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R code used for each data set to perform negative binomial regression, calculate overdispersion statistic, generate summary statistics, remove outliers
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Identification of errors or anomalous values, collectively considered outliers, assists in exploring data or through removing outliers improves statistical analysis. In biomechanics, outlier detection methods have explored the ‘shape’ of the entire cycles, although exploring fewer points using a ‘moving-window’ may be advantageous. Hence, the aim was to develop a moving-window method for detecting trials with outliers in intra-participant time-series data. Outliers were detected through two stages for the strides (mean 38 cycles) from treadmill running. Cycles were removed in stage 1 for one-dimensional (spatial) outliers at each time point using the median absolute deviation, and in stage 2 for two-dimensional (spatial–temporal) outliers using a moving window standard deviation. Significance levels of the t-statistic were used for scaling. Fewer cycles were removed with smaller scaling and smaller window size, requiring more stringent scaling at stage 1 (mean 3.5 cycles removed for 0.0001 scaling) than at stage 2 (mean 2.6 cycles removed for 0.01 scaling with a window size of 1). Settings in the supplied Matlab code should be customised to each data set, and outliers assessed to justify whether to retain or remove those cycles. The method is effective in identifying trials with outliers in intra-participant time series data.
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This dataset comprises records collected during the seed regeneration routine of the barley collection hosted at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) between the years 1946 and 2015. The following traits were recorded: (i) Flowering Time (FT) which corresponds to days after the 1st of January of each year for winter barley, and days after the sowing date for spring barley, (ii) Plant Height (PH) evaluated in cm, and (iii) Thousand Grain Weight (TGW) expressed in g. The dataset also compromises information respecting to accession identifiers, accession numbers, sowing date, harvest year and country as the geographic place reported by donors or collectors. The dataset and metadata are formatted using the ISA-Tab format (see subfolder /ISA-Tab). The files consist of original historical data, data derived from an outlier exclusion approach, and the computed best linear unbiased estimators (BLUEs) of accessions (see subfolder /BLUEs). The data analyses were performed using linear mixed models combined with an outlier detection approach based on rescaled median absolute deviation and Bonferroni-Holm test. The statistical approaches for processing the data were performed in R and the corresponding scripts are also included (see subfolder /R_Scripts).
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R Code for Analyses. This is a zip file containing all of the R code used to perform simulations and to analyze the breast cancer data. (ZIP 407 kb)
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**Correlation is significant at the 0.01 level (2-tailed).*Correlation is significant at the 0.05 level (2-tailed).'Correlation is significant at the 0.1 level (2-tailed).For each model, the two categories of sibling pairs are derived from Table 2. In each case, a possible fit (in bold) is indicated by the second correlation being less than the first.
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This collection contains R code for Statistical Analysis and plots related to our article "Is taurine an aging biomarker?" [ M. E. Fernandez et al., Science 388, eadl2116 (2025). DOI: 10.1126/science.adl2116 ]“Analysis” folderThis folder contains R code for all statistical analysis associated to our article "Is taurine an aging biomarker?"“Main Manuscript Plots” folderThis folder contains the plots provided in the figures of the main manuscript, together with equivalent plots (same data and results) that present different y-axis limits, allowing to better assess variability within each cohort. This is indicated in the name of the plot. E.g., the string “y0-75” in the name “1Ba_Balearic-F(5x11,2.5pt,y0-75)” indicates that the limits of the y-axis were set between 0-75, which corresponds to the range of taurine values in this cohort (in contrast to the main manuscript figure, which presents the y-axis limits between 0-336 for ease of comparison across cohorts). Similarly, the characters in the string “1Ba_” indicate that this plot is equivalent to the plot in Figure 1, panel B, left side, respectively. The full name of the cohort is indicated in the name of the plot. Plots for females and males are indicated with “F” and “M”, respectively.“Supplementary Material Plots” folderThis folder contains the plots provided in the figures of the supplementary material, together with equivalent plots (same data, same results) that present different y-axis limits that allow to better assess variability within each cohort. This is indicated in the name of the plot. E.g., the string “y0-75” in the name “S1Ba_Balearic-F(5x11,2.5pt,y0-75)” indicates that the limits of the y-axis were set between 0-75, , which corresponds to the range of taurine values in this cohort (in contrast to the main manuscript figure, which presents the y-axis limits between 0-336 for ease of comparison across cohorts). Similarly, the characters in the string “1Ba_” indicate that this plot is equivalent to the plot in Figure 1, panel B, left side, respectively. The full name of the cohort is indicated in the name of the plot. Plots for females and males are indicated with “F” and “M”, respectively.“Complementary Plots” folderThis folder contains plots to visualize the results presented in supplementary tables S40-S45 of our article (for details see scripts "Analysis1_BLSA-females-adjDiet.R", "Analysis1_BLSA-males-adjDiet.R", “Analysis1_BLSA-females-excluding-outliers.R”, “Analysis1_BLSA-males-excluding-outliers.R”, “Analysis1_BLSA-females-onlyActive.R”, “Analysis1_BLSA-males-onlyActive.R”, in the folder “Analysis”). None of these plots are provided in the figures of the main manuscript or the supplementary material. The names of the plots follow the same rules described above.
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MLR models of age at onset of T1D after removing outliers (N = 354).
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Comparison of R2Y and Q2 values calculated for the OPLS-DA models with and without excluding the outliers.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
R code used for each data set to perform negative binomial regression, calculate overdispersion statistic, generate summary statistics, remove outliers