Automated quantitative measurements and associated error covariances for planetary image analysis

Tar, Paul; Thacker, Neil A.; Gilmour, J. D.; Jones, Merren

doi:10.1016/j.asr.2015.03.043

Cited by 6 publications

(8 citation statements)

References 31 publications

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“…In recent work we derived an ICA method for data with Poisson sampling characteristics, see Tar and Thacker (2014) : Linear Poisson Modelling (LPM). It has been applied to planetary and medical images (see Tar et al , 2015 , 2017 ). We believe this method [(*) in Table 1 ] provides the best match to the properties of MALDI data and is therefore evaluated here on the task of measuring mixtures of complex lipid specimens.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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MotivationMatrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian.ResultsWe provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth.Availability and implementationSoftware is available via the open source image analysis system TINA Vision, www.tina-vision.net.Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…In a our alternative analysis, the preprocessing techniques described in this paper were applied in order to obtain Poisson independent noise characteristics. Achieving these statistical properties allowed us to apply an LPM 26 (independent component analysis with an assumption of Poisson noise), to model the contamination within 'blank' spectra. The modelled contamination was then subtracted from the spectra, and repeated ratio calculation were again computed using the same 15 repeat data sets.…”

Section: Benchmark Comparisonmentioning

confidence: 99%

“…The aim of our preprocessing is to minimise these effects so that data analysis based upon ideal Poisson statistics can be applied, such as Linear Poisson Modelling. [26][27][28] Preprocessing is an essential step for the analysis of mass spectra, 13 but represents a challenging problem. Poorly performed preprocessing may prevent meaningful analysis of signal.…”

Section: Introductionmentioning

confidence: 99%

The statistical properties of raw and preprocessed ToF mass spectra

Thacker

Tar

Seepujak

et al. 2018

International Journal of Mass Spectrometry

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Single-valued summaries, such as signal-to-noise ratio, are insufficient to fully describe the input and output characteristics of time-of-flight mass spectra preprocessing methods. A detailed understanding of uncertainty, biases and correlations is essential for selecting appropriate methods, and for drawing confident scientific conclusions from preprocessed data. We apply a range of diagnostic tests to mass spectra, allowing statistical and systematic sources of uncertainty to be assessed throughout the typical stages of a preprocessing pipeline. Baseline correction, alignment and peak detection are reconsidered, with an emphasis on producing outputs with statistical properties compatible with an independent Poisson ion counting process. Benchmarking is also performed against a range of alternative preprocessing methods. In contrast to other preprocessing methods, new techniques are presented which provide improved statistical stability. The benefits are demonstrated using simulation and also data from the RELAX (refrigerator-enhanced laser analyser for xenon) mass spectrometer. A twofold improvement in accuracy of Xe peak measurement over the original method for the same dataset is observed.

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“…A LPM (Tar and Thacker 2014;Tar et al 2015) can describe the shape and variability of distributions found within histograms using a linear combination of simpler fixed components 1 . In the case of crater match score histograms, one set of components describes ''true craters'' and one set describes ''false craters''.…”

Section: Training and Fitting Lpmmentioning

confidence: 99%

“…1). This work shows how Linear Poisson Models (LPM), a supervised learning system based upon Likelihood regression (Tar and Thacker 2014;Tar et al 2015), can be applied to crater annotations to reduce uncertainty in contaminated counts. Unlike alternative machine learning methods such as Support Vector Machines (Steinwart and Christmann 2008) and Random Forests (Ho 1995), LPMs incorporate an error theory for predicting the stability of estimated quantities of identified true and false features.…”

Section: Introductionmentioning

confidence: 99%

Estimating False Positive Contamination in Crater Annotations from Citizen Science Data

et al. 2016

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Web-based citizen science often involves the classification of image features by large numbers of minimally trained volunteers, such as the identification of lunar impact craters under the Moon Zoo project. Whilst such approaches facilitate the analysis of large image data sets, the inexperience of users and ambiguity in image content can lead to contamination from false positive identifications. We give an approach, using Linear Poisson Models and image template matching, that can quantify levels of false positive contamination in citizen science Moon Zoo crater annotations. Linear Poisson Models are a form of machine learning which supports predictive error modelling and goodness-of-fits, unlike most alternative machine learning methods. The proposed supervised learning system can reduce the variability in crater counts whilst providing predictive error assessments of estimated quantities of remaining true verses false annotations. In an area of research influenced by human subjectivity, the proposed method provides a level of objectivity through the utilisation of image evidence, guided by candidate crater identifications.

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Automated quantitative measurements and associated error covariances for planetary image analysis

Cited by 6 publications

References 31 publications

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

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

The statistical properties of raw and preprocessed ToF mass spectra

Estimating False Positive Contamination in Crater Annotations from Citizen Science Data

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