Package: peco 1.25.0

Chiaowen Joyce Hsiao

peco: A Supervised Approach for Predicting cell Cycle Progression 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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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
peco/json (API)

# 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:

cellcyclegenes_whitfield2002 - List of cell cycle genes identified in Whitfield et al. 2002.
model_5genes_predict - A SingleCellExperiment object
model_5genes_train - Traing model results among samples from 5 individuals.
sce_top101genes - Molecule counts of the 101 significant cyclical genes in the 888 samples analyzed in the study.
training_human - Training data from 888 single-cell samples and 101 top cyclic genes

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

sequencing rnaseq geneexpression transcriptomics singlecell software statisticalmethod classification visualization cell-cycle single-cell-rna-seq

6.34 score 14 stars 35 scripts 11 exports 84 dependencies

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

Target	Result	Time
bioc-checks	NOTE	201
linux-devel-x86_64	OK	580
source / vignettes	OK	373
linux-release-x86_64	OK	570
macos-release-arm64	OK	344
macos-oldrel-arm64	OK	299
windows-devel	OK	509
windows-release	OK	445
windows-oldrel	OK	510
wasm-release	OK	184

Exports:circ_dist cycle_npreg_insample cycle_npreg_outsample data_transform_quantile fit_bspline fit_cyclical_many fit_loess fit_trendfilter intensity2circle rotation shift_origin

Dependencies:abind assertthat assorthead beachmat beeswarm BH Biobase BiocGenerics BiocNeighbors BiocParallel BiocSingular boot circular cli codetools conicfit cpp11 DelayedArray doParallel dqrng farver FNN foreach formatR futile.logger futile.options geigen generics genlasso GenomicRanges ggbeeswarm ggplot2 ggrepel glue gridExtra gtable igraph IRanges irlba isoband iterators labeling lambda.r lattice lifecycle magrittr Matrix MatrixGenerics matrixStats mvtnorm pheatmap pkgconfig pracma R6 RColorBrewer Rcpp RcppAnnoy RcppEigen RcppML RcppProgress rlang RSpectra rsvd Rtsne S4Arrays S4Vectors S7 ScaledMatrix scales scater scuttle Seqinfo SingleCellExperiment sitmo snow SparseArray SummarizedExperiment uwot vctrs vipor viridis viridisLite withr XVector

Predicting cell cycle phase using peco

Rendered fromvignette.Rmdusingknitr::rmarkdown

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

Help page	Topics
list of cell cycle genes identified in Whitfield et al. 2002.	cellcyclegenes_whitfield2002
Pairwise distance between two circular variables	circ_dist
Obtain cyclic trend estimates from the training data	cycle_npreg_insample
Infer angles or cell cycle phase based on gene expression data	cycle_npreg_loglik
Estimate parameters of the cyclic trends	cycle_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 gene	data_transform_quantile
Use bsplies to cyclic trend of gene expression levels	fit_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 values	fit_loess
Using trendfiltering to estimate cyclic trend of gene expression	fit_trendfilter
For prediction, initialize grid points for cell cycle phase on a circle.	initialize_grids
Infer angles for each single-cell samples using fluorescence intensities	intensity2circle
A SingleCellExperiment object	model_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_var	rotation
Molecule counts of the 101 significant cyclical genes in the 888 samples analyzed in the study.	sce_top101genes
Shift origin of the angles	shift_origin
Training data from 888 single-cell samples and 101 top cyclic genes	training_human

Package: peco 1.25.0

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

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)

Package: peco 1.25.0

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

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)

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