fit_bspline: Use bsplies to cyclic trend of gene expression levels

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

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)

peco documentation built on Nov. 8, 2020, 8:16 p.m.