smoothGeneProfileByPenalizedSpline: Smooth gene expression by fitting penalized splines using...

In haiyueliu/Eskrate: Estimate time-dependent RNA kinetics

Smooth gene expression by fitting penalized splines using general additive models.

Description

Smooth gene expression by fitting penalized splines using general additive models.

Usage

smoothGeneProfileByPenalizedSpline(
  dataConcentration,
  numberOfAnchorPoints = 20,
  gamma = 1.4,
  isInputConcentrations = TRUE,
  ignoreOutliers = FALSE,
  outlierIQRfactor = 3
)

Arguments

dataConcentration	data frame of normalized gene expression for m_u,: genes on rows and sorted cells on columns
numberOfAnchorPoints	integer the number of anchors used for the spline fitting (default: 20)
gamma	numeric a number that will be passed to GAM model to adjust the over-fitting (defaut: 1, use 1.4 if you want to adjust overfit)
isInputConcentrations	logic
ignoreOutliers	logic (default: TRUE)
outlierIQRfactor	numeric (default: 3)

Value

a data frame with smoothed values in columns and sorted cells order in rows

Examples

data_spline <- smoothGeneProfileByPenalizedSpline(dataConcentration=observed.data["PCNA",], numberOfAnchorPoints = 20, gamma=1.4)

haiyueliu/Eskrate documentation built on Sept. 3, 2023, 3:33 p.m.