cycle_npreg_mstep: Estimate parameters of the cyclic trends

In peco: A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data

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

peco documentation built on Nov. 8, 2020, 8:16 p.m.