A linear mixed model spline framework for analyzing time course 'omics' data PLOSONE, 10(8), e0134540.Linux Mint 18.2 Cinnamon 64 (but in the update panel it says Sylvia!) Straube J., Gorse A.-D., Huang B.E., & Le Cao K.-A. The analysis of designed experiments and longitudinal data by using smoothing splines. Selecting the number of knots for penalized splines. Simple fitting of subject-specific curves for longitudinal data. 1=linear mixed effect model spline (LMMS) with defined basis ('cubic' by default) 2 = LMMS taking subject-specific random intercept, 3 = LMMS with subject specific intercept and slope.ĭata.frame containing predicted values based on linear model object or linear mixed effect model object.ĭata.frame containing predicted for the time*group model values based on linear model object or linear mixed effect model object.Ī list of class lme, containing the models for every feature modelling the time effect.Ī list of class lme, containing the models for every feature modelling group effect.Ī list of class lme, containing the models for every feature modelling time and group interaction effect.Īn object of class character, describing the test performed either time, group, time*group or all.Īn object of class character describing the model used to perform differential expression analysis.ĭurban, M., Harezlak, J., Wand, M. Numeric vector indicating the model used to fit the data. LmmsDE returns an object of class lmmsde containing the following components:ĭata.frame returning p-values and adjusted p-values using Benjamini-Hochberg correction for multiple testing of the differential expression testing over time, group or their interaction. LmmsDE extends the LMMS modelling framework to permit tests between groups, across time, and for interactions between the two implemented as described in Straube et al. Default value is automatically estimated. Default value is FALSEĪlternative numeric value indicating the number of CPU cores to be used for parallelization. Not in use for the 'cubic' smoothing spline basis.Īlternative logical value if you want to keep the model output. 2005 or a "cubic p-spline".Ĭan take an integer value corresponding to the number of knots for the chosen basis or by default calculated as in Ruppert 2002.
What type of basis to use, matching one of "cubic" smoothing spline as defined by Verbyla et al. model organism, cell culture), "longitudinal1" for different intercepts and "longitudinal2" for different intercepts and slopes.Ĭharacter string. Use "all" for data-driven selection of model "timecourse" for replicated experiments with less variation in individual expression values (e.g. Use "all" to calculate all three types.Ĭharacter describing the experiment performed for correlation handling. Options are "time" to identify differential expression over time, "group" to identify profiles with different baseline levels (intercepts), and "time*group" an interaction between these two. Numeric vector containing the sample time point information.Ĭharacter, numeric or factor vector containing information about the unique identity of each sampleĬharacter, numeric or factor vector containing information about the group (or class) of each sampleĬharacter indicating what type of analysis is to be performed.
LmmsDE ( data, time, sampleID, group, type, experiment, basis, knots, keepModels, numCores )ĭata.frame or matrix containing the samples as rows and features as columns