Group differences in viral loads in oropharyngeal and cloacal swabs were identified using linear mixed-effects model followed by a pairwise comparison using Tukey’s test in R statistical software (R studio version 1.0.153, Boston, MA, USA). For identifying differences in IFN-γ concentrations, the one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparisons was used. A one-tailed t-test was used to identify differences among two groups. Data in graphs are shown in the original scale of measurements. However, due to non-normality and inability to satisfy model assumptions of some datasets, log transformation was applied to these prior to analysis. GraphPad Prism Software 5 (La Jolla, CA, USA) and R statistical software (R studio version 1.0.153, Boston, MA, USA) was used to perform model statistics.
R statistical software
Manufactured by Posit
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, and is highly extensible.
Automatically generated - may contain errors
Lab products found in correlation
Prism 7, by GraphPad (2 mentions)
Rstudio integrated development environment, by Posit (2 mentions)
Sas 9, by SAS Institute (2 mentions)
Ingenuity pathway analysis (ipa), by Qiagen (1 mentions)
Spss version, by IBM (1 mentions)
Sigmaplot 15, by Merck Group (1 mentions)
Rstudio ide, by Posit (1 mentions)
56 protocols using r statistical software
1
Viral Load and Cytokine Quantification
2
Assessing Stability and Proportion Errors in Ecological Datasets
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Following the use of the ‘broken windows’ algorithm, we assessed if and how study parameters affected years to stability time, the overall proportion wrong, and the proportion wrong before stability was reached for each dataset. We did this by comparing the means between different groups using independent samples t-tests in base R using RStudio and R statistical software (R Studio Team, 2020 ; R Core Team, 2020 ). Using this method, we compared results from datasets that varied by life stage studied, the geographic scope of the study, sampling techniques used, and the study response variables. For example, we tested whether the mean stability time for datasets collected using dragging sampling methods was significantly different from the mean stability time for datasets collected using opportunistic sampling methods. We also used Pearson product moment correlation tests in base R using RStudio and R statistical software (R Studio Team, 2020 ; R Core Team, 2020 ). These tests were used to assess potential correlations between study length and stability time, overall proportion wrong, and the overall proportion wrong compared to proportion wrong before stability was reached. The dataset, R code, test results, and resulting figures are also provided in the Supplemental Materials .
3
Assessing Factors Affecting Ecological Stability
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Following the use of the 'broken windows' algorithm, we assessed if and how study parameters affected years to stability time, the overall proportion wrong, and the proportion wrong before stability was reached for each dataset. We did this by comparing the means between different groups using independent samples t-tests in base R using RStudio and R statistical software (RStudio Team, 2020; R Core Team, 2020) . Using this method, we compared results from datasets that varied by life stage studied, the geographic scope of the study, sampling techniques used, and the study response variables. For example, we tested whether the mean stability time for datasets collected using dragging sampling methods was significantly different from the mean stability time for datasets collected using opportunistic sampling methods. We also used Pearson product moment correlation tests in base R using RStudio and R statistical software (RStudio Team, 2020; R Core Team, 2020) . These tests were used to assess potential correlations between study length and stability time, overall proportion wrong, and the overall proportion wrong compared to proportion wrong before stability was reached. The dataset, R code, test results, and resulting figures are also provided in the Supplemental Materials.
4
Statistical Analysis of Research Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Data were collected in spreadsheets and were analyzed using R statistical software (v. 4.0.5; RStudio) and SPSS (v. 25; IBM). Differences between groups were investigated by chi-square, Fisher’s, Wilcoxon signed-rank, or unpaired two-tailed t-tests. Kaplan–Meier and log-rank tests were used for survival analysis, whereas Cox and logistic regression models were employed for investigation of the impact of independent variables on survival and outcomes. A P-value of <0.05 was considered statistically significant.
5
Random-Effects Meta-Analysis in RStudio
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
A method for random-effects meta-analysis using the statistical software RStudio version 1.2.1335 (RStudio Inc., Boston, MA, USA) was applied, with the studies being weighted by the Mantel-Haenszel method. Heterogeneity was calculated by the inconsistency index (I2) and the variance was obtained by calculating the tau2, which was estimated by the DerSimonian-Laird method. Sensitivity analysis was performed when a study judged to present a high risk of bias was included in the analysis, thus evaluating whether the inclusion of this study altered the estimates obtained. Confidence intervals of 95% were generated (95% CI) and the significance level was set at 5%. To analyze the influence of heterogeneity on the range estimates of the analyses, 95% prediction intervals (95% PI) were calculated for the estimated global effect. Analyses and the forest plot graph were generated using the R statistical software, version 1.2.1335 (RStudio Inc.).
6
Statistical Analysis of Demographic and Clinical Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Demographic and clinical data were processed in R statistical software [86 ] through RStudio (1.4.1717). Categorical data were analysed by Pearson’s chi-squared (χ2) test. The age of participants was tested using the Wilcoxon rank sum test after the Shapiro–Wilk normality test for non-normal distribution was performed. Statistical significance was considered at p < 0.05. Where appropriate, p-values were adjusted for multiple comparisons using Benjamini and Hochberg’s FDR correction [87 (link)] unless otherwise stated. The R packages used for downstream data manipulation and visualisation included tidyverse suite [88 (link)], ggplot2 [89 ], viridis [90 ], RColorBrewer [91 ], Fantaxtic [92 ], microbiome [79 ], microbiomeutilities [93 ], eulerr [94 ], ggpubr [95 ], showtext [96 (link)], magrittr [97 ], broom [98 ], Hmisc [99 ], knitr [100 ], biomformat (https://github.com/joey711/biomformat ), and scales [101 ].
7
Statistical Analysis of Immunoglobulin and Complement
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Statistical analyses were done using R statistical software (version 3.4.3) and RStudio (version 1.1.383), or Prism 7.0 (version 7.0a, GraphPad, Dan Diego, CA). Graphics were created with R (heatmap) or with Prism 7.0. Three technical replicates per sample were measured for each outcome. A linear mixed model that accounted for repeated measures within a subject was used for IgG versus C3 and IgG versus Factor H analyses and executed using the lme4 package (version 1.1–15) in R. Statistical significance for pairwise post hoc testings of the marginal means from a linear mixed model was calculated using two-sided t-test and a Tukey multiple testing correction in the emmeans (version 1.1) package in R. For Fig. 5d , statistical significance for pairwise post hoc testing of the marginal means from a linear mixed model was calculated using two-sided t-test and a Dunnett multiple testing correction.
8
Bacteria Quantification Using Log Transformation
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All tested bacteria were normalized to CFU and then log 10 transformed before analysis. The data were analyzed using one-way analysis of variance; significant differences were assumed if probabilities <0.05. Statistical analysis was performed with R statistical software (Rstudio, Boston, MA, USA).
9
R Statistical Software Protocol Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Descriptive information was calculated with R Statistical Software (version 3.6.1, RStudio, Inc., Boston, Massachusetts).
10
Statistical Analysis of Research Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
A normality test was performed on all data. Continuous variables of normal distribution are expressed as mean ± standard deviation, and categorical variables are given numbers (percentages). Independent Student's t-test, Fisher's exact test or the Chi-squared test explored the intergroup difference of characteristics. A p value <0.05 was the significance level. R Statistical Software (RStudio, Inc., Boston, MA, USA; version 1.0.153) performed statistical analyses and plotting.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!