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Bixverse implementation of singscore (single gene set)

calc_singscore.Rd

Scores all samples against one up-regulated gene set with an optional paired down-regulated set. When n_permutations > 0, additionally runs a permutation test and includes empirical p-values.

Usage

calc_singscore(
  ranks,
  up_set,
  down_set = NULL,
  center_score = TRUE,
  known_direction = TRUE,
  n_permutations = 0L,
  seed = 42L
)

Arguments

ranks

Numerical matrix. Output of calc_singscore_rank().

up_set

Character vector. Gene names of the up-regulated set.

down_set

Character vector or NULL. Optional paired down-regulated set.

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.

n_permutations

Integer. Number of permutations. 0 disables the permutation test.

seed

Integer. RNG seed for the permutation test.

Value

A data.table with one row per sample. Columns: total_score, total_dispersion, optionally up_score, up_dispersion, down_score, down_dispersion, and (when permutations are run) pval. With permutations, the null distribution is attached as attr(., "null_distribution").

References

Foroutan et al., BMC Bioinformatics, 2018.

Examples

# score every sample against one up-regulated gene set
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)
head(calc_singscore(ranks, up_set = sprintf("gene_%03i", 1:20)))
#>    total_score total_dispersion sample_id
#>          <num>            <num>    <char>
#> 1:  0.05611111         78.57792  sample_1
#> 2: -0.01944444         61.52799  sample_2
#> 3: -0.07138889         57.08019  sample_3
#> 4: -0.08083333         49.66717  sample_4
#> 5:  0.01888889         57.82149  sample_5
#> 6:  0.03388889         74.87141  sample_6