hcapca: Automated Hierarchical Clustering and Principal Component Analysis of Large Metabolomic Datasets in R

Chanana, Shaurya; Thomas, Chris S.; Zhang, Fan; Rajski, Scott R.

doi:10.3390/metabo10070297

Cited by 27 publications

(30 citation statements)

References 77 publications

(112 reference statements)

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“…It was observed that the types of honey samples are successfully differed from each other. Spectral-based HCA assumes that samples with similar spectral profiles are chemically related and should be assigned to a single group [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools

Elhamdaoui

Orche

Cheikh

et al. 2020

Journal of Analytical Methods in Chemistry

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In this study, the Fourier transform mid-infrared (FT-MIR) spectroscopy technique combined with chemometrics methods was used to monitor adulteration of honey with sugar syrup. Spectral data were recorded from a wavenumber region of 4000–600 cm−1, with a spectral resolution of 4 cm−1. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for qualitative analysis to discriminate between adulterated and nonadulterated honey. For quantitative analysis, we used partial least-squares regression (PLS-R) and the support vector machine (SVM) to develop optimal calibration models. The use of PCA shows that the first two principal components account for 96% of the total variability. PCA and HCA allow classifying the dataset into two groups: adulterated and unadulterated honey. The use of the PLS-R and SVM-R calibration models for the quantification of adulteration shows high-performance capabilities represented by a high value of correlation coefficients R2 greater than 98% and 95% with lower values of root mean square error (RMSE) less than 1.12 and 1.85 using PLS-R and SVM-R, respectively. Our results indicate that FT-MIR spectroscopy combined with chemometrics techniques can be used successfully as a simple, rapid, and nondestructive method for the quantification and discrimination of adulterated honey.

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Section: Resultsmentioning

confidence: 99%

Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools

Elhamdaoui

Orche

Cheikh

et al. 2020

Journal of Analytical Methods in Chemistry

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“…CA is usually used to analyze two-way data tables, including several sizes of relationships between rows and columns [29]. Hierarchical Clustering on Principal Components (HCPC) is a combination of Hierarchical Clustering with Principal Components of the PCA model [30]. HCPC is objective grouping techniques on the results of principal component analysis, which leads to better cluster solutions [29].…”

Section: Discussionmentioning

confidence: 99%

Multivariate analysis of five chicken breed in Indonesia based on microsatellite allele frequency

Saputra¹,

Sartika²,

Anggraeni³

et al. 2021

Livest. Anim. Res.

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Objective: This study tries to examine several multivariate methods in classifying genetic diversity using microsatellite allele frequency data.Methods: This study used microsatellite allele frequency data from White Leghorn (n = 48), Kampung (n = 48), Pelung (n = 24), Sentul (n = 24), and Black Kedu (n = 25) from Indonesian Research Institute for Animal Production. Allele frequency data were analyzed by the Neighbor-Joining (NJ) method using the POPTREE2 program. The data was also analyzed by the Principal Component Analysis (PCA), Correspondence Analysis (CA), and Hierarchical Clustering on Principal Components (HCPC) methods using the factoextra and FactoMineR packages in the R 4.0.0 program.Results: Correspondence Analysis (CA) found Sentul is more closer to Black Kedu. However, based on NJ, PCA, and HCPC showed Sentul is closer to Kampung. Based on the value of Dimension 1, Correspondence Analysis (80.7%) can explain greater variation than PCA (58.9%). However, CA method generated different results compared to NJ, PCA, and HCPC. NJ, PCA, and HCPC found four chicken clusters, namely cluster 1 (White Leghorn), cluster 2 (Pelung), cluster 3 (Black Kedu), and cluster 4 (Kampung and Sentul).Conclusions: In conclusion, HCPC is a better multivariate method for analyzing allele frequency data than PCA and CA. HCPC can be used to analyze allele frequency data better than PCA, because HCPC is a combination of methods from hierarchical clustering and principal components.

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“…In order to cluster the metabolites, principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) are normally used in the R packages or other tools, such as MetaboAnalyst, VOCCluster [93,95]. Tools with new features, such as interactive time-series cluster analysis (R package MetaboClust) [96], automated hierarchical cluster (R package hcapca) [97] are also developed for clustering analysis. Bayesian network method (BNM) can model the interactions of the metabolites to identify important metabolites in the optimal network, which has been demonstrated in the study of Rogers et al (2014) [98].…”

Section: Methods and Tools Applied In Metabolomics Analysismentioning

confidence: 99%

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

Hao

Bai

et al. 2021

Metabolites

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Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

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hcapca: Automated Hierarchical Clustering and Principal Component Analysis of Large Metabolomic Datasets in R

Cited by 27 publications

References 77 publications

Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools

Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools

Multivariate analysis of five chicken breed in Indonesia based on microsatellite allele frequency

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

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