greatR (version 2.0.0)

register: Register or synchronize different expression profiles

Description

register() is a function to register expression profiles a user wishes to compare.

Usage

register(
  input,
  stretches = NA,
  shifts = NA,
  reference,
  query,
  scaling_method = c("none", "z-score", "min-max"),
  overlapping_percent = 50,
  use_optimisation = TRUE,
  optimisation_method = c("lbfgsb", "nm", "sa"),
  optimisation_config = NULL,
  exp_sd = NA,
  num_cores = NA
)

Value

This function returns a res_greatR object containing:

data

a table containing the scaled input data and an additional timepoint_reg column after applying registration parameters to the query data.

model_comparison

a table comparing the optimal registration function for each gene (based on all_shifts_df scores) to model with no registration applied.

fun_args

a list of arguments used when calling the function.

Arguments

input

Input data frame containing all replicates of gene expression in each genotype at each time point.

stretches

Candidate registration stretch factors to apply to query data, only required if use_optimisation = FALSE.

shifts

Candidate registration shift values to apply to query data, only required if use_optimisation = FALSE.

reference

Accession name of reference data.

query

Accession name of query data.

scaling_method

Scaling method applied to data prior to registration process. Either none (default), z-score, or min-max.

overlapping_percent

Minimum percentage of overlapping time point range of the reference data. Shifts will be only considered if it leaves at least this percentage of overlapping time point range after applying the registration.

use_optimisation

Whether to optimise registration parameters. By default, TRUE.

optimisation_method

Optimisation method to use. Either "lbfgsb" for L-BFGS-B (default), "nm" for Nelder-Mead, or "sa" for Simulated Annealing.

optimisation_config

Optional list with arguments to override the default optimisation configuration.

exp_sd

Optional experimental standard deviation on the expression replicates.

num_cores

Number of cores to use if the user wants to register genes asynchronously (in parallel) in the background on the same machine. By default, NA, the registration will be run without parallelisation.

Examples

if (FALSE) {
# Load a data frame from the sample data
data_path <- system.file("extdata/brapa_arabidopsis_data.csv", package = "greatR")
all_data <- utils::read.csv(data_path)

# Running the registration
registration_results <- register(
  input = all_data,
  reference = "Ro18",
  query = "Col0"
)
}