Description Usage Arguments Value Author(s) See Also Examples
View source: R/fit_cyclic_many.R
For each gene, apply trend filtering as implemented in
the genlasso package to estimate cell cycle. For more details, see
link{fit_trendfiltering}
.
1 | fit_cyclical_many(Y, theta, polyorder = 2, ncores = 2)
|
Y |
A matrix (gene by sample) of gene expression values. The expression values are assumed to have been normalized and transformed to the standard normal distribution. |
theta |
A vector of cell cycle phase (angles) for single-cell samples. |
polyorder |
Argument passed to |
ncores |
Argument passed to
|
A list containing the following elements:
predict.yy |
A matrix of predicted expression values at observed cell cycle. |
cellcycle_peco_ordered |
A vector of predicted cell cycle. Values range between 0 to 2pi |
cellcycle_function |
List of predicted cell cycle functions. |
pve |
Vector of proportion of variance explained in each gene by the predicted cell cycle. |
Joyce Hsiao
fit_trendfilter
for fitting one gene
using trend filtering.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | library(SingleCellExperiment)
data(sce_top101genes)
# Select top 10 cyclic genes.
sce_top10 <- sce_top101genes[order(rowData(sce_top101genes)$pve_fucci,
decreasing=TRUE)[1:10],]
coldata <- colData(sce_top10)
# Get cell cycle phase based on FUCCI scores.
theta <- coldata$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)
|
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