XNet: A Bayesian Approach to Extracted Ion Chromatogram Clustering for Precursor Mass Spectrometry Data

Gutierrez, Mathew; Handy, Kyle; Smith, Rob

doi:10.1021/acs.jproteome.9b00068

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…There are many software tools that perform one or more stages of this process, including MaxQuant, Perseus, moFF, ProStar/DAPAR, and others. − We have previously described an algorithm for rapid label-free peptide quantification and implemented it in FlashLFQ, a free, open-source software program . We have since added significant functionality to FlashLFQ that includes intensity normalization, match-between-runs, relative protein quantification, and hypothesis testing.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Null Interval Hypothesis Test Controls False Discovery Rates and Improves Sensitivity in Label-Free Quantitative Proteomics

et al. 2020

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Statistical significance tests are a common feature in quantitative proteomics workflows. The Student’s t-test is widely used to compute the statistical significance of a protein’s change between two groups of samples. However, the t-test’s null hypothesis asserts that the difference in means between two groups is exactly zero, often marking small but uninteresting fold-changes as statistically significant. Compensations to address this issue are widely used in quantitative proteomics, but we suggest that a replacement of the t-test with a Bayesian approach offers a better path forward. In this article, we describe a Bayesian hypothesis test in which the null hypothesis is an interval rather than a single point at zero; the width of the interval is estimated from population statistics. The improved sensitivity of the method substantially increases the number of truly changing proteins detected in two benchmark data sets (ProteomeXchange identifiers PXD005590 and PXD016470). The method has been implemented within FlashLFQ, an open-source software program that quantifies bottom-up proteomics search results obtained from any search tool. FlashLFQ is rapid, sensitive, and accurate and is available both as an easy-to-use graphical user interface (Windows) and as a command-line tool (Windows/Linux/OSX).

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Section: Introductionmentioning

confidence: 99%

A Bayesian Null Interval Hypothesis Test Controls False Discovery Rates and Improves Sensitivity in Label-Free Quantitative Proteomics

et al. 2020

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“…XFlow outperforms existing XIC algorithms evaluated recently on a benchmark human-curated dataset and provides qualitative evidence in support of high-function on PLOS ONE alternative datasets. The output of XFlow can be used in conjunction with the XIC clustering algorithm Xnet [7] to map raw data points from an LC-MS run into the signal groups necessary for further analysis (see Fig 2).…”

Section: Introductionmentioning

confidence: 99%

XFlow: An algorithm for extracting ion chromatograms

Gutierrez

Smith

2020

PLoS ONE

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Mass spectrometry is a fundamental tool for modern proteomics. The increasing availability of mass spectrometry data paired with the increasing sensitivity and fidelity of the instruments necessitates new and more potent analytical methods. To that end, we have created and present XFlow, a feature detection algorithm for extracting ion chromatograms from MS1 LC-MS data. XFlow is a parameter-free procedurally agnostic feature detection algorithm that utilizes the latent properties of ion chromatograms to resolve them from the surrounding noise present in MS1 data. XFlow is designed to function on either profile or centroided data across different resolutions and instruments. This broad applicability lends XFlow strong utility as a one-size-fits-all method for MS1 analysis or target acquisition for MS2. XFlow is written in Java and packaged with JS-MS, an open-source mass spectrometry analysis toolkit.

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“…XFlow outperforms existing XIC algorithms evaluated recently on a benchmark human-curated dataset and provides qualitative evidence in support of high-function on alternative datasets. The output of XFlow can be used in conjunction with the XIC clustering algorithm XNet 6 to map raw data points from an LC-MS run into the signal groups corresponding to particular molecules at particular charge states.…”

Section: Introductionmentioning

confidence: 99%

XFlow: An algorithm for extracting ion chromatograms

Gutierrez

Smith

2019

Preprint

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13Mass spectrometry is a fundamental tool for modern proteomics. The increasing availability of 14 mass spectrometry data paired with the increasing sensitivity and fidelity of the instruments 15 necessitates new and more potent analytical methods. To that end, we have created and present 16 XFlow, a feature detection algorithm for extracting ion chromatograms from MS1 LC-MS data. 17 XFlow is a parameter-free procedurally agnostic feature detection algorithm that utilizes the latent 18 properties of ion chromatograms to resolve them from the surrounding noise present in MS1 data. 19 XFlow is designed to function on either profile or centroided data across different resolutions and 20 instruments. This broad applicability lends XFlow strong utility as a one-size-fits-all method for 21 MS1 analysis or target acquisition for MS2. XFlow is written in Java and packaged with JS- MS, 22 an open-source mass spectrometry analysis toolkit. 23 24 25Mass spectrometry is a popular approach for measuring the sample-bound content and quantity of 26 a variety of classes of molecules across a broad range of applications including pharmaceuticals, 2 27 forensics, biochemistry, and food science. All applications of mass spectrometry have a common 28 problem: the instrument itself does not provide measurements of molecules nor their identities, but 29 rather produces raw data that must be rendered human-interpretable through the application of data 30 processing algorithms. 31 32 According to community perceptions, advancements in software have lagged behind the steady 33 pace of instrumentation advancements. 1 Unlike other computational science fields (such as 34 genomics) where several foundational computational problems are regarded as solved, most mass 35 spectrometry users feel that significant problems in computational mass spectrometry remain 36 unsolved 1 despite (in some cases) dozens of published algorithms designed to address them. 2 37 Beyond user sentiment, the experimental influence of algorithm selection suggests that the analysis 38 and advancement of computational mass spectrometry algorithms is a valuable pursuit. 3 39 40 Mass spectrometry systems generate datasets that quantify counts of charged particles at specific 41 mass-to-charge (m/z) values. In liquid chromatography-mass spectrometry (LC-MS) systems, 42 these measurements are taken over the time (retention time or RT) required for the molecules to 43 elute from a chromatography column designed to slow or speed the migration of the molecules 44 depending on particular physico-chemical properties such as size, or polarity.45 46 Mapping the raw LC-MS data points to particular classes of molecule (say, a particular peptide at 47 gap present between isotopic-specific sub-signals (extracted ion chromatograms or XICs) in the 51 molecule's signal (see Figure 1). 52 Fig 1. 3d Isotopic Envelope. In this figure are five extracted ion chromatograms (XIC) bounded 53 by yellow rectangles. Each XIC is composed of points, each with m/z, RT and intensity...

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XNet: A Bayesian Approach to Extracted Ion Chromatogram Clustering for Precursor Mass Spectrometry Data

Cited by 4 publications

References 14 publications

A Bayesian Null Interval Hypothesis Test Controls False Discovery Rates and Improves Sensitivity in Label-Free Quantitative Proteomics

A Bayesian Null Interval Hypothesis Test Controls False Discovery Rates and Improves Sensitivity in Label-Free Quantitative Proteomics

XFlow: An algorithm for extracting ion chromatograms

XFlow: An algorithm for extracting ion chromatograms

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