied to treated tumors, the LPMs detected highly significant changes. The analyses were significant for all tumors, equating to a gain in power of 4-fold (i.e. equivalent to having a sample size 16 times larger), compared with the conventional approach. In contrast, highly significant changes are only detected at a cohort level using t-tests, restricting their potential use within personalized medicine and increasing the number of animals required during testing. Furthermore, LPM enabled the relative volumes of responding and non-responding tissue to be estimated for each xenograft model. Leave-one-out analysis of the treated xenografts provided quality control and identified potential outliers, raising confidence in LPM data at clinically relevant sample sizes.Availability and implementationTINA Vision open source software is available from www.tina-vision.net.Supplementary information Supplementary data are available at Bioinformatics online.

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

Nijs

Smets

Waelkens

et al. 2021

Rapid Comm Mass Spectrometry

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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, and LDA) are compared on seven different samples: three originated from mice pancreas and four from humanlymph-node tissues, all obtained using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).Results: Where matrix factorization methods are often used for the analysis of MSI data, we find that each method has different implications on the exactness and interpretability of the results. We have discovered promising results using KL-NMF, which has only rarely been used for MSI so far, improving both NMF and PLSA, and have shown that the hitherto stated equivalent KL-NMF and PLSA algorithms do differ in the case of MSI data analysis. LDA, assumed to be the better method in the field of text mining, is shown to be outperformed by PLSA in the setting of MALDI-MSI. Additionally, the molecular results of the human-lymph-node data have been thoroughly analyzed for better assessment of the methods under investigation. Conclusions:We present an in-depth comparison of multiple NMF-related factorization methods for MSI. We aim to provide fellow researchers in the field of MSI a clear understanding of the mathematical implications using each of these analytical techniques, which might affect the exactness and interpretation of the results.

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Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra

Cited by 9 publications

References 26 publications

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

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

A new method for the high-precision assessment of tumor changes in response to treatment

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

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