Skip to contents

Bixverse implementation of singscore (multiple gene sets)

calc_singscore_multi.Rd

Scores all samples against many up-regulated gene sets with optional paired down-regulated sets. Down sets are paired with up sets by name; unmatched names are dropped.

Usage

calc_singscore_multi(
  ranks,
  up_pathways,
  down_pathways = NULL,
  center_score = TRUE,
  known_direction = TRUE,
  min_size = 1L,
  max_size = 500L
)

Arguments

ranks

Numerical matrix. Output of calc_singscore_rank().

up_pathways

Named list of character vectors. Up-regulated gene sets.

down_pathways

Named list or NULL. Paired down-regulated gene sets.

center_score

Boolean. Centre scores around 0. Ignored when known_direction = FALSE.

known_direction

Boolean. Whether the up-set direction is known. Becomes irrelevant when down_set is also provided.

min_size

Integer. Minimum gene-set size after dropping missing genes.

max_size

Integer. Maximum gene-set size.

Value

A named list with two matrices, scores and dispersions, each of shape gene_sets × samples.

References

Foroutan et al., BMC Bioinformatics, 2018.

Examples

# score every sample against several up-regulated 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))
)
ranks <- calc_singscore_rank(exp_mat)
pathways <- list(
  set_a = sprintf("gene_%03i", 1:20),
  set_b = sprintf("gene_%03i", 50:80)
)
res <- calc_singscore_multi(ranks, up_pathways = pathways)
dim(res$scores)
#> [1]  2 10