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Bixverse implementation of GSVA

calc_gsva.Rd

Implementation of the bixverse version of the gene set variation analysis (GSVA), see Hänzelmann, et al.

Usage

calc_gsva(
  exp,
  pathways,
  kernel = c("gaussian", "poisson", "none"),
  gaussian = deprecated(),
  gsva_params = params_gsva(),
  .verbose = FALSE
)

Arguments

exp

Numerical matrix. Rows represents the features, columns the features/genes.

pathways

List. A named list with each element containing the genes for this pathway.

kernel

String. One of c("gaussian", "poisson", "none"). The kernel to use.

gaussian

Boolean. If set to TRUE the Gaussian kernel will be used, if FALSE the Poisson will be used. [Deprecated]

gsva_params

List. The GSVA parameters, see params_gsva() wrapper function. This function generates a list containing:

  • tau - Float. The tau parameter of the algorithm. Large values will emphasise the tails more. Defaults to 1.0.

  • min_size - Integer. Minimum size for the gene sets.

  • max_size - Integer. Maximum size for the gene sets.

  • max_diff - Boolean. Influences the scoring. If TRUE the difference will be used; if FALSE, the largest absolute value.

  • abs_rank - Boolean. If TRUE = pos-neg, FALSE = pos+neg for the internal calculations.

.verbose

Boolean. Controls verbosity.

Value

A matrix of shape pathways (that passed the thresholds) x samples.

References

see Hänzelmann, et al. Bmc Bioinformatics, 2013

Examples

# per-sample GSVA scores for two gene sets
set.seed(123L)
exp_mat <- matrix(
  rnorm(200 * 10),
  nrow = 200,
  dimnames = list(sprintf("gene_%03i", 1:200), sprintf("sample_%i", 1:10))
)
pathways <- list(
  set_a = sprintf("gene_%03i", 1:20),
  set_b = sprintf("gene_%03i", 50:80)
)
dim(calc_gsva(exp_mat, pathways))
#> [1]  2 10