circan: circan

In AndreaRP/CircaN: Identification Of Circadian Patterns In Omics Data

Description

Nonlinear least squares model for accurate detection of circadian expression patterns.

Usage

circan(
  data,
  meta,
  shiny = FALSE,
  mode = "default",
  init_value = 24,
  max_per = Inf,
  min_per = -Inf
)

Arguments

data	Dataframe containing the expression data. Samples must be in columns and genes in rows. For an example see data(expression_example).
meta	Dataframe containing the metadata for the samples. Must have at least a 'sample' column with the sample name as it appears in the data matrix; a 'time' column with the time point the sample was collected; and an 'ind' column containing information for the individual the sample comes from. For an example see data(metadata_example).
shiny	Is the package running in a shiny app? default to FALSE.
mode	Algorithm to use in the NLS regression. Must be one of 'default' for Gauss-Newton, 'plinear' for the Golub-Pereyra algorithm for partially linear least-squares models and 'port' for the ‘nl2sol’ algorithm from the Port library. Default is default. See nls documentation for extended info.
init_value	Initial value for the period. Default is set to 24.
max_per	Maximum period to regress. Default is set to Inf.
min_per	Minimum period to regress. Default is set to -Inf.

Examples

# This runs CircaN on the example data with the 'Port' algorithm.
circan(data=expression_example, meta=metadata_example, mode="port")

AndreaRP/CircaN documentation built on Jan. 25, 2021, 5:49 a.m.