Description Usage Arguments Value Author(s) See Also Examples

View source: R/fit_cyclic_many.R

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.

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 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. |

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. |

Joyce Hsiao

`fit_trendfilter`

for fitting one gene
using trendfilter

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)$pve_fucci,
decreasing=TRUE)[1:10],]
coldata <- colData(sce_top10)
# cell cycle phase based on FUCCI scores
theta <- coldata$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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