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Calibrate the blitzGSEA null model for a signature

blitzgsea_calibrate.Rd

Draws random gene sets across a log-spaced grid of set sizes, fits gamma tails to the resulting enrichment scores and smooths the fitted parameters across sizes, see Lachmann, et al. No gene set library enters here, so one calibration serves every library you score against that signature. This is where nearly all of the runtime sits; scoring afterwards costs one gamma tail evaluation per pathway.

The returned null is a plain list, so it survives saveRDS() and can be cached against a signature.

Usage

blitzgsea_calibrate(stats, blitz_params = params_blitzgsea())

Arguments

stats

Named numeric vector. The gene level statistic. Sorted internally, so the order you hand it in does not matter.

blitz_params

List. The blitzGSEA parameters, see params_blitzgsea() wrapper function.

Value

An object of class BlitzGseaNull, a list with the following elements:

  • anchor_sizes - Numeric vector. The anchor set sizes, ascending.

  • shape_pos - Numeric vector. Smoothed positive-tail gamma shape.

  • scale_pos - Numeric vector. Smoothed positive-tail gamma scale.

  • shape_neg - Numeric vector. Smoothed negative-tail gamma shape.

  • scale_neg - Numeric vector. Smoothed negative-tail gamma scale.

  • pos_ratio - Numeric vector. Smoothed fraction of positive null scores at each anchor.

  • ks_pos - Float. Mean goodness-of-fit p-value for the positive tail.

  • ks_neg - Float. Mean goodness-of-fit p-value for the negative tail.

  • centred - Boolean. Whether the signature was centred.

  • n_genes - Integer. Size of the signature it was calibrated on.

References

Lachmann, et al., Bioinformatics, 2022

Examples

# calibrate a null once, to be reused across gene set libraries
set.seed(42L)
stats <- stats::setNames(rnorm(500), sprintf("gene_%03i", 1:500))
null_model <- blitzgsea_calibrate(
  stats,
  blitz_params = params_blitzgsea(permutations = 1000L, anchors = 10L)
)
null_model
#> BlitzGseaNull (calibrated blitzGSEA null model)
#>   Signature:        500 genes
#>   Anchors:          10 (sizes 1 to 250)
#>   Centred:          TRUE
#>   KS p-value:       0.404 positive tail, 0.297 negative tail