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sample size and searches 177 the optimal linear combinations of multidimensional, highly correlated, explanatory variables (here innate immune 178 response variables), which best explain the variability in the multivariate outcome (here antibody responses). The 179 linear combinations of variables are summarized in latent components. The sparse version, sPLS, allows for variable 180 down-selection by including a Lasso penalization, Boulesteix and Strimmer, vol.181, issue.2012, 2007. ,
, The predictive value of the multivariable linear model was assessed by the root square residuals (RSR)
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, Kernel estimation of the density function windowed between 0 and the corresponding Low Level Of Quantification 192 (LLOQ) of the standard curve. The cytokine marker selected by sPLS (IP-10 plasma concentration on day 3) was 193 not concerned by this imputation
, For the analyses of differential gene expression after vaccination, we used the voom/limma pipeline, p.195
We first applied a filter to exclude genes with 197 very low mean count from statistical comparisons (exclusion of genes with mean raw count <5) and then used TMM 198 (Trimmed Mean of M-values) normalization and logCPM, 2014. ,
Using these weights, the limma empirical Bayes analysis pipeline, based on the normal 204 linear model, can then be applied to RNA Seq data. We tested the null hypothesis of log2 fold change =0 (compared 205 to Day 0) for each gene, and used the Benjamini-Hochberg procedure for controlling FDR to account for multiple 206 testing, TMM allows for between-sample normalization under the null hypothesis and serves as correction factor for library 200 size in the analyses, 1995. ,
, The voom method relies on linear modelling of logCPM transformed normalized data, with TMM library size 212 correction, with an estimation of mean-variance trend through a LOWESS curve. This non-parametric approach 213 avoids the negative binomial distribution assumption by instead estimating precision weights. Using these weights, 214 the limma empirical Bayes analysis pipeline, our analysis, we first applied a filter to exclude genes with very low mean count from statistical comparisons 208 (exclusion of genes with mean raw count <5) and then used TMM, 2010.
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