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R a language and environment for statistical computing version 3

R: A Language and Environment for Statistical Computing, version 3.2.0, is a free software environment for statistical computing and graphics. It is a widely used programming language for data analysis, visualization, and statistical modeling.

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Lab products found in correlation

2 protocols using r a language and environment for statistical computing version 3

1

miRNA Expression Data Analysis

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GenEx Professional software (MultiD Analyses, Sweden) was used for processing the raw miRNA expression data. Other statistical analyses were conducted with R: A Language and Environment for Statistical Computing, version 3.2.0 (R Foundation for Statistical Computing, Vienna, Austria). Results are presented as mean and standard deviation when normally distributed or median and interquartile range when values were non-normally distributed. Student’s t-tests were performed to investigate differences between normally distributed continuous variables and Mann-Whitney U tests for non-normally distributed continuous variables. Correlation analyses were performed between circulating miRNA levels and parameters of cardiac function. Pearson product moment correlation was performed when data was normally distributed, while Spearman correlation analyses were used for non-normally distributed data. Unless otherwise stated, P-values of ≤0.05 were considered significant.
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2

miRNA Expression Analysis Protocol

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GenEx Professional software (MultiD Analyses, Sweden) was used for the raw miRNA expression data. Other statistical analyses were conducted with R: A Language and Environment for Statistical Computing, version 3.2.0 (R Foundation for Statistical Computing, Vienna, Austria). Normally distributed variables were depicted as mean ± standard deviation and non-normally distributed variables were presented as median with the interquartile range. Differences between groups were determined using t tests for normally distributed continuous variables and Mann–Whitney U tests for non-normally distributed continuous variables. For binomially and categorical variables, the Chi-square test was used. Linear trend tests were used for miRNA and biomarker levels across groups and quartiles. To examine the predictive value of miRNAs for various endpoints uni- and multivariable Cox proportional hazards regression analyses were performed. P values of <0.05 were considered significant.
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