Normalise by quantiles the counts of transcripts/genes

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,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  target_distribution = NULL,
  action = "add"
)

# S4 method for class 'spec_tbl_df'
quantile_normalise_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  target_distribution = NULL,
  action = "add"
)

# S4 method for class 'tbl_df'
quantile_normalise_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  target_distribution = NULL,
  action = "add"
)

# S4 method for class 'tidybulk'
quantile_normalise_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  target_distribution = NULL,
  action = "add"
)

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

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

.sample

The name of the sample column

.transcript

The name of the transcript/gene column

.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.

action

A character string between "add" (default) and "only". "add" joins the new information to the input tbl (default), "only" return a non-redundant tbl with the just new information.

Value

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

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

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

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) )

Examples

 tidybulk::se_mini |>
   quantile_normalise_abundance()
#> class: SummarizedExperiment 
#> dim: 527 5 
#> metadata(0):
#> assays(2): count count_scaled
#> rownames(527): ABCB4 ABCB9 ... ZNF324 ZNF442
#> rowData names(1): entrez
#> colnames(5): SRR1740034 SRR1740035 SRR1740043 SRR1740058 SRR1740067
#> colData names(5): Cell.type time condition days dead