Run spline models and test for DE of contrasts.

Run spline models and test for DE of contrasts.

# S4 method for Moanin
DE_timecourse(object, contrasts, center = FALSE, use_voom_weights = TRUE)

Arguments

object	An object of class Moanin, an object containing all related information for time course data and the splines model that will be used (if applicable). See create_moanin_model for more details.
contrasts	Contrasts, either provided as a vector of strings, or a matrix of contrasts coefficients obtained using makeContrasts from the package limma. If given as a character string, will be passed to makeContrasts to be converted into such a matrix.
center	boolean, whether to center the data matrix
use_voom_weights	boolean, optional, default: TRUE. Whether to use voom weights. See details.

Value

A data.frame with two columns for each of the contrasts given in contrasts, corresponding to the raw p-value of the contrast for that gene (_pval) and the adjusted p-value (_qval). The adjusted p-values are FDR-adjusted based on the Benjamini-Hochberg method, as implemented in p.adjust. The adjustment is done across all p-values for all contrasts calculated.

Details

The implementation of the spline fit and the calculation of p-values was based on code from edge, and expanded to enable handling of comparisons of groups via contrasts.#' @seealso makeContrasts, create_moanin_model, DE_timepoints, edge

If use_voom_weights=TRUE, then before fitting splines to each gene, voom weights are calculated from assay(object):

  y <- edgeR::DGEList(counts=assay(object))
  y <- edgeR::calcNormFactors(y, method="upperquartile")
  v <- limma::voom(y, contrasts, plot=FALSE)
  weights <- limma::lmFit(v)

These weights are given to the lm.fit which fits the spline coefficients. This workflow assumes that the input to the Moanin object were counts.

If the user set log_transform=TRUE in the creation of the Moanin object, the splines will be fit to the log of the input data, and not directly to the input data. This is independent of whether the user chooses use_voom_weights.

Examples

data(exampleData)
moanin <- create_moanin_model(data=testData, meta=testMeta)
deTimecourse=DE_timecourse(moanin,
   contrasts="K-C", use_voom_weights=FALSE)
head(deTimecourse)
#>                        K-C_pval     K-C_qval
#> NM_009912          4.202051e-16 3.334961e-15
#> NM_008725          2.235850e-01 2.393844e-01
#> NM_007473          4.941392e-01 5.136583e-01
#> ENSMUST00000094955 1.242532e-17 1.242532e-16
#> NM_001042489       3.261235e-04 5.328816e-04
#> NM_008159          2.885140e-15 2.090681e-14