All treatments and analyses were performed in triplicate for statistical analyses. Data obtained were reported as mean value ± standard deviation. Statistical difference was analyzed by Tukey test (p < 0.05) with SigmaPlot software version 14, Systat Software, (San Jose, CA, USA). The composition and functional properties of casein and caseinate from EDBM-UF and chemical processes and from winter and summer milks were subjected to a two-way ANOVA with SigmaPlot software version 14, Systat Software, (San Jose, CA, USA).
Sigmaplot software version 14
Manufactured by Grafiti LLC
Sourced in United States
SigmaPlot software version 14 is a data analysis and graphing tool. It provides functions for data analysis, curve fitting, and creating publication-quality graphs.
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15 protocols using sigmaplot software version 14
1
Casein and Caseinate Composition Analysis
2
Survival Analysis of Treatment
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Statistical analysis was done by entering the data in an Excel sheet, and the survival assessment done with the help of Sigma plot software version 14 (Systat Software, Inc. San Jose, CA 95131 USA). Kaplan-Meier survival curve was prepared for the analysis.
3
Comprehensive Statistical Analysis Protocol
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All results were expressed as mean ± SEM. All data were analyzed for normality by SharpiroWilk normality test and Brown-Forsythe equal variance prior to parametric analysis or non-parametric analysis. Data was analyzed by Student t-test or ANOVA if data was parametric and if data was non-parametric Mann–Whitney rank sum test, Chi square, or Fisher exact test was used to analyze data. Chi square analysis was used for AAA incidence. Experiments containing small size have limitations in drawing conclusions. Statistical analysis was performed using SigmaPlot software version 14 (Systat Software Inc). Box plots are shown with median (solid line) and mean (dashed line). A P < 0.05 was considered significant.
4
Quantifying Vascular Permeability and NET Formation
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All statistical analyses were performed with SigmaPlot software, version 14 (Systat Software Inc., CA, USA). Using mean differences and corresponding SDs of albumin permeability and VE-cadherin expression in HPAECs and parameters of NET formation including cf-DNA, MPO-DNA, and NE-DNA, we calculated that a sample size of 5-7 was needed to find differences with a two-sided confidence interval of 0.95 and a desired power of 0.8. Continuous variables are shown as the mean with SD. For group comparison, a one-way analysis of variance was used, followed by a post hoc Holm-Sidak test.
5
Predictive Modeling in Clinical Research
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PFS was estimated by the Kaplan–Meier method using SPSS Statistics software version 22 (IBM Japan Ltd, Tokyo, Japan) and Stata version 14 (StataCorp LP, College Station, TX). Receiver operating characteristic (ROC) curves were generated using SigmaPlot software version 14 (Systat Software, San Jose, CA). The P value was defined as the probability that the observed C‐statistic would be 0.5 (null hypothesis: area = 0.5). The P < 0.05 would, thus, indicate that the observed C‐statistic differed significantly from 0.5. Optimal cutoff values were determined based on a pre‐test probability of 0.5 and cost ratio of 1.0. Any P < 0.05 was considered indicative of a statistically significant outcome.
6
RNA-seq Differential Expression Analysis
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Statistically significant differential expression for the RNA-sequencing dataset was defined in this study as a log fold change absolute value greater than 0.5 and multiple test-adjusted p value less than 0.05. The STAR/featureCounts/DESeq2 DE output and the Galaxy TopHat2/Cuffdiff DE output were examined to determine which genes fell into this category. The genes that were common to both approaches (n = 63 DEGs) were subjected to further exploratory data analysis and visualization, including volcano plots and heatmap clustering. The volcano plot and heatmap clustering were performed in R Statistical Programming Language. The union of the DEGs from both analyses (n = 259 DEGs) was used for Gene Ontology overrepresentation analyses and Ingenuity Pathway Analysis.
Gene expression data were analyzed using the SigmaPlot software, version 14 (Systat Software, Inc., San Jose, CA, USA) and are expressed as the mean ± SEM. Quantitative RT-qPCR expression data were analyzed using two-way ANOVA with sex and treatment as factors. Student–Newman–Keuls post-hoc tests were performed for alpha <0.05. Quality control statistics and bioinformatics used for genomic expression data are described above.
Gene expression data were analyzed using the SigmaPlot software, version 14 (Systat Software, Inc., San Jose, CA, USA) and are expressed as the mean ± SEM. Quantitative RT-qPCR expression data were analyzed using two-way ANOVA with sex and treatment as factors. Student–Newman–Keuls post-hoc tests were performed for alpha <0.05. Quality control statistics and bioinformatics used for genomic expression data are described above.
7
Split-Plot ANOVA Analysis of Variance
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We conducted the analysis of variance (ANOVA) with a split-plot design in the R version 3.5.2 using the Agricolae package version 1.2-8 to analyze the collected data. Data were analyzed separately for each year. The least significant difference (LSD) test at a 5% significance level was used to compare the treatment means. The SigmaPlot software version 14 (Systat Software, San Jose, CA) was used to make figures.
8
Statistical Analysis of Quantitative Data
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All quantitative data were analyzed for statistical significance using SigmaPlot software version 14, build 14.0.3.192 (Systat Software Inc.). Data were tested for normality and equal variance before choosing the appropriate parametric or non‐parametric test, respectively.
9
Comparative Statistical Analysis of Experimental Data
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All data are reported as mean values ± SEM and were compared using either the Student’s t-test or a one-way ANOVA in SigmaPlot software Version 14.0 (Systat Software, San Jose, CA, USA). Differences with a p value of <0.05 were considered statistically significant.
10
COVID-19 Biomarker Diagnostic Protocol
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All statistical analyses were performed with SigmaPlot software version 14.0 (Systat Software, San Jose, CA, USA). Data normality was assessed by Kolmogorov-Smirnov with Lilliefors correction. Data are shown as mean or median ± range, as appropriate.
For comparisons between two groups, Student's t-test or Mann-Whitney rank sum test with Yates correction were used. For comparisons of three groups, one way-analysis or variance (ANOVA) with Student-Newman-Keuls (SNK) post-hoc or Kruskal-Wallis test followed by a Dunn's post-hoc test were used. Results were considered significant at a p value <0.05. For correlations of two variables, Spearman Rank Order Correlation was used. The r values for each of the correlations were plotted in a bubble chart generated with Microsoft Excel 2019 (https://office.microsoft.com/excel). Receiver operating characteristic (ROC) curves were generated to find the accuracy of biomarkers to distinguish infected individuals and the severity of COVID19.
For comparisons between two groups, Student's t-test or Mann-Whitney rank sum test with Yates correction were used. For comparisons of three groups, one way-analysis or variance (ANOVA) with Student-Newman-Keuls (SNK) post-hoc or Kruskal-Wallis test followed by a Dunn's post-hoc test were used. Results were considered significant at a p value <0.05. For correlations of two variables, Spearman Rank Order Correlation was used. The r values for each of the correlations were plotted in a bubble chart generated with Microsoft Excel 2019 (https://office.microsoft.com/excel). Receiver operating characteristic (ROC) curves were generated to find the accuracy of biomarkers to distinguish infected individuals and the severity of COVID19.
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