preprocess_acf: Computes Time-Series Autocovariance Function

In nesscoder/TimeCycle: TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts

Description

Computing the autocovariance function from a data.frame of time-series. The resulting autocovariance function is smoothed using a moving average defined by the period and sampling scheme. Returns the smoothed time-series as a data.frame.

Usage

preprocess_acf(data, period = 24, linearTrend = F)

Arguments

data	a data.frame of numeric gene expression over time (row = genes x col = ZT times).
period	a numeric specifying the period of interest in hours for rhythm detection. Default is 24.
linearTrend	a logical scalar. Should TimeCycle Prioritize detecting linear trending signals? Default FALSE. Not recommended to change from default FALSE - will increases false positives rate. See vignette("TimeCycle") for more details.

Value

a smoothed data.frame of numeric gene expression covariance over time (row = genes x col = ZT times).

See Also

meanCenter, scaleTimeSeries

nesscoder/TimeCycle documentation built on June 29, 2021, 5:16 a.m.