Package 'peco'

list of cell cycle genes identified in Whitfield et al. 2002.

Description

List of cell cycle genes and their associated cell cycle state as reported in Whitfield et al. 2002.

Usage

data(cellcyclegenes_whitfield2002)

Format

A list with the follwing elements

hgnc

Gene symbol

ensembl

ENSEMBL gene ID

phase

Marker phase identified in Whitfield et al. 2002

---

Pairwise distance between two circular variables

Description

We define distance between two angles: the minimum of the differences in both clockwise and counterclockwise directions.

Usage

circ_dist(y1, y2)

Arguments

y1	A vector of angles.
y2	A vector of angles.

Value

A vector of distances between angles.

Author(s)

Joyce Hsiao, Matthew Stephens

Examples

# a vector of angles
theta_ref <- seq(0,2*pi, length.out=100)

# shift the origin of theta_ref to pi
theta_compare <- shift_origin(theta_ref, origin = pi)
mean(circ_dist(theta_ref, theta_compare))

# after rotation of angles, difference is 0 between the original
# and the shifted angles
theta_compare_rotated <- rotation(ref_var=theta_ref,
    shift_var=theta_compare)
mean(circ_dist(theta_ref, theta_compare_rotated))

---

Obtain cyclic trend estimates from the training data

Description

Estimates cyclic trends of gene expression levels using training data.

Usage

cycle_npreg_insample(Y, theta, ncores = 2, polyorder = 2,
  method.trend = c("trendfilter", "loess", "bspline"))

Arguments

Y	A matrix of normalized and transformed gene expression values. Gene by sample.
theta	A vector of angles.
ncores	We use doParallel package for parallel computing.
polyorder	We estimate cyclic trends of gene expression levels using nonparamtric trend filtering. The default fits second degree polynomials.
method.trend	Varous methods that can be applied to estimate cyclic trend of gene expression levels.

Value

A list with four elements:

Y	Gene expression marix.
theta	Vector of angles or cell cycle phases.
sigma_est	Estimated standard error of the cyclic trend for each gene.
funs_est	A list of functions for approximating the cyclic trends of gene express levels for each gene.

Author(s)

Joyce Hsiao

See Also

cycle_npreg_mstep for estimating cyclic functions given inferred phases from cycle_npreg_loglik, cycle_npreg_outsample for predicting cell cycle phase using parameters learned from cycle_npreg_insample

Other peco classifier functions: cycle_npreg_loglik, cycle_npreg_mstep, cycle_npreg_outsample, initialize_grids

Examples

# see \code{\link{cycle_npreg_insample}}

---

Infer angles or cell cycle phase based on gene expression data

Description

Infer angles or cell cycle phase based on gene expression data

Usage

cycle_npreg_loglik(Y, sigma_est, funs_est, grids = 100,
  method.grid = c("pca", "uniform"))

Arguments

Y	Gene by sample expression matrix.
sigma_est	A vector of standard errors for each gene from the training data.
funs_est	A vector of cyclic functions estimated for each gene from the training data.
grids	number of bins to be selected along 0 to 2pi.
method.grid	The approach to initialize angles in the computation. uniform creates k equally-spaced bins (grids). pca uses gene expression values to infer angles, and then use these pca-based angles to move the cells to the closest bin (as defined by uniform).

Value

A list with the following three elements:

cell_times_est	Inferred angles or cell cycle phases, NOT ordered.
loglik_est	Log-likelihood estimates for each gene.
prob_per_cell_by_celltimes	Probabilities of each cell belong to each bin.

Author(s)

Joyce Hsiao

See Also

initialize_grids for selecting angles in cycle_npreg_loglik, cycle_npreg_mstep for estimating cyclic functions given inferred phases from cycle_npreg_loglik, cycle_npreg_outsample for predicting cell cycle phase using parameters learned from cycle_npreg_insample

Other peco classifier functions: cycle_npreg_insample, cycle_npreg_mstep, cycle_npreg_outsample, initialize_grids

---

Estimate parameters of the cyclic trends

Description

This is used in both cycle_npreg_insample (training data fitting) and cycle_npreg_outsample (testing data prediction) to estimate cyclic trends of gene expression values. The function outputs for each gene standard error of the cyclic trend, cyclic function, and the estimated expression levels given the cyclic function.

Usage

cycle_npreg_mstep(Y, theta, method.trend = c("trendfilter", "loess",
  "bspline"), polyorder = 2, ncores = 2)

Arguments

Y	Gene by sample expression matrix (log2CPM).
theta	Observed cell times.
method.trend	How to estimate cyclic trend of gene expression values? We offer three options: 'trendfilter' (fit_trendfilter()), 'loess' (fit_loess()) and 'bsplines' (fit_bspline()). 'trendfilter' provided the best fit in our study. But 'trendfilter' uses cross-validation and takes some time. Therefore, we recommend using bspline for quick results.
polyorder	We estimate cyclic trends of gene expression levels using nonparamtric trend filtering. The default fits second degree polynomials.
ncores	How many computing cores to use? We use doParallel package for parallel computing.

Value

A list with the following elements:

Y	Input gene expression data.
theta	Input angles.
mu_est	Estimated expression levels given the cyclic function for each gene.
sigma_est	Estimated standard error of the cyclic trends for each gene
funs	Estimated cyclic functions

Author(s)

Joyce Hsiao

See Also

cycle_npreg_insample for estimating cyclic functions given known phasesfrom training data, cycle_npreg_outsample for predicting cell cycle phase using parameters learned from cycle_npreg_insample

Other peco classifier functions: cycle_npreg_insample, cycle_npreg_loglik, cycle_npreg_outsample, initialize_grids

---

Predict test-sample ordering using training labels (no update)

Description

Apply the estimates of cycle_npreg_insample to another gene expression dataset to infer an angle or cell cycle phase for each cell.

Usage

cycle_npreg_outsample(Y_test, sigma_est, funs_est,
  method.trend = c("trendfilter", "loess", "bspline"), normed = TRUE,
  polyorder = 2, method.grid = "uniform", ncores = 2, grids = 100,
  get_trend_estimates = FALSE)

Arguments

Y_test	A SingleCellExperiment object.
sigma_est	Input from training data. A vector of gene-specific standard error of the cyclic trends.
funs_est	Input fron training data. A vector of cyclic functions estimating cyclic trends.
method.trend	Varous methods that can be applied to estimate cyclic trend of gene expression levels.
normed	Is the data already normalized? TRUE or FALSE.
polyorder	We estimate cyclic trends of gene expression levels using nonparamtric trend filtering. The default fits second degree polynomials.
method.grid	Method for defining bins along the circle.
ncores	We use doParallel package for parallel computing.
grids	number of bins to be selected along 0 to 2pi.
get_trend_estimates	To re-estimate the cylic trend based on the predicted cell cycle phase or not (T or F). Default FALSE. This step calls trendfilter and is computationally intensive.

Value

A list with the following elements:

Y	The input gene expression marix.
cell_times_est	Inferred angles or cell cycle phases, NOT ordered.
loglik_est	Log-likelihood estimates for each gene.
cell_times_reordered	The inferred angles reordered (in ascending order).
Y_reorded	The input gene expression matrix reordered by cell_times_reordered.
sigma_reordered	Estimated standard error of the cyclic trend for each gene, reordered by cell_times_reordered.
funs_reordered	A list of functions for approximating the cyclic trends of gene express levels for each gene, reordered by cell_times_reordered.
mu_reordered	Estimated cyclic trend of gene expression values for each gene, reordered by cell_times_reordered.
prob_per_cell_by_celltimes	Probabilities of each cell belong to each bin.

See Also

cycle_npreg_insample for obtaining parameteres for cyclic functions from training data, cycle_npreg_loglik for log-likehood at angles between 0 to 2pi, initialize_grids for selecting angles in cycle_npreg_loglik, cycle_npreg_mstep for estimating cyclic functions given inferred phases from cycle_npreg_loglik

Other peco classifier functions: cycle_npreg_insample, cycle_npreg_loglik, cycle_npreg_mstep, initialize_grids

Examples

# import data
library(SingleCellExperiment)
data(sce_top101genes)

# select top 5 cyclic genes
sce_top5 <- sce_top101genes[order(rowData(sce_top101genes)$pve_fucci,
                                  decreasing=TRUE)[1:5],]

# Select samples from NA18511 for our prediction example
coldata <- colData(sce_top5)
which_samples_train <- rownames(coldata)[coldata$chip_id != "NA18511"]
which_samples_predict <- rownames(coldata)[coldata$chip_id == "NA18511"]

# learning cyclic functions of the genes using our training data
sce_top5 <- data_transform_quantile(sce_top5)
expr_quant <- assay(sce_top5, "cpm_quantNormed")
Y_train <- expr_quant[, colnames(expr_quant) %in% which_samples_train]
theta_train <-
    coldata$theta_shifted[rownames(coldata) %in% which_samples_train]
names(theta_train) <-
    rownames(coldata)[rownames(coldata) %in% which_samples_train]

# obtain cyclic function estimates
model_5genes_train <- cycle_npreg_insample(Y = Y_train,
                                           theta = theta_train,
                                           polyorder=2,
                                           ncores=2,
                                           method.trend="trendfilter")

# predict cell cycle
model_5genes_predict <- cycle_npreg_outsample(
  Y_test=sce_top5[,colnames(sce_top5) %in% which_samples_predict],
  sigma_est=model_5genes_train$sigma_est,
  funs_est=model_5genes_train$funs_est,
  method.trend="trendfilter",
  ncores=2,
  get_trend_estimates=FALSE)

# estimate cyclic gene expression levels given cell cycle for each gene
predict_cyclic <-
    fit_cyclical_many(Y=assay(model_5genes_predict$Y,"cpm_quantNormed"),
                      theta=colData(model_5genes_predict$Y)$cellcycle_peco)
all.equal(names(predict_cyclic[[2]]), colnames(predict_cyclic[[1]]))

par(mfrow=c(2,3), mar=c(4,4,3,1))
for (g in seq_along(rownames(model_5genes_predict$Y))) {
  plot(assay(model_5genes_predict$Y,"cpm_quantNormed")[
      rownames(model_5genes_predict$Y)[g],],
       x=colData(model_5genes_predict$Y)$cellcycle_peco, axes=FALSE,
       xlab="FUCCI phase",
       ylab="Predicted phase")
  points(y=predict_cyclic$cellcycle_function[[
           rownames(model_5genes_predict$Y)[g]]](
    seq(0, 2*pi, length.out = 100)),
    x=seq(0, 2*pi, length.out = 100),
    pch=16, col="royalblue")
  axis(2); axis(1,at=c(0,pi/2, pi, 3*pi/2, 2*pi),
                labels=c(0,expression(pi/2), expression(pi),
                         expression(3*pi/2), expression(2*pi)))
  abline(h=0, lty=1, col="black", lwd=.7)
  title(rownames(model_5genes_predict$Y_reordered)[g])
}
title("Predicting cell cycle phase for NA18511", outer=TRUE)

---

Transform counts by first computing counts-per-million (CPM), then quantile-normalize CPM for each gene

Description

For each gene, transform counts to CPM and then to a normal distribution.

Usage

data_transform_quantile(sce, ncores = 2)

Arguments

sce	SingleCellExperiment Object.
ncores	We use doParallel package for parallel computing.

Value

SingleCellExperiment Object with an added slot of cpm_quant, cpm slot is added if it doesn't exist.

Author(s)

Joyce Hsiao

Examples

# use our data
library(SingleCellExperiment)
data(sce_top101genes)

# perform CPM normalization using scater, and
# quantile-normalize the CPM values of each gene to normal distribution
sce_top101genes <- data_transform_quantile(sce_top101genes, ncores=2)

plot(y=assay(sce_top101genes, "cpm_quantNormed")[1,],
     x=assay(sce_top101genes, "cpm")[1,],
    xlab = "CPM bbefore quantile-normalization",
    ylab = "CPM after quantile-normalization")

---

Use bsplies to cyclic trend of gene expression levels

Description

Use bsplies to cyclic trend of gene expression levels

Usage

fit_bspline(yy, time)

Arguments

yy	A vector of gene expression values for one gene. The expression values are assumed to have been normalized and transformed to standard normal distribution.
time	A vector of angels (cell cycle phase).

Value

A list with one element, pred.yy, giving the estimated cyclic trend.

Author(s)

Joyce Hsiao

Examples

library(SingleCellExperiment)
data(sce_top101genes)

# select top 10 cyclic genes
sce_top10 <- sce_top101genes[order(rowData(sce_top101genes)$pve_fucci,
                                  decreasing=TRUE)[1:10],]
coldata <- colData(sce_top10)

# cell cycle phase based on FUCCI scores
theta <- coldata$theta
names(theta) <- rownames(coldata)

# normalize expression counts
sce_top10 <- data_transform_quantile(sce_top10, ncores=2)
exprs_quant <- assay(sce_top10, "cpm_quantNormed")

# order FUCCI phase and expression
theta_ordered <- theta[order(theta)]
yy_ordered <- exprs_quant[1, names(theta_ordered)]

fit <- fit_bspline(yy_ordered, time=theta_ordered)

plot(x=theta_ordered, y=yy_ordered, pch=16, cex=.7, axes=FALSE,
  ylab="quantile-normalized expression values", xlab="FUCCI phase",
  main = "bspline fit")
points(x=theta_ordered, y=fit$pred.yy, col="blue", pch=16, cex=.7)
axis(2)
axis(1,at=c(0,pi/2, pi, 3*pi/2, 2*pi),
  labels=c(0,expression(pi/2), expression(pi), expression(3*pi/2),
  expression(2*pi)))
abline(h=0, lty=1, col="black", lwd=.7)

---

Compute proportation of variance explained by cyclic trends in the gene expression levels for each gene.

Description

We applied quadratic (second order) trend filtering using the trendfilter function in the genlasso package (Tibshirani, 2014). The trendfilter function implements a nonparametric smoothing method which chooses the smoothing parameter by cross-validation and fits a piecewise polynomial regression. In more specifics: The trendfilter method determines the folds in cross-validation in a nonrandom manner. Every k-th data point in the ordered sample is placed in the k-th fold, so the folds contain ordered subsamples. We applied five-fold cross-validation and chose the smoothing penalty using the option lambda.1se: among all possible values of the penalty term, the largest value such that the cross-validation standard error is within one standard error of the minimum. Furthermore, we desired that the estimated expression trend be cyclical. To encourage this, we concatenated the ordered gene expression data three times, with one added after another. The quadratic trend filtering was applied to the concatenated data series of each gene.

Usage

fit_cyclical_many(Y, theta, polyorder = 2, ncores = 2)

Arguments

Y	A matrix (gene by sample) of gene expression values. The expression values are assumed to have been normalized and transformed to standard normal distribution.
theta	A vector of cell cycle phase (angles) for single-cell samples.
polyorder	We estimate cyclic trends of gene expression levels using nonparamtric trend filtering. The default fits second degree polynomials.
ncores	doParallel package is used to perform parallel computing to reduce computational time.

Value

A list containing the following objects

predict.yy	A matrix of predicted expression values at observed cell cycle.
cellcycle_peco_ordered	A vector of predicted cell cycle. The values range between 0 to 2pi
cellcycle_function	A list of predicted cell cycle functions.
pve	A vector of proportion of variance explained in each gene by the predicted cell cycle.

Author(s)

Joyce Hsiao

See Also

fit_trendfilter for fitting one gene using trendfilter

Examples

library(SingleCellExperiment)
data(sce_top101genes)

# select top 10 cyclic genes
sce_top10 <- sce_top101genes[order(rowData(sce_top101genes)$pve_fucci,
                                  decreasing=TRUE)[1:10],]
coldata <- colData(sce_top10)

# cell cycle phase based on FUCCI scores
theta <- coldata$theta
names(theta) <- rownames(coldata)

# normalize expression counts
sce_top10 <- data_transform_quantile(sce_top10, ncores=2)
exprs_quant <- assay(sce_top10, "cpm_quantNormed")

# order FUCCI phase and expression
theta_ordered <- theta[order(theta)]
yy_ordered <- exprs_quant[, names(theta_ordered)]

fit <- fit_cyclical_many(Y=yy_ordered, theta=theta_ordered)

---

Use loess to estimate cyclic trends of expression values

Description

Use loess to estimate cyclic trends of expression values

Usage

fit_loess(yy, time)

Arguments

yy	A vector of gene expression values for one gene. The expression values are assumed to have been normalized and transformed to standard normal distribution.
time	A vector of angles (cell cycle phase).

Value

A list with one element, pred.yy, giving the estimated cyclic trend.

Author(s)

Joyce Hsiao

Examples

library(SingleCellExperiment)
data(sce_top101genes)

# select top 10 cyclic genes
sce_top10 <- sce_top101genes[order(rowData(sce_top101genes)$pve_fucci,
                                  decreasing=TRUE)[1:10],]
coldata <- colData(sce_top10)

# cell cycle phase based on FUCCI scores
theta <- coldata$theta
names(theta) <- rownames(coldata)

# normalize expression counts
sce_top10 <- data_transform_quantile(sce_top10, ncores=2)
exprs_quant <- assay(sce_top10, "cpm_quantNormed")

# order FUCCI phase and expression
theta_ordered <- theta[order(theta)]
yy_ordered <- exprs_quant[1, names(theta_ordered)]

fit <- fit_loess(yy_ordered, time=theta_ordered)

plot(x=theta_ordered, y=yy_ordered, pch=16, cex=.7, axes=FALSE,
  ylab="quantile-normalized expression values", xlab="FUCCI phase",
  main = "loess fit")
points(x=theta_ordered, y=fit$pred.yy, col="blue", pch=16, cex=.7)
axis(2)
axis(1,at=c(0,pi/2, pi, 3*pi/2, 2*pi),
  labels=c(0,expression(pi/2), expression(pi), expression(3*pi/2),
  expression(2*pi)))
abline(h=0, lty=1, col="black", lwd=.7)

---

Using trendfiltering to estimate cyclic trend of gene expression

Description

We applied quadratic (second order) trend filtering using the trendfilter function in the genlasso package (Tibshirani, 2014). The trendfilter function implements a nonparametric smoothing method which chooses the smoothing parameter by cross-validation and fits a piecewise polynomial regression. In more specifics: The trendfilter method determines the folds in cross-validation in a nonrandom manner. Every k-th data point in the ordered sample is placed in the k-th fold, so the folds contain ordered subsamples. We applied five-fold cross-validation and chose the smoothing penalty using the option lambda.1se: among all possible values of the penalty term, the largest value such that the cross-validation standard error is within one standard error of the minimum. Furthermore, we desired that the estimated expression trend be cyclical. To encourage this, we concatenated the ordered gene expression data three times, with one added after another. The quadratic trend filtering was applied to the concatenated data series of each gene.

Usage

fit_trendfilter(yy, polyorder = 2)

Arguments

yy	A vector of gene expression values for one gene that are ordered by cell cycle phase. Also, the expression values are normalized and transformed to standard normal distribution.
polyorder	We estimate cyclic trends of gene expression levels using nonparamtric trend filtering. The default fits second degree polynomials.

Value

A list with two elements:

trend.yy	The estimated cyclic trend.
pve	Proportion of variance explained by the cyclic trend in the gene expression levels.

Author(s)

Joyce Hsiao

Examples

library(SingleCellExperiment)
data(sce_top101genes)

# select top 10 cyclic genes
sce_top10 <- sce_top101genes[order(rowData(sce_top101genes)$pve_fucci,
                                  decreasing=TRUE)[1:10],]
coldata <- colData(sce_top10)

# cell cycle phase based on FUCCI scores
theta <- coldata$theta
names(theta) <- rownames(coldata)

# normalize expression counts
sce_top10 <- data_transform_quantile(sce_top10, ncores=2)
exprs_quant <- assay(sce_top10, "cpm_quantNormed")

# order FUCCI phase and expression
theta_ordered <- theta[order(theta)]
yy_ordered <- exprs_quant[1, names(theta_ordered)]

fit <- fit_trendfilter(yy_ordered)

plot(x=theta_ordered, y=yy_ordered, pch=16, cex=.7, axes=FALSE,
  ylab="quantile-normalized expression values", xlab="FUCCI phase",
  main = "trendfilter fit")
points(x=theta_ordered, y=fit$trend.yy, col="blue", pch=16, cex=.7)
axis(2)
axis(1,at=c(0,pi/2, pi, 3*pi/2, 2*pi),
  labels=c(0,expression(pi/2), expression(pi), expression(3*pi/2),
  expression(2*pi)))
abline(h=0, lty=1, col="black", lwd=.7)

---

For prediction, initialize grid points for cell cycle phase on a circle.

Description

For prediction, initialize grid points for cell cycle phase on a circle.

Usage

initialize_grids(Y, grids = 100, method.grid = c("pca", "uniform"))

Arguments

Y	Gene expression matrix. Gene by sample.
grids	number of bins to be selected along 0 to 2pi.
method.grid	The approach to initialize angles in the computation. uniform creates k equally-spaced bins (grids). pca uses gene expression values to infer angles, and then use these pca-based angles to move the cells to the closest bin (as defined by uniform).

Value

A vector of initialized angles to be used in cycle_npreg_loglik to infer angles.

Author(s)

Joyce Hsiao

See Also

cycle_npreg_loglik for log-likehood at angles between 0 to 2pi, cycle_npreg_mstep for estimating cyclic functions given inferred phases from cycle_npreg_loglik, cycle_npreg_outsample for predicting cell cycle phase using parameters learned from cycle_npreg_insample

Other peco classifier functions: cycle_npreg_insample, cycle_npreg_loglik, cycle_npreg_mstep, cycle_npreg_outsample

---

Infer angles for each single-cell samples using fluorescence intensities

Description

We use FUCCI intensities to infer the position of the cells in cell cycle progression. The result is a vector of angles on a unit circle corresponding to the positions of the cells in cell cycle progression.

Usage

intensity2circle(mat, plot.it = FALSE, method = c("trig", "algebraic"))

Arguments

mat	A matrix of two columns of summarized fluorescence intensity. Rows correspond to samples.
plot.it	TRUE or FALSE. Plot the fitted results.
method	The method used to fit the circle. trig uses trignometry to transform intensity measurements from cartesian coordinates to polar coordinates. algebraic uses an algebraic approach for circle fitting, using the conicfit package.

Value

The inferred angles on unit circle based on the input intensity measurements.

Author(s)

Joyce Hsiao

Examples

# use our data
library(SingleCellExperiment)
data(sce_top101genes)

# FUCCI scores - log10 transformed sum of intensities that were
# corrected for background noise
ints <- colData(sce_top101genes)[,c("rfp.median.log10sum.adjust",
                          "gfp.median.log10sum.adjust")]
intensity2circle(ints, plot.it=TRUE, method = "trig")

---

A SingleCellExperiment object

Description

Pre-computed results. Applied cycle_npreg_outsample and results stored in model_5genes_train to predict cell cycle phase for single-cell samples of NA19098. The predicted cell cycle is stored as variable cellcycle_peco.

Usage

data(model_5genes_predict)

Format

A list with the follwing elements

cellcycle_peco

Predict cell cycle, the values ranged between 0 to 2pi

---

Traing model results among samples from 5 individuals.

Description

Pre-computed results. Applied cycle_npreg_insample to obtain gene-specific cyclic trend parameters using samples from 5 individuals

Usage

data(model_5genes_train)

Format

A list with the follwing elements

Y

a data.frame (gene by sample) of quantile-normailzed gene expression values

theta

a vector of cell cycl phase values (range between 0 to 2pi)

sigma_est

a vector of estimated standard errors

funs_est

a list of estimated cyclic functions

---

Rotate circular variable shift_var to minimize distance between ref_var and shift_var

Description

Because the origin of the cell cycle phases is arbitrary, we transform the angles prior to computing the distance (rotation and shifting) to minimize the distance between two vectors. After this, one can apply circ_dist to compute the distance between the output value and ref_var.

Usage

rotation(ref_var, shift_var)

Arguments

ref_var	A vector of reference angles.
shift_var	A vector of angles to be compared to ref_var.

Value

The transformed values of shift_var after rotation and shifting.

Author(s)

Matthew Stephens

Examples

# a vector of angles
theta_ref <- seq(0,2*pi, length.out=100)

# shift the origin of theta_ref to pi
theta_compare <- shift_origin(theta_ref, origin = pi)

# rotate theta_compare in a such a way that the distance
# between theta_ref and thet_compare is minimized
theta_compare_rotated <- rotation(ref_var=theta_ref, shift_var=theta_compare)

par(mofrow=c(1,2))
plot(x=theta_ref, y = theta_compare)
plot(x=theta_ref, y = theta_compare_rotated)

---

Molecule counts of the 101 significant cyclical genes in the 888 samples analyzed in the study.

Description

A SingleCellExperiment object (require SingleCellExperiment package) including molecule count data after gene and smaple filtering. The 'colData()' slot contains sample phenotype information and the 'rowData()' slot contains gene feature information.

Usage

data(sce_top101genes)

Format

A SingleCellExperiment object with 888 samples and the 101 significant cyclic genes,

theta

Inferred angles of each cell along a circle, also known as FUCCI phase.

---

Shift origin of the angles

Description

Shift origin of the angles for visualization

Usage

shift_origin(phase, origin)

Arguments

phase	A vector of angles (in radians).
origin	the new origin of the angles.

Value

A vector of angles shifted to the new origin.

Author(s)

Joyce Hsiao

Examples

# make a vector of angles
theta <- seq(0,2*pi, length.out=100)

# shift the origin of theta to pi
theta_shifted <- shift_origin(theta, origin = pi)

plot(x=theta, y = theta_shifted)

---

Training data from 888 single-cell samples and 101 top cyclic genes

Description

Pre-computed results. Applied fit_cyclic_many to 888 single-cell samples that have both normalized gene expression values and cell cycle labels to obtain training results that can be used as input for predicting cell cycle phase in other data.

Usage

data(training_human)

Format

A list with the follwing elements

predict.yy

Estimated cyclic expression values in the training data

cellcycle_peco_ordered

Training labels ordered from 0 to 2pi

cell_cycle function

Nonparametric function of cyclic gene expression trend obtained by trendfilter function in genlasso

pve

Proportion of variance explained in each gene by the cell cycl phase label

---