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

## Documentation for package ‘peco’ version 1.6.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## 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