Skip to contents

Calculates the Limma Voom DGE

calculate_dge_limma.Rd

This function will apply the Limma Voom DGE workflow. 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_limma(
  object,
  contrast_column,
  contrast_list = NULL,
  filter_column = NULL,
  co_variates = NULL,
  limma_params = params_limma_voom(),
  .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.

co_variates

Optional string vector. Any co-variates you wish to consider during the Limma Voom modelling.

limma_params

List. The limma parameters, see params_limma_voom().

.verbose

Controls verbosity of the function.

Value

Returns the class with additional data added to the outputs.

Examples

# limma voom 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_limma(
  object,
  contrast_column = "case_control",
  .verbose = FALSE
)
head(get_dge_limma_voom(object))
#>     gene_id      logFC        CI.L        CI.R   AveExpr         t     P.Value
#>      <char>      <num>       <num>       <num>     <num>     <num>       <num>
#> 1: gene_589  0.2246372  0.08408076  0.36519360 10.469973  3.169891 0.002011843
#> 2:  gene_25 -0.6156898 -1.03578573 -0.19559385  9.230661 -2.906878 0.004475873
#> 3: gene_894  0.2106366  0.05376385  0.36750934 10.165859  2.663175 0.008992482
#> 4: gene_691  0.1905472  0.04817933  0.33291502 10.089603  2.654630 0.009208069
#> 5: gene_649 -0.2152072 -0.37767245 -0.05274193 10.033452 -2.627300 0.009929367
#> 6:  gene_24 -0.2383683 -0.42102215 -0.05571450 10.247703 -2.588410 0.011043868
#>    adj.P.Val         B        contrast subgroup
#>        <num>     <num>          <char>   <lgcl>
#> 1: 0.7339682 -2.136281 case_vs_control       NA
#> 2: 0.7339682 -3.252944 case_vs_control       NA
#> 3: 0.7339682 -3.072213 case_vs_control       NA
#> 4: 0.7339682 -3.113507 case_vs_control       NA
#> 5: 0.7339682 -3.175555 case_vs_control       NA
#> 6: 0.7339682 -3.150449 case_vs_control       NA