This is used by cycle_npreg_insample
(model fitting from training data) and
cycle_npreg_outsample
(prediction in test data
estimate cyclic trends of gene expression values. The function
outputs, for each gene, a standard error of the cyclic trend, a
cyclic function, and the estimated expression levels from the
cyclic function.
cycle_npreg_mstep( Y, theta, method.trend = c("trendfilter", "loess", "bspline"), polyorder = 2, ncores = 2 )
Y | Gene by sample expression matrix (log2CPM). |
---|---|
theta | Observed cell times. |
method.trend | How to estimate cyclic trend of gene expression
values. We offer three options: |
polyorder | We estimate cyclic trends of gene expression levels using nonparamtric trend filtering. |
ncores | How many computing cores to use? We use the
|
A list with the following elements:
Input gene expression data.
Input angles.
Estimated expression levels given the cyclic function for each gene.
Estimated standard error of the cyclic trends for each gene.
Estimated cyclic functions.