Calculates the Hedge's G effect size

This function will calculate the Hedge's G effect size on the normalised counts. Should batch-corrected counts be found, these will be used. At a minimum you will need to provide contrast_column that can be found in the meta-data. If you do not provide a vector of contrasts that you wish to test for, every permutation of groups represented in that column will be tested against each other.

Usage

calculate_dge_hedges(
  object,
  contrast_column,
  contrast_list = NULL,
  filter_column = NULL,
  .verbose = TRUE
)

Arguments

object

The underlying class, see BulkDge().

contrast_column

String. The contrast column in which the groupings are stored. Needs to be found in the meta_data within the properties.

contrast_list

Optional string vector. A vectors that contains the contrast formatted as "contrast1-contrast2". Default NULL will create all possible contrast automatically.

filter_column

Optional String. If there is a column you wish to use as sub groupings, this can be provided here. An example could be different sampled tissues and you wish to run the DGE analyses within each tissue separately in the data.

.verbose

Controls verbosity of the function.

Value

Returns the class with additional data added to the outputs.

Examples

# effect sizes over every contrast in the case_control column
syn <- synthetic_bulk_cor_matrix()
meta <- data.table::data.table(
  sample_id = colnames(syn$counts),
  case_control = rep(c("case", "control"), each = 50)
)
object <- BulkDge(raw_counts = syn$counts, meta_data = meta)
object <- qc_bulk_dge(object, group_col = "case_control", .verbose = FALSE)
object <- normalise_bulk_dge(
  object,
  group_col = "case_control",
  .verbose = FALSE
)
object <- calculate_dge_hedges(
  object,
  contrast_column = "case_control",
  .verbose = FALSE
)
head(get_dge_effect_sizes(object))
#>    effect_sizes standard_errors gene_id     combination subgroup
#>           <num>           <num>  <char>          <char>   <lgcl>
#> 1:   -0.4541874       0.2046602  gene_1 case_vs_control       NA
#> 2:   -0.2674739       0.2029738  gene_2 case_vs_control       NA
#> 3:   -0.4256828       0.2043474  gene_3 case_vs_control       NA
#> 4:   -0.3793411       0.2038811  gene_4 case_vs_control       NA
#> 5:   -0.2601789       0.2029254  gene_5 case_vs_control       NA
#> 6:   -0.4551123       0.2046707  gene_6 case_vs_control       NA