Orthogonal partial-least squares discriminant analysis (OPLS-DA) using in-house R scripts (R foundation for statistical computing, Vienna, Austria) and the ropls package were used to interrogate the CPMG (global metabolomics) and AXINON® lipoFIT® (targeted metabolomics) spectral data to identify metabolomics differences between patient groups.24 ,25 (link) All OPLS-DA models were validated on independent test data using external 10-fold cross-validation with 100 iterations. Details of this approach have been previously published.13 (link) Briefly, this involves repeated cycles of (i) balancing class sizes; (ii) random splitting of the spectral data into a training set (90% of data) and a test set (remaining 10% of data); (iii) construction of OPLS-DA models using the training set alone; and then (iv) determining the predictive accuracy of the OPLS-DA model using the independent test set. The validity of the metabolic separation between patient groups was confirmed if the mean predictive accuracy of the ensemble of model accuracies was significantly higher than the mean predictive accuracy of a separate ensemble created by random class assignments on the same spectral data.
R script
Manufactured by R Project for Statistical Computing
Sourced in Austria
R script is a programming language and software environment for statistical computing and graphics. It provides a wide range of statistical and graphical techniques, and is highly extensible.
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Lab products found in correlation
Survival, by R Project for Statistical Computing (1 mentions)
Survminer, by R Project for Statistical Computing (1 mentions)
Image j, by R Project for Statistical Computing (1 mentions)
Prism 9, by GraphPad (1 mentions)
Peroxidase blocking, by Agilent Technologies (1 mentions)
Ki 67 antibody, by Agilent Technologies (1 mentions)
Aec 3 amino 9 ethyl carbazole chromogen, by Thermo Fisher Scientific (1 mentions)
Ultravision lp detection system, by Thermo Fisher Scientific (1 mentions)
Schiff reagent, by Merck Group (1 mentions)
Mhii broth, by Merck Group (1 mentions)
Originpro 2017, by OriginLab (1 mentions)
14 protocols using r script
1
Metabolomics Profiling using OPLS-DA
2
Amygdalar Receptor Profiling and Clustering
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Data analysis was performed using in-house R-scripts (R Foundation for Statistical Computing; http://www.r-project. org) mainly based on the R-built-in functions hclust() for hierarchical clustering, k-means() for k-means clustering and iso.MDS() for nonlinear, multidimensional scaling (MDS).
For each subdivision of the amygdala, the normalized mean densities of 15 receptors (in %) were combined into a feature vector. The Euclidean distances between each pair of vectors quantified the degree of dissimilarity between the subdivisions and their multi-receptor balance. A nonlinear, multidimensional scaling (MDS) visualized these dissimilarities in two-dimensions (Fig. 4a). The grouping of the subdivisions into clusters sharing a similar balance of receptors was established using a hierarchical cluster analysis (Ward linkage with Euclidean distances).
The resulting clusters were further validated using a silhouette analysis by assessing the degree of separation 1 3
For each subdivision of the amygdala, the normalized mean densities of 15 receptors (in %) were combined into a feature vector. The Euclidean distances between each pair of vectors quantified the degree of dissimilarity between the subdivisions and their multi-receptor balance. A nonlinear, multidimensional scaling (MDS) visualized these dissimilarities in two-dimensions (Fig. 4a). The grouping of the subdivisions into clusters sharing a similar balance of receptors was established using a hierarchical cluster analysis (Ward linkage with Euclidean distances).
The resulting clusters were further validated using a silhouette analysis by assessing the degree of separation 1 3
3
Survival Analysis of Gene Expression
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KMSA was performed using customized R scripts (http://www.r-project.org/ ). Patients’ gene expression, clinical and scoring data was matched using the assigned patient barcodes. For each gene expression or score, patients were assigned to two groups; Upper Quartile (UQ) and Lower Quartile (LQ) based on whether their gene expression or score values were in the bottom 25% or the top 25% of gene expression or score. KMSA was performed with the R package survminer (survminer/index.html">https://cran.r-project.org/web/packages/survminer/index.html ) using default arguments and Rho = 0, and the R package survival (survival/index.html">https://cran.r-project.org/web/packages/survival/index.html ) using default settings to extract a log rank test p-value for overall survival of up to 1825 days (5 years). p-values of significant associations were −log10 transformed for visualization.
4
Image Analysis and Data Modeling for Biological Insights
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5
Differential Protein Abundance Analysis
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Normalized protein abundances were exported from Progenesis QI and arcsinh-transformed. Using an in-house written R script (R Foundation for Statistical Computing, Vienna, Austria). Benjamini–Hochberg corrected one-way ANOVA was used for the calculation of the FDR-corrected p-values (29 (link)). For proteins passing a significance level of 0.05, a post hoc test (Tukey’s honest significant difference method) was conducted to obtain p-values for pairwise comparisons.
6
Standardized Microarray Data Analysis
7
Randomized ICU Intervention Trial
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We will randomly allocate participating ICUs to receive “A&F only” or “A&F with action implementation toolbox” in a 1:1 ratio. We will randomise ICUs using a block randomisation, with randomly permuted blocks of two or four each consisting of an equal number of interventions and controls. A researcher, who is otherwise unaffiliated with the study and blinded to the identity of the units, will perform the randomisation according to a computer-generated random schedule produced using an R script (R Foundation for Statistical Computing; Vienna, Austria) before the study starts. The size and the contents of the randomisation blocks will be concealed from the investigators enrolling the ICUs. Participants will not be told explicitly what aspect of the intervention is randomised; they will only be aware that there are two variations of providing A&F. Due to the character of the intervention, it is not possible to blind the investigators.
8
Performing Statistical Analysis on Proteomics Data
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Statistics comparing the two groups were done using the unpaired non-parametric t-test or Mann-Whitney t-test. When more than two groups were compared, a one-way ANOVA was used with a Tukey post-test. (GraphPad Prism software; GraphPad Software Inc., San Diego, USA). For statistical analysis of proteomics data, normalized protein abundances were exported from Progenesis QI software and transformed using arcsinh-function. Using an in-house written R script (R Foundation for Statistical Computing, Vienna, Austria), Benjamini–Hochberg corrected one-way ANOVA was used for the calculation of the FDR-corrected p-values as described earlier (25 (link)).
9
Inflammatory and Cell Signaling Profiling
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The PCA plots were generated using R Script (R Foundation for Statistical Computing, Vienna, Austria), and heat maps were generated using Prism 9.0.0 (GraphPad, San Diego, CA) based on differentially expressed inflammatory mediators (IL-6, IL-8, IL-10, IL-1β, and TNF-α) and cell signaling mediators (Akt, STAT3, JNK, p70 S6 kinase, and NF-кB).
10
Statistical Analysis of Proteomics Data
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Statistics comparing the two groups were done using the unpaired non-parametric t-test or Mann-Whitney t-test. When more than two groups were compared, a one-way ANOVA was used with a Tukey post-test. (GraphPad Prism software; GraphPad Software Inc., San Diego, USA). For statistical analysis of proteomics data, normalized protein abundances were exported from Progenesis QI software and transformed using arcsinh-function. Using an in-house written R script (R Foundation for Statistical Computing, Vienna, Austria), Benjamini–Hochberg corrected one-way ANOVA was used for the calculation of the FDR-corrected p-values as described earlier [81 (link)].
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