Application of Subwindow Factor Analysis and Mass Spectral information for accurate alignment of non-targeted metabolic profiling

Yang, Tianjun; Pan, Yan; He, Min; Hong, Liang; Pei, Rui; Zhang, Zhimin; Yi, Lunzhao; Yuan, Xin-Ya

doi:10.1016/j.chroma.2018.05.071

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…Factor scores are normalized with the mean value of 0 and an SD of 1. The factor scores of the compositions were greater than 1 (Figure ), which are regarded as the characteristic compounds . The results are shown in Table .…”

Section: Resultsmentioning

confidence: 92%

“…Reportedly, it can be employed in the determination of a number of compounds, peak purity problems, resolution of overlapped compounds, or as an extension to the simultaneous analysis of multiple runs (higher‐order data structures) to obtain qualitative and quantitative information. Subwindow FA was used to calibrate the position between the chromatogram and the reference chromatogram for the accurate alignment of non‐targeted metabolic profiling .…”

Section: Introductionmentioning

confidence: 99%

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Determination and comparison of agarwood from different origins by comprehensive two‐dimensional gas chromatography–quadrupole time‐of‐flight mass spectrometry

Sun

Zhang

Liu

et al. 2020

J of Separation Science

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Agarwood, a species of resinous heartwood, is a precious medicinal plant and a type of rare natural spice, which is widely used in medicine, cosmetics, religious activities, and other fields. In this study, agarwood samples from eight different regions across four countries were analyzed by comprehensive two-dimensional gas chromatography−quadrupole time-of-flight mass spectrometry. A total of 232 species were identified (the match factors of these compounds were above 750). The main compounds of agarwood are oxygenated sesquiterpenes and chromones. The compositions of India1 and Malaysia2 were significantly different from those of other samples, which might be attributed to the different production processes of agarwood.For further investigation, factor analysis was conducted for six agarwood samples. The results showed that the data classification possessed a regional characteristic;according to the retention time and relative content, characteristic compositions were determined by factor scores. Finally, the differences of characteristic compositions were simply analyzed, and the reasons were speculated. K E Y W O R D Sagarwood, comprehensive two-dimensional, factor analysis, gas chromatography, regional characteristic compositions 1284

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Section: Resultsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Determination and comparison of agarwood from different origins by comprehensive two‐dimensional gas chromatography–quadrupole time‐of‐flight mass spectrometry

Sun

Zhang

Liu

et al. 2020

J of Separation Science

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“…We compare our alignment results with two existing algorithms: the correlation optimized warping (COW) algorithm [27,28,29] 5 , and the GCalignR algorithm [15]. We investigated many other existing algorithms, however, most are unsuitable to compare with our algorithm -in some of the exiting algorithms the peak alignment module was not designed to be used on its own [30,11]; some algorithms do not have source code available [12]; some requires a specific MATLAB toolbox that we do not own [14,10]; and another built on proprietary software [13].…”

Section: Comparison With Existing Algorithmmentioning

confidence: 99%

“…These authors concluded that due to the complexity of the metabolome, all existing software will require further improvement and researchers are recommended to perform manual checks on the alignment of important biomarkers. More alignment algorithms have been developed since the review papers above [10,11,12,13,14,15], many of these utilising the mass spectral information for alignment. Current alignment algorithms are all based on traditional, symbolic artificial intelligence (AI) techniques, that is, the use of a set of formal, mathematical rules.…”

Section: Introductionmentioning

confidence: 99%

Peak alignment of gas chromatography–mass spectrometry data with deep learning

Wang

2019

Journal of Chromatography A

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We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC ∼ 1 for simple data sets and AUC ∼ 0.85 for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online.

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“…They are not very robust and are not suitable for the treatment of complex systems, including TCMs samples. Lately, an algorithm of combining sub‐window factor analysis with mass spectrum information (SFA‐MS) has been proposed to deal with the overlapping peaks in complex samples, which can remedy the lack of separation conditions.…”

Section: Introductionmentioning

confidence: 99%

Multi‐scale Gaussian/Haar wavelet strategies coupled with sub‐window factor analysis for an accurate alignment in nontargeted metabolic profiling to enhance herbal origin discrimination capability

Hong

Zhou

2019

J of Separation Science

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Metabolic dataset can provide an overview of different herbal origin, which is conducted by some statistical procedures. Such results often deviate to a certain degree, due to peaks shifts in chromatographic signals. In order to solve this problem, an improved algorithm of combining sub-window factor analysis with the mass spectrum information is proposed. The algorithm uses a peak detection approach derived either from multi-scale Gaussian function or Haar wavelet to locate the peaks with different application scope; the candidate drift points at each peak are estimated by Fast Fourier transform cross correlation; Specifically, the best drift points at each candidate peaks are confirmed by sub-window factor analysis and mass spectrum information in nontargeted metabolic profiling. Finally, the peak regions were aligned against a reference chromatogram, and the non-peak regions were used linear interpolation.The chromatographic signals of 30 Bupleurum samples were aligned as an illustration of this algorithm, and they could be well distinguished using some statistical procedures. The result demonstrates that the presented method is stronger than other mass-spectra based algorithms, when facing the alignment of some co-eluted peaks. K E Y W O R D Shaar wavelet, mass spectrometry, multi-scale gaussian function, peak alignment, traditional Chinese medicine

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Application of Subwindow Factor Analysis and Mass Spectral information for accurate alignment of non-targeted metabolic profiling

Cited by 13 publications

References 26 publications

Determination and comparison of agarwood from different origins by comprehensive two‐dimensional gas chromatography–quadrupole time‐of‐flight mass spectrometry

Determination and comparison of agarwood from different origins by comprehensive two‐dimensional gas chromatography–quadrupole time‐of‐flight mass spectrometry

Peak alignment of gas chromatography–mass spectrometry data with deep learning

Multi‐scale Gaussian/Haar wavelet strategies coupled with sub‐window factor analysis for an accurate alignment in nontargeted metabolic profiling to enhance herbal origin discrimination capability

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