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()
#> # A SummarizedExperiment-tibble abstraction: Features=63677 | Samples=8 | 
#> #   Assays=counts, counts_scaled
#> #                                                 |--------- COVARIATES -------|
#>        .feature    .sample | counts counts_scaled | SampleName cell  dex   albut
#>        <chr>       <chr>   | <chr>  <chr>         | <fct>      <fct> <fct> <fct>
#> 1      ENSG000000… SRR103… | 679    691.25        | GSM1275862 N613… untrt untrt
#> 2      ENSG000000… SRR103… | 0      0             | GSM1275862 N613… untrt untrt
#> 3      ENSG000000… SRR103… | 467    469.375       | GSM1275862 N613… untrt untrt
#> 4      ENSG000000… SRR103… | 260    257.75        | GSM1275862 N613… untrt untrt
#> 5      ENSG000000… SRR103… | 60     58.625        | GSM1275862 N613… untrt untrt
#>        --------    ------- - ------ ------------- - ---------- ----  ---   -----
#> 509412 ENSG000002… SRR103… | 0      0             | GSM1275875 N061… trt   untrt
#> 509413 ENSG000002… SRR103… | 0      0             | GSM1275875 N061… trt   untrt
#> 509414 ENSG000002… SRR103… | 0      0             | GSM1275875 N061… trt   untrt
#> 509415 ENSG000002… SRR103… | 0      0             | GSM1275875 N061… trt   untrt
#> 509416 ENSG000002… SRR103… | 0      0             | GSM1275875 N061… trt   untrt
#> # ℹ 17 more variables: Run <fct>, avgLength <chr>, Experiment <fct>,
#> #   Sample <fct>, BioSample <fct>, condition <fct>, `|` <|>, gene_id <chr>,
#> #   gene_name <chr>, entrezid <chr>, gene_biotype <chr>, gene_seq_start <chr>,
#> #   gene_seq_end <chr>, seq_name <chr>, seq_strand <chr>,
#> #   seq_coord_system <chr>, symbol <chr>