Data-driven mixed effect model splines fit and differential expression analysis


The package provides quality control and filtering methods as well as linear mixed effect model splines techniques for modelling and differential expression analysis to model and mine highly dimensional data sets: investNoise to identify noisy profiles and filterNoise to remove them; lmmSpline to model heterogeneous time course expression profiles; lmmsDE to perform differential expression analysis of time course data to identify differential expression over time, between groups or time and group interaction.


Package: lmms
Type: Package
Version: 1.3.3
Date: 2016-03-04
License: GPL-2
LazyLoad: yes

Functions for quality control and filtering: investNoise, filterNoise,summary.noise,plot.noise
Functions for data modelling: lmmSpline, lmmsDE,deriv.lmmspline,predict.lmmspline
Functions for summarization: summary.lmmspline, summary.lmmsde
Functions for plots: plot.lmmspline, plot.lmmsde


Jasmin Straube with contributions from Kim-Anh Le Cao, Emma Huang and Dominique Gorse

Maintainer: Jasmin Straube <>

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