Normalise by quantiles the counts of transcripts/genes

Source: R/quantile_normalise_abundance.R

quantile_normalise_abundance() takes as input A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and Scales transcript abundance compansating for sequencing depth (e.g., with TMM algorithm, Robinson and Oshlack doi.org/10.1186/gb-2010-11-3-r25).

quantile_normalise_abundance(
  .data,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  target_distribution = NULL
)

# S4 method for class 'SummarizedExperiment'
quantile_normalise_abundance(
  .data,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  target_distribution = NULL
)

# S4 method for class 'RangedSummarizedExperiment'
quantile_normalise_abundance(
  .data,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  target_distribution = NULL
)

Arguments

.data

A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment))

.abundance

The name of the transcript/gene abundance column

method

A character string. Either "limma_normalize_quantiles" for limma::normalizeQuantiles or "preprocesscore_normalize_quantiles_use_target" for preprocessCore::normalize.quantiles.use.target for large-scale datasets.

target_distribution

A numeric vector. If NULL the target distribution will be calculated by preprocessCore. This argument only affects the "preprocesscore_normalize_quantiles_use_target" method.

Value

A tbl object with additional columns with scaled data as `<NAME OF COUNT COLUMN>_scaled`

A `SummarizedExperiment` object

A `SummarizedExperiment` object

Details

`r lifecycle::badge("maturing")`

Tranform the feature abundance across samples so to have the same quantile distribution (using preprocessCore).

Underlying method

If `limma_normalize_quantiles` is chosen

.data |>limma::normalizeQuantiles()

If `preprocesscore_normalize_quantiles_use_target` is chosen

.data |> preprocessCore::normalize.quantiles.use.target( target = preprocessCore::normalize.quantiles.determine.target(.data) )

References

Mangiola, S., Molania, R., Dong, R., Doyle, M. A., & Papenfuss, A. T. (2021). tidybulk: an R tidy framework for modular transcriptomic data analysis. Genome Biology, 22(1), 42. doi:10.1186/s13059-020-02233-7

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007

Examples

## Load airway dataset for examples

  data('airway', package = 'airway')
  # Ensure a 'condition' column exists for examples expecting it

    SummarizedExperiment::colData(airway)$condition <- SummarizedExperiment::colData(airway)$dex




 airway |>
   quantile_normalise_abundance()
#> class: RangedSummarizedExperiment 
#> dim: 63677 8 
#> metadata(2): '' tidybulk
#> assays(2): counts counts_scaled
#> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
#>   ENSG00000273493
#> rowData names(10): gene_id gene_name ... seq_coord_system symbol
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(10): SampleName cell ... BioSample condition