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 summary.noise: Summary of a 'noise' Object.summary.lmmspline: Summary of a 'lmmspline' Object.summary.lmmsde: Summary of a 'lmmsde' Object.predict.lmmspline: Predicts fitted values of an 'lmmspline' Object.plot.noise: Plot of 'associations' objects.plot.lmmspline: Plot of 'lmmspline' object.lmmSpline-methods: Data-driven linear mixed effect model spline modelling.lmmspline-class: 'lmmspline' class a S4 class that extends 'lmms' class.lmms-package: Data-driven mixed effect model splines fit and differential.lmmsDE-methods: Differential expression analysis using linear mixed effect.lmmsde-class: 'lmmsde' class a S4 class that extends 'lmms' class.lmms-class: 'lmms' class a S4 superclass to extend 'lmmspline' and.kidneySimTimeGroup: Kidney Simulation Data.investNoise-methods: Quality control for time course profiles.
![lmms sample lmms sample](https://i.ytimg.com/vi/MxkSvKJ0Kkw/maxresdefault.jpg)
filterNoise-methods: Filter non-informative trajectories.deriv.lmmspline: Derivative information for 'lmmspline' objects.