genSmoothCurves: Fit smooth spline curves and return the response matrix

In monocle: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq

Description

This function will fit smooth spline curves for the gene expression dynamics along pseudotime in a gene-wise manner and return the corresponding response matrix. This function is build on other functions (fit_models and responseMatrix) and used in calILRs and calABCs functions

Usage

genSmoothCurves(cds, new_data, trend_formula = "~sm.ns(Pseudotime, df = 3)",
  relative_expr = T, response_type = "response", cores = 1)

Arguments

cds	a CellDataSet object upon which to perform this operation
new_data	a data.frame object including columns (for example, Pseudotime) with names specified in the model formula. The values in the data.frame should be consist with the corresponding values from cds object.
trend_formula	a formula string specifying the model formula used in fitting the spline curve for each gene/feature.
relative_expr	a logic flag to determine whether or not the relative gene expression should be used
response_type	the response desired, as accepted by VGAM's predict function
cores	the number of cores to be used while testing each gene for differential expression

Value

a data frame containing the data for the fitted spline curves.

monocle documentation built on Nov. 8, 2020, 5:06 p.m.