Normalise the count data for DGE.

Calculates the normalisation factors and applies voom on the filtered counts from qc_bulk_dge(), both in Rust via the edge-rs crate. Can additionally calculate TPM and FPKM values for plotting purposes.

Usage

normalise_bulk_dge(
  object,
  group_col,
  norm_method = c("TMM", "TMMwsp", "RLE", "upperquartile", "none"),
  calc_tpm = FALSE,
  calc_fpkm = FALSE,
  gene_lengths = NULL,
  .verbose = TRUE
)

Arguments

object

The underlying class, see BulkDge().

group_col

String. The column in the metadata that will contain the contrast groups. Needs to be part of the metadata stored in the class.

norm_method

String. One of c("TMM", "TMMwsp", "RLE", "upperquartile", "none"). Please refer to edgeR's calcNormFactors().

calc_tpm

Boolean. Output TPM calculation (default = FALSE).

calc_fpkm

Boolean. Output FPKM calculation (default = FALSE).

gene_lengths

Optional named numeric. If you want to calculate TPM or FPKM you need to provide this one. The names need to be the same identifier as used in the counts.

.verbose

Boolean. Controls the verbosity of the function.

Value

Returns the class with the processed_data data slot populated and applied parameters added to the params slot.

Examples

# TMM library size normalisation followed by voom
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
)
get_outputs(object)$normalised_counts[1:3, 1:3]
#>        sample_1 sample_10 sample_100
#> gene_1 9.438292  8.938609   8.962916
#> gene_2 3.372203  3.323899   6.404920
#> gene_3 5.694131  6.131254   9.538776