lmms: 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.
- Jasmin Straube [aut, cre], Kim-Anh Le Cao [aut], Emma Huang [aut], Dominique Gorse [ctb]
- Date of publication
- 2016-03-07 01:09:11
- Jasmin Straube <email@example.com>
- GPL (>= 2)
- Derivative information for 'lmmspline' objects
- Filter non-informative trajectories
- Quality control for time course profiles
- Kidney Simulation Data
- 'lmms' class a S4 superclass to extend 'lmmspline' and...
- 'lmmsde' class a S4 class that extends 'lmms' class.
- Differential expression analysis using linear mixed effect...
- Data-driven mixed effect model splines fit and differential...
- 'lmmspline' class a S4 class that extends 'lmms' class.
- Data-driven linear mixed effect model spline modelling
- 'noise' S4 class
- Plot of 'lmmsde' objects
- Plot of 'lmmspline' object
- Plot of 'associations' objects
- Predicts fitted values of an 'lmmspline' Object
- Summary of a 'lmmsde' Object
- Summary of a 'lmmspline' Object
- Summary of a 'noise' Object
Files in this package