pls toolbox version 9 1 eigenvector  (MathWorks Inc)

Journal: Nature Communications

Article Title: SARS-CoV-2 infection results in immune responses in the respiratory tract and peripheral blood that suggest mechanisms of disease severity

doi: 10.1038/s41467-022-30088-y

Figure Lengend Snippet: a Heatmaps with unsupervised clustering of SARS-CoV-2-specific antibodies in COVID-19 respiratory (endotracheal tube aspirate (ETA), sputum, or pleural fluid) and plasma samples. b median fluorescence intensity of IgM, IgG, IgA1, and IgA2 antibodies against receptor binding domain (RBD), spike proteins (S), and nucleoprotein (NP) of SARS-CoV-2 (SARS2), SARS-CoV-1 (SARS1), and other human coronaviruses (229E, NL63, OC43, HKU1) between COVID-19 and non-COVID-19 respiratory samples. The bounds of the box plot indicate the 25 th and 75 th percentiles, the bar indicates medians, and the whiskers indicate minima and maxima. Statistical significance was determined with a two-sided Mann-Whitney test. The P values for IgM against SARS2 RBD, SARS2 S1, SARS2 S2 and SARS2 Trimer S are 0.0103, 0.0143, 0.0143, 0.0103, respectively. The P values for IgG against SARS2 RBD, SARS2 S1, SARS2 S2, SARS2 Trimer S, SARS2 NP, SARS1 Trimer S and SARS1 NP are 0.0150, 0.0258, 0.0033, 0.0194, 0.0050, 0.0194, 0.0050, respectively. The P values for IgA1 against SARS2 RBD, SARS2 S1, SARS2 S2, and SARS2 Trimer S are 0.0437, 0.0258, 0.0072, 0.0258, respectively. The P values for IgA2 against SARS2 RBD, SARS2 S1, SARS2 S2, SARS2 Trimer S, SARS2 NP, SARS1 NP are 0.0258, 0.0258, 0.0103, 0.0339, 0.0143, 0.0258, respectively. c Partial Least-Squares Discriminant Analysis (PLSDA) scores and loading plots of ETA and plasma from five COVID-19 and five non-COVID-19 patients with the smallest difference in days post disease onset between ETA and plasma samples. n COVID-19 ETA = 10, n COVID-19 Sputum = 3, n COVID-19 pleural fluid = 1, n Respiratory matched COVID-19 plasma = 13, n Non-COVID-19 ETA = 5, n Non-COVID-19 sputum = 1. Source data are provided as a Source Data file.

Article Snippet: Partial least squares discriminant analysis (PLSDA), performed in Eigenvectors PLS toolbox 8.2 in Matlab 2017b, was used in conjunction with Elastic-Net, described above, to identify and visualize signatures that distinguish categorical outcomes (COVID-19 diagnosis, NIH scores, drug therapies).

Techniques: Clinical Proteomics, Fluorescence, Binding Assay, MANN-WHITNEY

Journal: Nature Communications

Article Title: SARS-CoV-2 infection results in immune responses in the respiratory tract and peripheral blood that suggest mechanisms of disease severity

doi: 10.1038/s41467-022-30088-y

Figure Lengend Snippet: a Levels of cytokines, soluble IL-6 receptor α (sIL-6Rα), and IL-6:sIL-6Rα ratio, b anti-RBD IgM, IgG, and IgA titres, microneutralization titres and c Partial Least-Squares Discriminant Analysis (PLSDA) scores and loadings plot of antibodies against human coronavirus between mild/moderate and severe/critical COVID-19 patients. d Volcano plot showing fold difference of 83 immunological features in blood samples between mild/moderate and severe/critical COVID-19 patients, and comparisons of cellular subset frequencies and correlation with days stayed in hospital. n Mild-Moderate V1 = 25, n Mild-Moderate V7 = 14, n Severe-Critical V1 = 31, n Severe-Critical V7 = 16. The bounds of the box plot indicate the 25th and 75th percentiles, the bar indicates medians, and the whiskers indicate minima and maxima. Statistical significance was determined with a two-sided Kruskal-Wallis test followed by Dunn’s multiple comparisons test. Partial Least-Squares Discriminant Analysis was performed for antibodies measured with multiplex bead array assay. Volcano plots were created using a two-sided Wilcoxon rank-sum test and statistics were corrected with FDR adjustment. Correlation was determined with Spearman’s correlation. V1, hospital admission; V7, hospital discharge. Source data are provided as a Source Data file.

Article Snippet: Partial least squares discriminant analysis (PLSDA), performed in Eigenvectors PLS toolbox 8.2 in Matlab 2017b, was used in conjunction with Elastic-Net, described above, to identify and visualize signatures that distinguish categorical outcomes (COVID-19 diagnosis, NIH scores, drug therapies).

Techniques: Multiplex Assay

Journal: Nature Communications

Article Title: SARS-CoV-2 infection results in immune responses in the respiratory tract and peripheral blood that suggest mechanisms of disease severity

doi: 10.1038/s41467-022-30088-y

Figure Lengend Snippet: a Levels of cytokines, soluble IL-6 receptor α (sIL-6Rα), and IL-6:sIL-6Rα ratio; b anti-RBD IgM, IgG, and IgA titres, microneutralization titres; c Partial Least-Squares Discriminant Analysis (PLSDA) scores and loadings plot of antibodies against human coronaviruses; d cellular immune subset frequencies between COVID-19 patients with or without dexamethasone treatment (with/without remdesivir). n No drug V1 = 24, n No drug V7 = 13, n Drug V1 = 32, n Drug V7 = 17. The bounds of the box plot indicate the 25th and 75th percentiles, the bar indicates medians, and the whiskers indicate minima and maxima. Statistical significance was determined with a two-sided Kruskal-Wallis test followed by Dunn’s multiple comparisons test. Partial Least-Squares Discriminant Analysis was performed for antibodies measured with multiplex bead array assay. Volcano plots were created using a two-sided Wilcoxon rank-sum test and statistics were corrected with FDR adjustment. V1, hospital admission; V7, hospital discharge. Source data are provided as a Source Data file.

Article Snippet: Partial least squares discriminant analysis (PLSDA), performed in Eigenvectors PLS toolbox 8.2 in Matlab 2017b, was used in conjunction with Elastic-Net, described above, to identify and visualize signatures that distinguish categorical outcomes (COVID-19 diagnosis, NIH scores, drug therapies).

Techniques: Multiplex Assay