R project software

Manufactured by R Project for Statistical Computing

Sourced in United States

R is an open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more. R is widely used in academia and industry for data analysis, visualization, and statistical modeling.

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Lab products found in correlation

Sas statistical software, by SAS Institute (1 mentions) Spss 17.0 software for windows, by IBM (1 mentions) Sigmaplot, by Merck Group (1 mentions) Sas software package, by SAS Institute (1 mentions)

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R project statistical software R project software version 3.0.2 R Project software version 4.0.3 R Project software (v4.1.1; R-project R software

40 protocols using r project software

Morphological and Physiological Characterization

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Fifteen independent replicates were analyzed for morphological parameters. From the obtained data, outliers values were removed. To identify the most relevant physiological and morphological parameters, an Analysis of Principal Component (PCA) was performed using R project software.
On the other hand, all spectrophotometric measures were performed by using three biological replicates, and two technical replicates, for each analysis. At the same time, three biological replicates were used for qPCR analysis, considering 3 technical replicates each one. Data from enzyme activities and gene expression were analyzed using One-way ANOVA test from R project software. Tukey post-hoc comparison was used when significant differences (p < 0.05) were found in each analysis.

Fortt J., González M., Morales P., Araya N., Remonsellez F., Peña T.C.d.l., Ostria‐Gallardo E., & Stoll A. (2022). Bacterial Modulation of the Plant Ethylene Signaling Pathway Improves Tolerance to Salt Stress in Lettuce (Lactuca sativa L.). Frontiers in Sustainable Food Systems, 6.

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Sinus Graft Bone Density Analysis

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Alveolar bone height was assessed on preoperative and postoperative CT scans. Radiographic bone density was computed in Hounsfield Units (HU) in a distance of 1 mm apical to the original sinus floor up to the top of the sinus graft using the Easy Vision Workstation (Philips, Eindhoven, The Netherlands). Association of bone density to patient age and gender was tested using Spearman correlation coefficient and Wilcoxon signed rank test with continuity correction. Friedman rank sum including post hoc Nemenyi testing was performed to determine differences in bone density with regards to the distance to the original sinus floor at a significance level of p = 0.05. To determine a critical threshold, multiple Wilcoxon signed rank tests were performed at a Bonferroni-corrected significance level of p = 0.05/16 = 0.0032 to account for multiple testing. All analyses were performed using R-project software (R Foundation for Statistical Computing, Vienna, Austria).

Pommer B., Unger E., Busenlechner D., Haas R., Mailath-Pokorny G., Fürhauser R, & Watzek G. (2015). Graft Remodeling following Transcrestal Sinus Floor Elevation via the Gel-Pressure Technique (GPT) and Pasteous Nano-Crystalline Hydroxyapatite Bone Substitute. Materials, 8(6), 3210-3220.

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Fracture Load Statistical Analysis

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The average and standard deviations of the fracture load values were calculated using the R Project software (R Foundation for Statistical Computing, Vienna, Austria), and one-way ANOVA was performed to evaluate the differences between the experimental groups. As a post hoc test, the Bonferroni test was performed, and the statistical significance was set to 0.05.

Kim S.H., Yeo M.Y., Choi S.Y, & Park E.J. (2022). Fracture Resistance of Monolithic Zirconia Crowns Depending on Different Marginal Thicknesses. Materials, 15(14), 4861.

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Quantitative PET/MR Imaging to Predict Breast Cancer Response

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The absolute values of all quantitative [18F]FDG PET/MR imaging variables at each time point, as well as the percentage decrease halfway through and after NCT, were compared between patients with a primary tumour and axillary pCR and RD separately by means of the Mann–Whitney U test. For all the significant quantitative variables, receiver operating characteristic (ROC) curves were generated to determine the cut-off value with optimal sensitivity and specificity. Diagnostic performance, expressed as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) with 95% confidence intervals (95% CIs), was calculated for each significant quantitative imaging variable at the optimal cut-off, both separately and combined. Lastly, the diagnostic performance of qualitative [18F]FDG PET, MRI, and [18F]FDG PET/MRI after NCT were calculated. For all analyses, the detection of residual disease via imaging or pathology analysis was considered as positive and pCR via imaging or pathology analysis was considered as negative. A two-sided p-value of <0.05 was considered to be statistically significant. R project software (version 4.2.0, R Foundation for Statistical Computing, Vienna, Austria) was used to perform the statistical analyses.

de Mooij C.M., van Nijnatten T.J., Goorts B., Kooreman L.F., Raymakers I.W., van Meijl S.P., de Boer M., Keymeulen K.B., Wildberger J.E., Mottaghy F.M., Lobbes M.B, & Smidt M.L. (2023). Prediction of Primary Tumour and Axillary Lymph Node Response to Neoadjuvant Chemo(Targeted) Therapy with Dedicated Breast [18F]FDG PET/MRI in Breast Cancer. Cancers, 15(2), 401.

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Intraventricular Chemotherapy CSF Profiles

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CSF profiles of the pretreatment lumbar, pretreatment ventricular, and posttreatment ventricular samples were compared using the Wilcoxon signed-rank test. Changes in CSF profiles were defined as a decrease, no change, or increase by using the difference between the pretreatment ventricular and posttreatment ventricular CSF profiles from the same compartment. The association between changes in CSF profiles and the treatment response in terms of ICP control and cytology response after intraventricular chemotherapy was evaluated using the chi-squared test or Fisher’s exact test.
The association of clinical variables, CSF profiles and their changes on OS were evaluated using the Cox proportional hazards model. The variables with marginal effects based on the univariable analysis (p≤0.2) were included in the multivariable analysis, and the final model was determined using the backward selection method with an elimination criterion of a p-value >0.05. The Kaplan-Meier curves with log-rank test p-values are presented for the significant variables in the final model. We considered a p-value of less than 0.05 to be statistically significant. All statistical analyses were performed using SAS ver. 9.4 (SAS Institute, Inc., Cary, NC, USA) and R project software (version 3.6.2; The R Foundation for Statistical Computing).

Kwon J.W., Shim Y., Gwak H.S., Park E.Y., Joo J., Yoo H, & Shin S.H. (2021). Cerebrospinal Fluid Profiles and Their Changes after Intraventricular Chemotherapy as Prognostic or Predictive Markers for Patients with Leptomeningeal Carcinomatosis. Journal of Korean Neurosurgical Society, 64(4), 631-643.

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Random Forest Ensemble for Data Analysis

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The statistical analyses were performed with R project software (R foundation, Vienna, Austria). An RF analysis was performed using the Scikit‐learn open‐source ML library, version 0.21.2. In this study, we used an RF algorithm, which is a decision tree‐based ensemble learning method for the classification, regression, and clustering of the data.²² The RF analysis was composed of three steps: (1) missing values imputation, (2) classification model building, and (3) feature selection.

Watanabe E., Noyama S., Kiyono K., Inoue H., Atarashi H., Okumura K., Yamashita T., Lip G.Y., Kodani E, & Origasa H. (2021). Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J‐RHYTHM registry. Clinical Cardiology, 44(9), 1305-1315.

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Evaluating Motion Correction and Imaging Metrics

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For the MRI sequences acquired with and without prospective motion correction, the frame-to-frame motion distribution variances were compared with an F test. The average edge strength ratio (an image sharpness measure) was calculated (20) following brain masking with the FSL Brain Extraction Tool (FMRIB Analysis Group). A paired t test was performed to evaluate if improved sharpness was achieved (empirical evidence suggested that the data were similar to a normal distribution). For the healthy young participants, a repeated-measures analysis of variance was performed to examine if the T2* magnitude, T2* phase, and T1 values were differentially dependent on the cerebellar cortical depth. Similarly, for the healthy young participants, a repeated-measures analysis of variance was used to examine if downsampling the initial data affected the derived cortical measures (surface area, thickness, and WM and GM volume). Significant main effects were investigated pairwise with paired t tests after Bonferroni adjustment. All tests were performed by N.P. using the R Project software (version 3.5.1, The R Foundation). Statistically significant difference was established at P < .05.

Priovoulos N., Andersen M., Dumoulin S.O., Boer V.O, & van der Zwaag W. (2023). High-Resolution Motion-corrected 7.0-T MRI to Derive Morphologic Measures from the Human Cerebellum in Vivo. Radiology, 307(2).

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Statistical Analysis of Biological Data

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Statistical analysis was performed using the Mann-Whitney U test or one-way ANOVA, and data are presented as the mean ± SEM. The graphs were created with R project software (version 4.0.5). The values and error bars presented in figures represent the means and standard errors of biological replicates.

Guo Y., Zhang Q., Hu X., Pang C., Li J, & Huang J. (2021). Mating Stimulates the Immune Response and Sperm Storage-Related Genes Expression in Spermathecae of Bumblebee (Bombus terrestris) Queen. Frontiers in Genetics, 12, 795669.

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Assessing Socioeconomic Factors and Food Security

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The descriptive statistics of all of variables of interest across each district in two states were tabulated to understand the variation in our variables across study sites. SPSS was used to create all descriptive tables. We ran linear regressions for each of the six districts across two states using R Project Software to identify the associations between agricultural (e.g. crop diversity, landholding size, farm income and crops sold) and socioeconomic (e.g. family income, family education, distance to food markets and consumption of domestically produced milk) factors, and FCS. All continuous independent variables were normalized by the mean and standard deviation to make coefficient values comparable across all independent variables. We calculated variable inflation factors (VIFs) for each regression and found no evidence of multi-collinearity (VIF < 1.2) among independent variables used. Further, to reduce the effect of location on our results, we included block fixed effects in all regressions.

Singh S., Jones A.D, & Jain M. (2020). Regional differences in agricultural and socioeconomic factors associated with farmer household dietary diversity in India. PLoS ONE, 15(4), e0231107.

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Antimicrobial Susceptibility Profiling

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MIC, MBC, and killing quotient (KQ) distributions were analyzed according to the mucoid status, the multidrug resistance, and the ability to produce biofilm. Differences between groups were assessed by Mann-Whitney’s test, with the exception of the evaluation depending on biofilm producer groups, assessed by the Kruskal-Wallis’ test and Spearman correlation coefficient when Kruskal-Wallis’ test was positive. Data were analyzed at the total population level and at a restricted level defined by one isolate type per patient to avoid biased results due to possible clonal relatedness of isolates in a patient because strains have not been genotyped in this study. Statistical analysis of results was done with R project software (http://www.r-project.org). A P value ≤0.05 was considered to reflect significance.

Michon A.L., Jumas-Bilak E., Chiron R., Lamy B, & Marchandin H. (2014). Advances toward the Elucidation of Hypertonic Saline Effects on Pseudomonas aeruginosa from Cystic Fibrosis Patients. PLoS ONE, 9(2), e90164.

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