Package: peco 1.25.0

Chiaowen Joyce Hsiao

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

Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data.

Authors:Chiaowen Joyce Hsiao [aut, cre], Matthew Stephens [aut], John Blischak [ctb], Peter Carbonetto [ctb]

peco_1.25.0.tar.gz
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peco_1.25.0.tgz(r-4.6-any)peco_1.25.0.tgz(r-4.5-any)
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peco_1.25.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
peco/json (API)
NEWS

# Install 'peco' in R:
install.packages('peco', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jhsiao999/peco/issues

Datasets:

On BioConductor:peco-1.25.0(bioc 3.24)peco-1.24.0(bioc 3.23)

sequencingrnaseqgeneexpressiontranscriptomicssinglecellsoftwarestatisticalmethodclassificationvisualizationcell-cyclesingle-cell-rna-seq

6.33 score 14 stars 34 scripts 372 downloads 11 exports 93 dependencies

Last updated from:c4a5054710. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE224
linux-devel-x86_64OK551
source / vignettesOK340
linux-release-x86_64OK555
macos-release-arm64OK290
macos-oldrel-arm64OK434
windows-develOK500
windows-releaseOK463
windows-oldrelOK445
wasm-releaseOK154

Exports:circ_distcycle_npreg_insamplecycle_npreg_outsampledata_transform_quantilefit_bsplinefit_cyclical_manyfit_loessfit_trendfilterintensity2circlerotationshift_origin

Dependencies:abindassertthatassortheadbase64encbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularbootCairocircularclicodetoolsconicfitcpp11DelayedArraydoParalleldqrngfarverFNNforeachformatRfutile.loggerfutile.optionsgeigengenericsgenlassoGenomicRangesggbeeswarmggplot2ggrastrggrepelgluegridExtragtableigraphIRangesirlbaisobanditeratorsjsonlitelabelinglambda.rlatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsmvtnormpheatmappkgconfigpngpracmaR6raggRColorBrewerRcppRcppAnnoyRcppEigenRcppMLRcppProgressrlangRSpectrarsvdRtsneS4ArraysS4VectorsS7ScaledMatrixscalesscaterscuttleSeqinfoSingleCellExperimentsitmosnowSparseArraystringiSummarizedExperimentsystemfontstextshapinguwotvctrsviporviridisviridisLitewithrXVector

Predicting cell cycle phase using peco

Rendered fromvignette.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2020-03-23
Started: 2019-09-12

Readme and manuals

Help Manual

Help pageTopics
list of cell cycle genes identified in Whitfield et al. 2002.cellcyclegenes_whitfield2002
Pairwise distance between two circular variablescirc_dist
Obtain cyclic trend estimates from the training datacycle_npreg_insample
Infer angles or cell cycle phase based on gene expression datacycle_npreg_loglik
Estimate parameters of the cyclic trendscycle_npreg_mstep
Predict test-sample ordering using training labels (no update)cycle_npreg_outsample
Transform counts by first computing counts-per-million (CPM), then quantile-normalize CPM for each genedata_transform_quantile
Use bsplies to cyclic trend of gene expression levelsfit_bspline
Compute proportation of variance explained by cyclic trends in the gene expression levels for each gene.fit_cyclical_many
Use loess to estimate cyclic trends of expression valuesfit_loess
Using trendfiltering to estimate cyclic trend of gene expressionfit_trendfilter
For prediction, initialize grid points for cell cycle phase on a circle.initialize_grids
Infer angles for each single-cell samples using fluorescence intensitiesintensity2circle
A SingleCellExperiment objectmodel_5genes_predict
Traing model results among samples from 5 individuals.model_5genes_train
Rotate circular variable shift_var to minimize distance between ref_var and shift_varrotation
Molecule counts of the 101 significant cyclical genes in the 888 samples analyzed in the study.sce_top101genes
Shift origin of the anglesshift_origin
Training data from 888 single-cell samples and 101 top cyclic genestraining_human