Factoextra

Manufactured by Posit

Factoextra is a lab equipment product that facilitates the extraction and visualization of results from factor analysis, including principal component analysis (PCA) and multiple correspondence analysis (MCA). It provides a set of functions for easily extracting and visualizing the output of these multivariate analyses.

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Lab products found in correlation

Factominer, by Posit (1 mentions) Rstudio version 1, by Posit (1 mentions) Spss 19.0 statistic software, by IBM (1 mentions) Model uv 1700, by Shimadzu (1 mentions) Ggplot2, by Posit (1 mentions)

4 protocols using factoextra

Principal Component Analysis of COVID-19 Symptoms

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Unsupervised principal components analyses (PCA) were performed in R Studio. Complete data were scaled to variance units using FactoMineR (v2.4 R studio) and the PCA results were extracted and visualized using factoextra (v1.0.7 R Studio). For Figures 2, 3, the PCA was performed using all samples data and graphed by days from the onset of symptoms (Early ≤ 43 days; Late > 43 days) for Figure 2 or by the severity of symptoms (mild or more severe) for Figure 3. In Figures 4, 5 the data was divided into two subgroups by the days from the onset of symptoms criteria and then the PCA was performed using severity of symptoms for Figure 4 or sex for Figure 5.

Fuentes-Villalobos F., Garrido J.L., Medina M.A., Zambrano N., Ross N., Bravo F., Gaete-Argel A., Oyarzún-Arrau A., Amanat F., Soto-Rifo R., Valiente-Echeverría F., Ocampo R., Esveile C., Ferreira L., Cabrera J., Torres V., Rioseco M.L., Riquelme R., Barría S., Alvarez R., Pinos Y., Krammer F., Calvo M, & Barria M.I. (2022). Sustained Antibody-Dependent NK Cell Functions in Mild COVID-19 Outpatients During Convalescence. Frontiers in Immunology, 13, 796481.

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Biochar's Impact on Peanut Productivity

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SPSS 19.0 statistic software (SPSS Inc., Chicago, IL, United States) was used to perform the statistical analysis. Year and biochar application were assumed to be fixed factor and the replicates were assumed to be random factors. Error bars in the figures represent standard errors of the mean. Least significant differences were used to separate treatment means at the 5% probability level. Regression analysis was used to evaluate the relationships between leaf nitrogen content and net photosynthetic rate, net photosynthetic rate and peanut yield. The responses of chlorophyll fluorescence parameters, gas exchange parameters, leaf nitrogen content, yield, and yield components to biochar application were further analyzed with the principal component analysis in R studio version 1.1.442 using the Factoextra package (Kassambara, 2015 ).

Wang S., Zheng J., Wang Y., Yang Q., Chen T., Chen Y., Chi D., Xia G., Siddique K.H, & Wang T. (2021). Photosynthesis, Chlorophyll Fluorescence, and Yield of Peanut in Response to Biochar Application. Frontiers in Plant Science, 12, 650432.

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Coastal water nutrient analysis by PCA

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Nutrient measurements were done with colorimetric methods using a UV-visible spectrophotometer (SHIMADZU, Model UV-1700). Ammonium, NOx (nitrites and nitrates), soluble reactive silicate (SRSi) and soluble reactive phosphorus (SRP), were analyzed [42 –43 (link)]. All analyses were performed in triplicate in the Chemistry Laboratory at ECOSUR, Chetumal, Mexico.
Principal Component Analysis (PCA) was used to describe the relationship between the chemical variables measured in the water with each sampling location. The compiled data set representing the environmental variables analyzed in this study was transformed into a "site x variable" matrix. Euclidean distance and ordinations were plotted with FactoMineR and factoextra in Rstudio [44 (link)].

Yanez-Montalvo A., Gómez-Acata S., Águila B., Hernández-Arana H, & Falcón L.I. (2020). The microbiome of modern microbialites in Bacalar Lagoon, Mexico. PLoS ONE, 15(3), e0230071.

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Data Visualization Techniques for Scientific Research

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Statistical and data analyses were performed using GraphPad Prism 8.4.3, R 4.0.4, and R Studio 1.4.1103. Graphs were generated in Prism and R Studio and statistical differences between two groups were calculated by Mann-Whitney U-test. Statistical significance was defined as ^∗p < 0.05; ^∗∗p < 0.01; ^∗∗∗p < 0.001, and ^∗∗∗∗p < 0.0001. Scatter plots, bar graphs, heatmaps, and polar plots were visualized with ggplot2 (v3.3.3 R Studio). Correlation analysis (in Figures 7 and S4) were performed using the R package “correlation” (v0.6.0) in R Studio. Polar plots represent the value of different variables normalized to the Z-score of data. Each variable was mean-centered and then divided by the standard deviation of the variable to ensure each variable had zero mean and unit standard deviation. Unsupervised principal components analysis (PCA) was performed in R. The completed data were scaled to unit variance using FactoMineR (v2.4 R studio). The PCA results were extracted and visualized using factoextra (v1.0.7 R Studio). Outlier exclusion was performed using Prism. n and N values are mentioned at figure captions.

Garrido J.L., Medina M.A., Bravo F., McGee S., Fuentes-Villalobos F., Calvo M., Pinos Y., Bowman J.W., Bahl C.D., Barria M.I., Brachman R.A, & Alvarez R.A. (2022). IgG targeting distinct seasonal coronavirus- conserved SARS-CoV-2 spike subdomains correlates with differential COVID-19 disease outcomes. Cell Reports, 39(9), 110904.

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