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Compute rhythm statistics from fitted models

Source: R/get_stats.R
getRhythmStats.Rd

This function uses stats::optim() to compute various properties of fitted curves with respect to time, potentially in each condition and for each posterior sample, and adjusting for any covariates.

Usage

getRhythmStats(
  fit,
  fitType = c("posterior_mean", "posterior_samples", "raw"),
  features = NULL,
  dopar = TRUE,
  rms = FALSE
)

Arguments

fit

A limorhyde2 object.

fitType

String indicating which fitted models to use to compute the rhythmic statistics. A typical analysis using limorhyde2 will be based on 'posterior_mean', the default.

features

Vector of names, row numbers, or logical values for subsetting the features. NULL indicates all features.

dopar

Logical indicating whether to run calculations in parallel if a parallel backend is already set up, e.g., using doParallel::registerDoParallel(). Recommended to minimize runtime.

rms

Logical indicating whether to calculate rms_amp.

Value

A data.table containing the following rhythm statistics:

  • peak_phase: time between 0 and fit$period at which the peak or maximum value occurs

  • peak_value

  • trough_phase: time between 0 and fit$period at which the trough or minimum value occurs

  • trough_value

  • peak_trough_amp: peak_value - trough_value

  • rms_amp: root mean square difference between fitted curve and mean value between time 0 and fit$period (only calculated if rms is TRUE)

  • mesor: mean value between time 0 and fit$period

The rows of the data.table depend on the fit object and fitType:

  • fit contains data from one condition and fitType is posterior_mean' or 'raw': one row per feature.

  • fit contains data from one condition and fitType is 'posterior_samples': one row per feature per posterior sample.

  • fit contains data from multiple conditions and fitType is 'posterior_mean' or 'raw': one row per feature per condition.

  • fit contains data from multiple conditions and fitType is 'posterior_samples': one row per feature per condition per posterior sample.

See also

getModelFit(), getPosteriorFit(), getPosteriorSamples(), getDiffRhythmStats(), getStatsIntervals()

Examples

library('data.table')

# rhythmicity in one condition
y = GSE54650$y
metadata = GSE54650$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '13869'))

# rhythmicity and differential rhythmicity in multiple conditions
y = GSE34018$y
metadata = GSE34018$metadata

fit = getModelFit(y, metadata, nKnots = 3L, condColname = 'cond')
fit = getPosteriorFit(fit)
rhyStats = getRhythmStats(fit, features = c('13170', '12686'))
diffRhyStats = getDiffRhythmStats(fit, rhyStats)