cycle_npreg_mstep {peco} | R Documentation

## Estimate parameters of the cyclic trends

### Description

This is used in both cycle_npreg_insample (training data fitting) and cycle_npreg_outsample (testing data prediction) to estimate cyclic trends of gene expression values. The function outputs for each gene standard error of the cyclic trend, cyclic function, and the estimated expression levels given the cyclic function.

### Usage

    cycle_npreg_mstep(Y, theta, method.trend = c("trendfilter", "loess",
      "bspline"), polyorder = 2, ncores = 2)

### Arguments

`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: 'trendfilter' (`fit_trendfilter()`), 'loess' (`fit_loess()`) and 'bsplines' (`fit_bspline()`). 'trendfilter' provided the best fit in our study. But 'trendfilter' uses cross-validation and takes some time. Therefore, we recommend using bspline for quick results.
`polyorder` | We estimate cyclic trends of gene expression levels using nonparamtric trend filtering. The default fits second degree polynomials.
`ncores` | How many computing cores to use? We use doParallel package for parallel computing.

### Value

A list with the following elements:

`Y` | Input gene expression data.
`theta` | Input angles.
`mu_est` | Estimated expression levels given the cyclic function for each gene.
`sigma_est` | Estimated standard error of the cyclic trends for each gene
`funs` | Estimated cyclic functions

### Author(s)

Joyce Hsiao

### See Also

`cycle_npreg_insample` for estimating cyclic functions given known phasesfrom training data, `cycle_npreg_outsample` for predicting cell cycle phase using parameters learned from `cycle_npreg_insample`

Other peco classifier functions: `cycle_npreg_insample`, `cycle_npreg_loglik`, `cycle_npreg_outsample`, `initialize_grids`

[Package peco version 1.2.0 Index]