Package: peco 1.17.0

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]

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NEWS

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

Peer review:

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

Datasets:

On BioConductor:peco-1.17.0(bioc 3.20)peco-1.16.0(bioc 3.19)

bioconductor-package

11 exports 0.82 score 110 dependencies

Last updated 2 months agofrom:9ceb0d7459

Exports:circ_distcycle_npreg_insamplecycle_npreg_outsampledata_transform_quantilefit_bsplinefit_cyclical_manyfit_loessfit_trendfilterintensity2circlerotationshift_origin

Dependencies:abindaskpassassertthatbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularbootCairocircularclicodetoolscolorspaceconicfitcpp11crayoncurlDelayedArrayDelayedMatrixStatsdoParalleldqrngfansifarverFNNforeachformatRfutile.loggerfutile.optionsgeigengenlassoGenomeInfoDbGenomeInfoDbDataGenomicRangesggbeeswarmggplot2ggrastrggrepelgluegridExtragtablehttrigraphIRangesirlbaisobanditeratorsjsonlitelabelinglambda.rlatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellmvtnormnlmeopensslpheatmappillarpkgconfigpngpracmaR6raggRColorBrewerRcppRcppAnnoyRcppEigenRcppHNSWRcppMLRcppProgressrlangRSpectrarsvdRtsneS4ArraysS4VectorsScaledMatrixscalesscaterscuttleSingleCellExperimentsitmosnowSparseArraysparseMatrixStatsSummarizedExperimentsyssystemfontstextshapingtibbleUCSC.utilsutf8uwotvctrsviporviridisviridisLitewithrXVectorzlibbioc

Predicting cell cycle phase using peco

Joyce Hsiao

Rendered fromvignette.Rmdusingknitr::rmarkdownon Jun 20 2024.

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