lmmsDE function fits LMM models with either a cubic, p-spline or cubic p-spline basis and compares the models to the null models. The type of basis to use is specified with the basis argument.
lmmsDE(data, time, sampleID, group, type,
experiment, basis, knots,keepModels, numCores)data.frame or matrix containing the samples as rows and features as columnsnumeric vector containing the sample time point information.character, numeric or factor vector containing information about the unique identity of each samplecharacter, numeric or factor vector containing information about the group (or class) of each samplecharacter indicating what type of analysis is to be performed. 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 . Use "all" to calculate all three types.character describing the experiment performed for correlation handling. Use "all" for data-driven selection of model; "timecourse" for replicated experiments with less variation in individual expression values (e.g. model organism, cell culture), "longitudinal1" for different intercepts and "longitudinal2" for different intercepts and slopes.character string. What type of basis to use, matching one of "cubic" smoothing spline as defined by Verbyla et al. 1999, "p-spline" Durban et al. 2005 or a "cubic p-spline".logical value if you want to keep the model output. Default value is FALSEnumeric value indicating the number of CPU cores to be used for parallelization. Default value is automatically estimated.lmmsde containing the following components:
data.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.numeric vector indicating the model used to fit the 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.data.frame containing predicted values based on linear model object or linear mixed effect model object.data.frame containing predicted values based on linear model object or linear mixed effect model object.data.frame containing predicted values based on linear model object or linear mixed effect model object.data.frame containing predicted for the time*group model values based on linear model object or linear mixed effect model object.list of class lme, containing the models for every feature modelling the time effect.list of class lme, containing the models for every feature modelling group effect. list of class lme, containing the models for every feature modelling time and group interaction effect. character, describing the test performed either time, group, time*group or all. character describing the model used to perform differential expression analysis.Ruppert, D. (2002). Selecting the number of knots for penalized splines. J. Comp. Graph. Stat. 11, 735-757
Verbyla, A. P., Cullis, B. R., & Kenward, M. G. (1999). The analysis of designed experiments and longitudinal data by using smoothing splines. Appl.Statist, 18(3), 269-311.
Straube J., Gorse A.-D., Huang B.E., & Le Cao K.-A. (2015). A linear mixed model spline framework for analyzing time course 'omics' data PLOSONE, 10(8), e0134540.
summary.lmmsde, plot.lmmsde
## Not run:
# data(kidneySimTimeGroup)
# lmmsDEtest <-lmmsDE(data=kidneySimTimeGroup$data,time=kidneySimTimeGroup$time,
# sampleID=kidneySimTimeGroup$sampleID,group=kidneySimTimeGroup$group)
# summary(lmmsDEtest)## End(Not run)
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