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


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Documentation for package ‘peco’ version 1.0.0

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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