Recent trends in multi-block data analysis in chemometrics for multi-source data integration

Mishra, Puneet; Roger, Jean Michel; Bouveresse, Delphine Jouan-Rimbaud; Biancolillo, Alessandra; Marini, Federico; Nordon, Alison; Rutledge, Douglas N.

doi:10.1016/j.trac.2021.116206

Cited by 112 publications

(65 citation statements)

References 56 publications

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“…Accordingly, implementing appropriate spectral preprocessing and variable selection can improve the accuracy of a fitted model (Zou et al, 2010 ; Gerretzen et al, 2016 ). For example, the SNV-SVR model is based on SNV, which reduces particle size noise effects by scaling each spectrum to have an SD of 1.0 and by utilizing accurate prediction performance of both catechins and caffeine as suggested by Wang et al (Wang et al, 2020 ); DET can reduce the curvature of each spectrum (Murphy et al, 2021 ); and BS can balance the effect of the modeled blocks, to avoid any block dominating the model (Mishra et al, 2021 ). In this study, this exercise shows that the sMC algorithm combined with PLS can efficiently identify the useful informative wavelength to provide a promising and robust correction model for the prediction of MDA.…”

Section: Discussionmentioning

confidence: 99%

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

et al. 2021

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Drought is a major abiotic stress that adversely affects the growth and productivity of plants. Malondialdehyde (MDA), a substance produced by membrane lipids in response to reactive oxygen species (ROS), can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to drought stress tolerance. Still measuring MDA is usually a labor- and time-consuming task. In this study, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of MDA, and the application of this technique to plant drought stress experiments was also investigated. Two exotic conifer tree species, namely, slash pine (Pinus elliottii) and loblolly pine (Pinus taeda), were used as plant material exposed to drought stress; different types of spectral preprocessing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best MDA-predicting model. The results show that the best PLS model is established via the combined treatment of detrended variable–significant multivariate correlation algorithm (DET-sMC), where latent variables (LVs) were 6. This model has a respectable predictive capability, with a correlation coefficient (R2) of 0.66, a root mean square error (RMSE) of 2.28%, and a residual prediction deviation (RPD) of 1.51, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the MDA content in real time.

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Section: Discussionmentioning

confidence: 99%

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

et al. 2021

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“…In particular, Witten and Tibshirani [43] developed a supervised sparse canonical correlation model in order to find significant linear combinations between copy number and gene expression data. For an extensive reading on the extended family of PCA and PLS methods we refer the reader to Mishra et al [28], O'Shea and Misra [35], Gromski et al [94], Mendez et al [95].…”

Section: Study Focusmentioning

confidence: 99%

“…Table 3 provides a brief summary of some of the popular and recent tools that support multi-omics analysis, indicating which integrative analysis type based on study focus each tool is most suitable for. For comprehensive surveys on available software for integrative analysis we refer the reader to Mishra et al [28], Pinu et al [96].…”

Section: Study Focusmentioning

confidence: 99%

“…An arsenal of mathematical and computational techniques was developed to achieve integrative analyses ranging from least squares-based models [28] to Bayesian models [29] and deep learning models [30,31]. In the era of high-throughput data, it became necessary to look into the fundamentals of integrating multi-omics data to provide early career researchers with a guidance on considerations that should be regarded when developing multi-omics data integration methods.…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 provides a review of this primer on data integration and summarizes the different categories that we discuss later. We acknowledge that there are various surveys on statistical multi-omics integrative methods in the literature [28,[32][33][34][35][36][37]; however, these often focus on one specific aspect of data integration. By contrast, our review covers more comprehensive discussions on a higher level about the heuristics of data integration and the considerations relevant to the integrative analysis process.…”

Section: Introductionmentioning

confidence: 99%

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Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Jendoubi

2021

Metabolites

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Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria — hypothesis, data types, strategies, study design and study focus — to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

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Data fusion of multiple‐information strategy based on Fourier transform near infrared spectroscopy and Fourier‐transform mid infrared for geographical traceability of Wolfiporia cocos combined with chemometrics

Zhang²,

Zuo

et al. 2022

Journal of Chemometrics

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Owing to the widespread concern relating to herb safety and quality, there is a momentum to discriminate the geographical traceability of fungus with multiple‐information technologies. In this study, we attempted to evaluate the fusion strategy of multiple‐information for the geographical traceability of this fungus based on Fourier transform near infrared spectroscopy (FT‐NIR) and Fourier‐transform mid infrared spectroscopy (FT‐MIR) with chemometrics. From all results, (1) comparative visualization of t‐distributed stochastic neighbor embedding (t‐SNE) emerged that the superior effect of FT‐NIR spectroscopy was better than FT‐MIR, which were separated according to the samples of different regions; (2) in view of optimized spectrum, the geographical traceability of extreme learning machine (ELM) model was better than the partial least squares discriminant analysis (PLS‐DA) and support vector machine (SVM) models that the accuracy of training set for all models was 100%; (3) the accuracy of train and test for ELM model were 100% with single spectrum, low‐level, and mid‐level fusion of FT‐NIR and FT‐MIR based on principal components (PCs) and competitive adaptive weighting algorithm (CARS), which was suitable for geographical traceability and evaluation of this fungus. (4) The three models based on successive projection algorithm (SPA) were not dissatisfied for the geographical traceability of this fungus. (5) The result of hierarchical cluster analysis (HCA) was consistent with the above results, which could distinguish samples according to different regions. All results showed that models could be developed as superior model on the foundation of single spectrum based on FT‐NIR, low‐level or mid‐level (except SPA) fusion of ELM, which could be employed in geographical traceability of Wolfiporia cocos, effectively.

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Recent trends in multi-block data analysis in chemometrics for multi-source data integration

Cited by 112 publications

References 56 publications

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Data fusion of multiple‐information strategy based on Fourier transform near infrared spectroscopy and Fourier‐transform mid infrared for geographical traceability of Wolfiporia cocos combined with chemometrics

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