A Supervised Approach for Predicting cell Cycle Progression using scRNA-seq data

Documentation for package ‘peco’ version 1.0.0

Help Pages

cellcyclegenes_whitfield2002	list of cell cycle genes identified in Whitfield et al. 2002.
circ_dist	Pairwise distance between two circular variables
cycle_npreg_insample	Obtain cyclic trend estimates from the training data
cycle_npreg_loglik	Infer angles or cell cycle phase based on gene expression data
cycle_npreg_mstep	Estimate parameters of the cyclic trends
cycle_npreg_outsample	Predict test-sample ordering using training labels (no update)
data_transform_quantile	Transform counts by first computing counts-per-million (CPM), then quantile-normalize CPM for each gene
fit_bspline	Use bsplies to cyclic trend of gene expression levels
fit_cyclical_many	Compute proportation of variance explained by cyclic trends in the gene expression levels for each gene.
fit_loess	Use loess to estimate cyclic trends of expression values
fit_trendfilter	Using trendfiltering to estimate cyclic trend of gene expression
initialize_grids	For prediction, initialize grid points for cell cycle phase on a circle.
intensity2circle	Infer angles for each single-cell samples using fluorescence intensities
model_5genes_predict	A SingleCellExperiment object
model_5genes_train	Traing model results among samples from 5 individuals.
rotation	Rotate circular variable shift_var to minimize distance between ref_var and shift_var
sce_top101genes	Molecule counts of the 101 significant cyclical genes in the 888 samples analyzed in the study.
shift_origin	Shift origin of the angles
training_human	Training data from 888 single-cell samples and 101 top cyclic genes