Sas software 9

Manufactured by SAS Institute

Sourced in United States

SAS software 9.4 is a comprehensive data analysis and reporting tool. It provides a wide range of statistical and analytical capabilities to help organizations make data-driven decisions. The software enables users to access, manage, analyze, and report on data from various sources.

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Lab products found in correlation

Sas enterprise guide 7, by SAS Institute (9 mentions) Sas 9, by SAS Institute (6 mentions) Prism 9, by GraphPad (3 mentions) Prism 8, by GraphPad (3 mentions) Spss version 23, by IBM (2 mentions) Jmp pro 15, by SAS Institute (2 mentions) Prism 5, by GraphPad (2 mentions) Sas software, by SAS Institute (2 mentions) Excel 2013, by SAS Institute (2 mentions) Gcms qp2010 ultra system, by Shimadzu (1 mentions) Stata statistical software release 15, by StataCorp (1 mentions) 6.2 prism, by GraphPad (1 mentions) Spss version 25, by IBM (1 mentions) Spss statistics version 25, by SAS Institute (1 mentions) Sigmaplot 13, by Grafiti LLC (1 mentions) Sas proc mixed, by SAS Institute (1 mentions) Prism software 8, by GraphPad (1 mentions) Phoenix winnonlin, by Phoenix Pharmaceuticals (1 mentions) Phoenix winnonlin software, by Phoenix Pharmaceuticals (1 mentions) Sigmaplot v12, by Grafiti LLC (1 mentions)

366 protocols using sas software 9

Statistical Analysis of Microbial Data

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Statistical analysis of the data obtained was performed using generalized linear model (GLM) procedure of the SAS software 9.4 (SAS Institute Inc., Cary, NC, USA.). All data were analysed by the least-squares means method using the GLM procedures of SAS. Significantly different means were then further separated using Tukey HSD test. The microbial population data were checked for normality using the UNIVARIATE procedure of SAS software 9.4 (SAS Institute Inc., Cary, NC, USA.). All statistical tests were conducted at 95% confidence level.

Behan A.A., Loh T.C., Fakurazi S., Kaka U., Kaka A, & Samsudin A.A. (2019). Effects of Supplementation of Rumen Protected Fats on Rumen Ecology and Digestibility of Nutrients in Sheep. Animals : an Open Access Journal from MDPI, 9(7), 400.

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Genetic Associations Analysis Protocol

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On each SNP site, the χ 2 test for deviation from Hardy-Weinberg equilibrium, along with expected and observed heterozygosity, polymorphism information content (PIC) and linkage disequilibrium (LD) were calculated using the algorithms provided for by the SAS software 9.4 (ALLELE procedure).
The associations between SNPs and traits were analysed using the general linear model (GLM procedure of SAS software 9.4):
where Y ik is the phenotype, μ was the overall population mean for the trait, G i is the fixed effect of the genotype at the SNP (i = AA, AB, BB) and e ik is the random error. Significance of the SNP and least-squares means were determined using the Student's t-test in GLM procedure.
The association between haplotypes and traits were analysed by a GLM on each haplotype by inserting the number of copies as a fixed: Y ik = μ + H i + e ik , where Y ik is the phenotype, μ is the overall population mean for the trait, H i is the fixed effect of the haplotype [i = 0, individuals with no haplotype; 1, individuals with one copy only (heterozygotes); 2, individuals homozygous for that haplotype] and e ik is the random error. Significance of the haplotype and least-squares means were determined using the Student's t-test in GLM procedure.

Napolitano F., Grandoni F., De Matteis G., Degano L., Vicario D, & Buttazzoni L. (2021). Novel SNPs and haplotypes identified in the CD4 gene and their influence on deregressed MACE EBV indexes of milk-related traits in Simmental breed. The Journal of dairy research, 88(4).

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Factorial Analysis of Growth Performance

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All the data were tested for normality using the Shapiro–Wilk test. Growth performance data (gain, gain efficiency, and dietary energetics), DM intake, and carcass data were analyzed as a randomized complete block design with a 2 × 2 factorial arrangement of treatments, with the pen as the experimental unit, using the MIXED procedures of SAS software 9.3 [29 ], with treatment and block as fixed effects and the experimental unit within treatment as a random effect. Visceral organ mass and gene expression data were analyzed as a randomized complete block design with a 2 × 2 factorial arrangement of treatments, using the MIXED procedure of SAS software 9.3 [29 ] with treatment and pen as fixed effects and treatment–pen interaction as random effect. In all cases, contrasts are considered significant when the p value < 0.05.

Estrada-Angulo A., Verdugo-Insúa M., Escobedo-Gallegos L.D., Castro-Pérez B.I., Urías-Estrada J.D., Ponce-Barraza E., Mendoza-Cortez D., Ríos-Rincón F.G., Monge-Navarro F., Barreras A., Zinn R.A., Corona-Gochi L, & Plascencia A. (2024). Influences of a Supplemental Blend of Essential Oils Plus 25-Hydroxy-Vit-D3 and Zilpaterol Hydrochloride (β2 Agonist) on Growth Performance and Carcass Measures of Feedlot Lambs Finished under Conditions of High Ambient Temperature. Animals : an Open Access Journal from MDPI, 14(9), 1391.

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Statistical Analysis of Oxidative Stress

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Statistical analyses were performed using SAS software 9.1 (SAS Institute, Inc.). Sample size was based on a 0.90 power to detect the same difference that we observed in the least sensitive measure of oxidative stress in our previous study using a two-tailed α-level of 0.05 [18 (link)]. Baseline characteristics with normal distribution were compared between cases and controls using a paired t-test for continuous variables and Chi-square/Fisher exact test for categorical variables. Nonparametric tests were used for variables with skewed distribution. Mean (and median, where appropriate) levels of oxidative stress and ACE markers in cases and controls were compared using t-test for normally distributed variables and NPAR1WAY procedure (SAS software 9.1) for variables with skewed distribution. All variables significant on univariate analysis were entered into multiple logistic regression models to calculate adjusted odds ratios.

Negi S.I., Jeong E.M., Shukrullah I., Veleder E., Jones D.P., Fan T.H., Varadarajan S., Danilov S.M., Fukai T, & Dudley SC J.r. (2015). Renin-Angiotensin Activation and Oxidative Stress in Early Heart Failure with Preserved Ejection Fraction. BioMed Research International, 2015, 825027.

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Survival Analysis of Biomarkers in Cancer

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For clinical data, as well as our in silico data analysis, patients’ overall survival was used as the endpoint for survival analysis. A log-rank test was applied to determine the significance of survival differences between subgroups. The cut-off points that we found for CA and HIF1-α were those which maximized survival differences between high- and low-risk subgroups. The range of CA value was 4.85–71.31 and 23 was used as the cut-off point as it resulted in the minimization of the log-rank p-value. The test of group mean differences shown in box-whisker plots is based on the Mann–Whitney U test. In cases with more than two groups, the differences were evaluated by the Kruskal–Wallis test. Statistical analyses were performed using SAS software 9.4 (SAS Institute Inc., Cary, NC). Details of the survival model used in our in silico analysis are included in the Supplementary Materials.

Mittal K., Choi D.H., Wei G., Kaur J., Klimov S., Arora K., Griffith C.C., Kumar M., Imhansi-Jacob P., Melton B.D., Bhimji-Pattni S., Osan R.M., Rida P., Golusinski P, & Aneja R. (2020). Hypoxia-Induced Centrosome Amplification Underlies Aggressive Disease Course in HPV-Negative Oropharyngeal Squamous Cell Carcinomas. Cancers, 12(2), 517.

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Adducts and LINE-1 Methylation Analysis

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We used the χ² test for categorical variables and Student’s t-test for continuous variables to assess the difference in selected characteristics between exposure groups (detectable vs undetectable adducts). We calculated the Spearman rank correlation coefficients (r_s) to determine the correlation of adducts and plasma LINE-1 methylation. All analyses were performed with SAS software 9.4 (SAS Institute, Cary, NC).

Wu H.C., Shen J., Siegel A, & Santella R.M. (2021). Environmental exposure and clinical correlates of hepatocellular carcinoma in New York City: a case only study. Cancer causes & control : CCC, 33(1), 153-159.

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Hydrophilic Metabolite Extraction and GC-MS Analysis

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The hydrophilic metabolites from the cells were extracted by modifying a previously described (Kim et al. 2017 (link)). The sample was transferred into auto-sampler vials and analyzed by gas chromatography-mass spectrometry (GC-MS, a GCMS-QP2010 Ultra system, Shimadzu, Kyoto, Japan). GC-MS analysis was performed as previously described (Song et al. 2021 (link)). Pearson's correlation analysis was carried out using SAS software 9.4 (SAS Institute, San Diego, CA, USA) and then visualized with Hierarchical Clustering Analysis (HCA) using Multi-Experiment Viewer version 4.9.0 (MeV; https://webmev.tm4.org).

Kuk M.U., Ga Y.J., Kim Y.J., Park J.Y., Song E.S., Lee H., Lee Y.H., Ko G., Kim J.K., Yeh J.Y., Kwon H.W., Byun Y, & Park J.T. (2022). Metabolic reprogramming as a novel therapeutic target for Coxsackievirus B3. Animal Cells and Systems, 26(6), 275-282.

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Multivariate Statistical Analysis of Meat Metabolites

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The experiment followed a completely randomized design. Data obtained for fatty acid parameters were analyzed using GLM procedures of SAS software (9.4) (SAS Institute Inc., Cary, NC, USA). Means were compared using the Tukey’s Honest Significant Difference (HSD) test and the level of significance was set at p < 0.05. The meat quality data were analyzed using a factorial design 4 × 3 (diets × postmortem aging periods) employed for data (pH, drip loss, cooking loss, color and shear force values). Univariate statistical analysis of NMR data was performed using GraphPad Prism version 5.0. One-way analysis of variance (ANOVA) was applied to evaluate the significance level of differences between metabolites from different meat groups. Statistical differences were considered significant at a level of p ≤ 0.05. Tukey’s multiple comparison test was used to study the statistical difference between means of four meat groups. The ASCII formatted files were imported to SIMCA-P+ version 13.0 32-bits (Umetrics AB, Umeå, Sweden) and subjected to multivariate data analysis (MvDA). The data was Pareto scaled. In Pareto scaling, the square root is used as a scaling factor and each variable is divided by the square root of standard deviation [37 ]. Usually, Pareto scaling is applied to data with a large dynamic range [38 (link)].

Behan A.A., Akhtar M.T., Loh T.C., Fakurazi S., Kaka U., Muhamad A, & Samsudin A.A. (2021). Meat Quality, Fatty Acid Content and NMR Metabolic Profile of Dorper Sheep Supplemented with Bypass Fats. Foods, 10(5), 1133.

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Bland-Altman Plot for Biometric Agreement

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The Bland–Altman plot was used as it compares two measurements of the same variable. The X-axis represents the mean of two measurements, and the Y-axis represents the difference between the two measurements [25 (link)]. Plotting difference versus mean aids in discovery of possible relationships between measurement inaccuracy and real value. Left and right eyes were grouped separately. Agreement between biometrics was examined by eye (left vs. right) using Bland–Altman plots with 2 (red) and 3 (green) standard deviation reference bounds using SAS Software 9.4 (Cary, NC, USA). If points are scattered above and below zero (mean black line) then there is no persistent bias toward one imaging modality over the other. In contrast, if values generally increase or decrease then there is evidence that the measures are not concordant. Bland–Altman analyses provide evidence for agreement and thus cannot be used to determine if one method is better than another (i.e., because there is no gold standard).

DuPont M., Hunsicker J., Shirley S., Warriner W., Rowland A., Taylor R., DuPont M J.r., Lagatuz M., Yilmaz T., Foderaro A., Lahm T., Ventetuolo C.E, & Grant M.B. (2022). Comparison of Retinal Imaging Techniques in Individuals with Pulmonary Artery Hypertension Using Vessel Generation Analysis. Life, 12(12), 1985.

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Fluid Overload and Acute Kidney Injury Outcomes

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Descriptive statistics for continuous variables were summarized as mean and standard deviation or median and inter quartile range when determined skewed by a Shapiro-Wilks test. Categorical variables were summarized as counts and percentages. Summary statistics are presented in tabular form. Differences in ordinal outcome such as length of stay and PRISM-III score were compared with Unpaired T-Tests or Wilcoxon Rank Sum tests, and dichotomous outcomes by Chi-Squared or Fisher’s Exact tests. Logistic regression models assessed bivariate associations between dichotomous outcomes and fluid overload as a continuous variable, Spearman’s correlation assessed bivariate associations between ordinal variables. Multiple general linear regression models were used to derive estimated associations between AKI, FO and the outcomes of duration of mechanical ventilation and length of stay (hospital and ICU). The regression models incorporated the model terms of age, fluid overload, AKI, and PRISM-III scores – to a priori limit the effects of collinearity but also to use practical, highest relevance of importance. Pairwise differences between the four FO/AKI phenotypes were analyzed yielding model estimated means. Statistical significance level was set at alpha < 0.05 level. The data analysis was performed using SAS software 9.4, Cary, NC, USA, and JMP. Copyright © 2002–2012 SAS Institute Inc.

Gist K.M., Selewski D.T., Brinton J., Menon S., Goldstein S.L, & Basu R.K. (2020). Assessment of the Independent and Synergistic Effects of Fluid Overload and Acute Kidney Injury on Outcomes of Critically Ill Children. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 21(2), 170-177.

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