Multivariate Hypergeometric Similarity Measure

Kaddi, Chanchala; Parry, R. Mitchell; Wang, May D.

doi:10.1109/tcbb.2013.28

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…This kind of similarity matching is performed in DetectTLC by first allowing the user to select the precursor ion spot of interest from a low collision-energy image (Supplementary Figure SI 9). This region of interest is then used to generate a template against which to measure similarity of the high collision-energy images using either Pearson correlation or the hypergeometric similarity measure [27, 28]. The results are displayed as a set of ion images, but the user also has the option of generating an all-fragment mass spectrum displaying peaks for m/z values with similar spatial distribution as the precursor.…”

Section: Resultsmentioning

confidence: 99%

DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Features

Kaddi

Bennett

Paine

et al. 2015

J. Am. Soc. Mass Spectrom.

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Full characterization of complex reaction mixtures is necessary to understand mechanisms, optimize yields, and elucidate secondary reaction pathways. Molecular-level information for species in such mixtures can be readily obtained by coupling mass spectrometry imaging (MSI) with thin layer chromatography (TLC) separations. User-guided investigation of imaging data for mixture components with known m/z values is generally straightforward; however, spot detection for unknowns is highly tedious, and limits the applicability of MSI in conjunction with TLC. To accelerate imaging data mining, we developed DetectTLC, an approach that automatically identifies m/z values exhibiting TLC spot-like regions in MS molecular images. Furthermore, DetectTLC can also spatially match m/z values for spots acquired during alternating high and low collision-energy scans, pairing product ions with precursors to enhance structural identification. As an example, DetectTLC is applied to the identification and structural confirmation of unknown, yet significant, products of abiotic pyrazinone and aminopyrazine nucleoside analog synthesis.

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Section: Resultsmentioning

confidence: 99%

DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Features

Kaddi

Bennett

Paine

et al. 2015

J. Am. Soc. Mass Spectrom.

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“…Mass spectra were processed in a similar way to previously described [23][24][25][26][27][28][29][30] . So, mass spectra were converted to vectors with 1m/z binning width and were normalized to total ion current.…”

Section: Discussionmentioning

confidence: 99%

“…The study of the dynamics of individual ion currents is widely used in the analysis of mass spectrometric images [24][25][26][27] . In this work, using the analysis of the dynamics of ion currents, we investigated the influence of the internal heterogeneity of the sample, as well as the processes of ion suppression and extraction on the interpretation of mass spectrometric profiles of glioblastoma samples.…”

Section: Introductionmentioning

confidence: 99%

Analysis of ion currents in mass spectrometric profiles using glioblastoma tissue

et al. 2021

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Background: The development of direct ambient ionization methods makes way for fast mass-spectrometry profiling of biological samples, which has great potential in medicine. Those methods, unlike traditional mass spectrometric analysis with chromatographic separation, are not able to take into account inter-ion interaction, ion suppression, and matrix effect due to the absence of chromatographic separation of the mixture components. So dynamics of ion current during direct ambient ionization mass-spectra is governed by the component micro-extraction and electrospray ionization influenced by the geometry of the sample, its position, and internal heterogeneity. Despite the progress in mass-spectrometry of biological samples, not much is known about the influence of sample type and structure on its molecular profile peculiarities. Methods: In this work, we propose to use analysis of the correlation between individual ion currents for a better understanding of ion current variability sources and grouping ions of high biological importance. Several fragments of glioblastoma tissue from a single patient are used for these purposes. Results: Ion currents have different dynamics considering different ions in different fragments. The correlation of two selected ion currents could be positive or negative for single fragment measurement. Correlations have persistent or alternating signs in different fragments for two selected ions. The spread of correlations of each pair of ion currents is calculated for evaluation of the signs’ stability. Conclusions: We were able to group ions according to the primary reason for their variabilities such as micro-extraction, mass-spectrometry measurement, or specimens' properties. Such grouping would allow the development of more reliable and reproducible methods of mass-spectrometry data analysis and improve the accuracy of results of its application in medicine.

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“…McDonnell et al (2008) investigated colocalization between ion images on the basis of (spatial) correlation. Kaddi et al (2013) used a hypergeometric similarity measure to retrieve ion images similar to a query ion image, while Bruand et al (2011aBruand et al ( , 2011b showed the use of clustering results and ROIs to find molecular signatures and ions of interest, using rank-based statistics.…”

Section: G Correlation Analysismentioning

confidence: 99%

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Verbeeck

Caprioli

Plas

2019

Mass Spectrometry Reviews

201

190

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Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high‐dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data‐driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry‐based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1–47, 2019.

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Multivariate Hypergeometric Similarity Measure

Cited by 10 publications

References 29 publications

DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Features

DetectTLC: Automated Reaction Mixture Screening Utilizing Quantitative Mass Spectrometry Image Features

Analysis of ion currents in mass spectrometric profiles using glioblastoma tissue

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

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