Statistical analysis was performed using SigmaPlot (SigmaPlot.com), or R statistical environment (r-project.org), and the ggplot2 module (ggplot2.org). Statistical significance was calculated using a two-tailed ANOVA on data found to be normal using a Shapiro-Wilk test and demonstrating constant variance. In case of unequal variances, the non-parametric Spearman rank order correlation test was used.
R statistical environment
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The R statistical environment is a free and 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 others.
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45 protocols using r statistical environment
1
Statistical Analysis for Biological Data
2
Longitudinal Analysis of Brain Characteristics
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Normal-model based analysis of variance (ANOVA) was performed to investigate the differences between the approaches with and without lesion filling. Normality assumption was assessed by Shapiro-Wilk test
[34 (link)] and homoscedasticity was assessed by the robust Brown-Forsythe version of the Levene’s test
[35 (link)]. Non-parametric Wilcoxon signed rank test was used if data did not meet the assumptions of the linear model. The results (WM lesion classification, tissue volumes, tissues intensity and whole brain and ROI mean CTh) were compared both within each session and over the time points. In order to reduce the risk of type I errors the ROI results were corrected for multiple comparisons by using the False Discovery Rate (FDR) approach set at alpha levels of 0.05. Moreover, the vertex-wise longitudinal analysis was performed using a linear mixed model (http://www.bic.mni.mcgill.ca/ServicesSoftware/StatisticalAnalysesUsingR ) including age at baseline, gender and time points as fixed-effects and patients as random-effect. All statistical analyses were performed using the R statistical environment (http://www.r-project.org ).
[34 (link)] and homoscedasticity was assessed by the robust Brown-Forsythe version of the Levene’s test
[35 (link)]. Non-parametric Wilcoxon signed rank test was used if data did not meet the assumptions of the linear model. The results (WM lesion classification, tissue volumes, tissues intensity and whole brain and ROI mean CTh) were compared both within each session and over the time points. In order to reduce the risk of type I errors the ROI results were corrected for multiple comparisons by using the False Discovery Rate (FDR) approach set at alpha levels of 0.05. Moreover, the vertex-wise longitudinal analysis was performed using a linear mixed model (
3
Robust Gene Expression Profiling
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ROC analysis was performed in the R statistical environment (http://www.r-project.org ) using the ROC Bioconductor library (http://www.bioconductor.org ). Statistical significance was set at p<0.05. The two cohorts comprising of 50 and 38 patients were processed separately to avoid batch effects, and only genes resulting in significant correlation in each dataset were considered significant. Mapping between the platforms was performed using the annotation tables of Affymetrix (http://www.affymetrix.com ). The final ranking of the genes was performed by computing the average AUC across the two platforms.
4
Muscle Contraction Analysis via R
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The “R” statistical environment (version 3.4.2, www.r-project.org ) was used for all statistical analyses. A linear-mixed effects model with allowances for heterogeneity of variance according to ‘contraction state’ were performed. Then repeated-measures analysis of variance examined for differences between ‘contraction state’. Then a priori T-tests were performed comparing each contraction state to the first ‘rest’ state. To assess regional differences in muscle contraction, data were converted to percentage change compared to the baseline ‘rest’ condition. Then similar linear-mixed effects models were used to, for each contraction state, compare differences in the percentage change of each parameter between the upper, middle and lower portions of the muscles. An alpha-level of 0.05 was taken for statistical significance. To control for potential type I errors, p-values were adjusted via the false discovery rate method19 . Adding age, body mass and sex covariates to the models does not change the findings of the study.
5
RCCS and Radiation Impacts on Cell
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RCCS experiments were analyzed using three-way analysis of variance (ANOVA) with repeated measures of each combination of the RCCS and OSM induction (corresponding to a culture vessel) at each level of time. The RCCS with radiation experiments were analyzed using four-way ANOVA with repeated measures of each combination of radiation, RCCS, and OSM induction (corresponding to a culture vessel) at each level of time. Multiple comparisons were conducted with Tukey's HSD test post hoc. Each response variable was treated separately. For all comparisons, α = 0.05. In figures, bars and asterisks (*) indicate p < 0.05 for the indicated main effect. Any interactions identified in the test had a p < 0.05. Calculations were performed in the R statistical environment (R Project).
6
RNA-Seq Data Analysis Pipeline
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The Perl script was used to trim the original data (raw data) obtained from the sequencing system containing the reads of contaminated adapters, the low-quality reads, and the reads containing poly-Ns. The clean data were filtered statistically for the quality and data quantity, including Q30 statistics, data quantity statistics, base content statistics, etc. Reference gene and genome annotation files were downloaded from the UCSC (http://hgdownload.soe.ucsc.edu/goldenPath/galGal4 ) to build the reference genome library using ‘Bowtie2’ (v2.2.3), and then the clean data were mapped to the reference genome by ‘TopHat’ (v2.0.12). Quantification scores for all chicken genes and Reads per Kilobase Million Mapped Reads (RPKM) values were calculated using ‘Cufflinks’ (v2.0.2), which correct the transcript length and total numbers of mapped reads from the library to compensate different read depths for different samples. In addition, ‘HTSeq’ (v0.6.0) was run to calculate read counts for each gene. All data were analyzed using R Statistical Environment (http://www.r-project.org/ ), accompanied with an additional package of ‘gplots’.
7
Intervertebral Disc T2 Mapping
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In each IVD and for each of the five predefined subregions the corresponding T2 was calculated using custom code written in the R statistical environment (version 3.4.2, www.r-project.org ) from the decay of signal intensity measured at 16 different echo times (Fig 2 ). The primary analysis focussed on whole IVD T2 (T2 values of the whole IVD averaged across all anatomical slices) and T2 in the nucleus (T2 in the central subregion of the five IVD subregions in the third anatomical slice). Secondary analysis considered the reliability in the five IVD subregions from the anterior to posterior annulus, averaged across all images.
8
Comparative Metabolite Analysis in Serum and Plasma
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The data were analyzed statistically using the Wilcoxon matched-pairs signed-rank tests for comparison of serum and plasma levels for each metabolite for the same group of subjects, and the Mann-Whitney U-test test for comparison of metabolite levels among the four groups. Statistical analyses were carried out using R statistical environment (http://r-project.org/ ) software. Differences with p values of less than 0.05 were considered statistically significant.
9
Comparative Gene Expression Analysis
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Data were analyzed using R Statistical Environment (https://www.r-project.org ) with Bioconductor R software for principal component analysis and impute package for missing values. Normfinder (https://moma.dk/normfinder-software ) software was used for evaluating reference genes used for normalization (25 (link)).
For comparative gene analysis, we used the linear modeling framework from the R Stats Package with the (lm() function using normalized CT values (delta CT). Independent samples and paired samples were taken into account. Correction for sex and endoscopic remission status was performed. P values were adjusted for multiple testing using the Benjamini Hochberg method, with a significant adjusted P < 0.05 set as threshold (26 ). Comparison of gene expression between groups was given as log2 fold change. Quantification of immunostaining was performed with t tests for independent groups and a paired t test when comparing acute and remission disease, all after control of normality with IBM SPSS Statistics version 25.0 (Armonk, NY). Plot and bar charts were visualized with GraphPad prism v. 7 (La, Jolla, CA).
For comparative gene analysis, we used the linear modeling framework from the R Stats Package with the (lm() function using normalized CT values (delta CT). Independent samples and paired samples were taken into account. Correction for sex and endoscopic remission status was performed. P values were adjusted for multiple testing using the Benjamini Hochberg method, with a significant adjusted P < 0.05 set as threshold (26 ). Comparison of gene expression between groups was given as log2 fold change. Quantification of immunostaining was performed with t tests for independent groups and a paired t test when comparing acute and remission disease, all after control of normality with IBM SPSS Statistics version 25.0 (Armonk, NY). Plot and bar charts were visualized with GraphPad prism v. 7 (La, Jolla, CA).
10
Dvl Knockout Mouse Behavioral Analysis
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