Introduction | Load packages | Load the data | Fit linear models and compute posterior fits | Get rhythm statistics | Get differential rhythm statistics | Get observed and fitted time-courses

Introduction | Load packages | Load the data | Fit linear models | Get posterior fits | Get rhythm statistics | Get observed and fitted time-courses

Introduction | Load packages | Load the data | Fit linear models and compute posterior fits | Draw samples from the posterior fits | Get fitted time-courses | Get rhythm and differential rhythm statistics

Load packages and set parameters | Load the data | Identify rhythmic genes | Identify differentially rhythmic genes | Identify differentially expressed genes | Plot the results

Load the necessary packages | Generate example data | Create training and testing observations | Train and test a ZeitZeiger predictor | Plot the periodic spline fits | Plot prediction error on the test set | Plot a test observation's time-dependent likelihood | Run cross-validation | Plot the error for each set of parameter values | Train a model on the full dataset | Plot the behavior of the SPCs over time | Plot the coefficients of the features for the SPCs

Load the packages we'll use | Simulate the data | Plot the simulated time-course for selected genes | Detect rhythmic genes | Evaluate accuracy of rhythmic gene detection

Load required packages | Evenly-spaced timepoints | Custom rhythm function | Specified timepoints | Random timepoints | Time-dependent amplitude and base | Differential rhythmicity between conditions | Controlling negative binomial dispersion and base | Poisson sampling at high resolution

Introduction | Load the data | Filter out lowly expressed genes | Fill in counts for sample-gene pairs having zero counts | Option 1: limma-voom | Option 2: DESeq2 | Continue using limorhyde2