Linear Mixed Effect Model Splines for Modelling and Analysis of Time Course Data

Linear Mixed effect Model Splines ('lmms') implements linear mixed effect model splines for modelling and differential expression for highly dimensional data sets: investNoise() for quality control and filterNoise() for removing non-informative trajectories; lmmSpline() to model time course expression profiles and lmmsDE() performs differential expression analysis to identify differential expression between groups, time and/or group x time interaction.


Reference manual

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1.3.3 by Jasmin Straube, 4 years ago

Browse source code at

Authors: Jasmin Straube [aut, cre] , Kim-Anh Le Cao [aut] , Emma Huang [aut] , Dominique Gorse [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports stats, methods, nlme, lmeSplines, parallel, reshape2, gdata, gplots, gridExtra

Depends on ggplot2

Suggested by dynOmics.

See at CRAN