Package 'greatR'

Calculate distance between sample data before and after registration

Description

calculate_distance() is a function that allows users to calculate pairwise distances between samples from different time points to investigate the similarity of progression before or after registration.

Usage

calculate_distance(results, type = c("registered", "all"), genes_list = NULL)

Arguments

results	Result of registration process using register().
type	Whether to calculate distance considering only "registered" genes (default) or "all" genes.
genes_list	Optional vector indicating the gene_id values to be considered.

Value

This function returns a dist_greatR object containing two data frames:

registered	pairwise distance between scaled reference and query expressions using registered time points.
original	pairwise distance between scaled reference and query expressions using original time points.

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Get approximate stretch factor

Description

get_approximate_stretch() is a function to get a stretch factor estimation given input data. This function will take the time point ranges of both reference and query data and compare them to estimate the stretch factor.

Usage

get_approximate_stretch(data, reference = "ref", query = "query")

Arguments

data	Input data frame, either containing all replicates of gene expression or not.
reference	Accession name of reference data.
query	Accession name of query data.

Value

This function returns an estimation of a stretch factor for registering the data.

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Visualise registration results

Description

Visualise registration results

Usage

## S3 method for class 'res_greatR'
plot(
  x,
  type = c("result", "original"),
  genes_list = NULL,
  show_rep_mean = FALSE,
  ncol = NULL,
  title = NULL,
  ...
)

## S3 method for class 'dist_greatR'
plot(
  x,
  type = c("result", "original"),
  match_timepoints = TRUE,
  title = NULL,
  ...
)

## S3 method for class 'summary.res_greatR'
plot(
  x,
  type = c("all", "registered"),
  type_dist = c("histogram", "density"),
  genes_list = NULL,
  bins = 30,
  alpha = NA,
  scatterplot_size = c(4, 3),
  title = NULL,
  ...
)

Arguments

x	Input object. For plot.res_greatR(): registration results, output of the register() registration process. For plot.summary.res_greatR(): registration results summary, output of summary(). For plot.dist_greatR(): pairwise distances between reference and query time points, output of calculate_distance().
type	Type of plot. For both plot.res_greatR() and plot.dist_greatR(): whether to use registration "result" (default) or "original" time points. For plot.summary.res_greatR(): whether to show "all" genes (default) or only "registered" ones.
genes_list	Optional vector indicating the gene_id values to be plotted.
show_rep_mean	Whether to show replicate mean values.
ncol	Number of columns in the plot grid. By default this is calculated automatically.
title	Optional plot title.
...	Arguments to be passed to methods (ignored).
match_timepoints	If TRUE, will match query time points to reference time points.
type_dist	Type of marginal distribution. Can be either "histogram" (default), or "density".
bins	Number of bins to use when type_dist = "histogram". By default, 30.
alpha	Optional opacity of the points in the scatterplot.
scatterplot_size	Vector c(width, height) specifying the ratio of width and height of the scatterplot with respect to stretch and shift distribution plots.

Value

• For plot.res_greatR(): plot of genes of interest after registration process (type = "result") or showing original time points (type = "original").

• For plot.dist_greatR(): distance heatmap of gene expression profiles over time between reference and query.

• For plot.summary.res_greatR(): TODO.

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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
)

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.

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.

Examples

## Not run: 
# 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"
)

## End(Not run)

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Summarise registration results

Description

Summarise registration results

Usage

## S3 method for class 'res_greatR'
summary(object, ...)

Arguments

object	Registration results, output of the register() registration process.
...	Arguments to be passed to methods (ignored).

Value

This function returns a list containing:

summary	table containing the summary of the registration results.
registered_genes	vector of gene accessions which were successfully registered.
non_registered_genes	vector of non-registered gene accessions.
reg_params	table containing distribution of registration parameters.

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