Identify abundant transcripts/genes in a per-category manner

Identifies transcripts/genes that are consistently expressed above a threshold across samples. This function adds a logical column `.abundant` to indicate which features pass the filtering criteria.

identify_abundant_per_category(
  .data,
  abundance = assayNames(.data)[1],
  design = NULL,
  formula_design = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7,
  minimum_count_per_million = NULL,
  minimum_category = 1,
  force = FALSE,
  coerce_design = FALSE,
  ...
)

# S4 method for class 'SummarizedExperiment'
identify_abundant_per_category(
  .data,
  abundance = assayNames(.data)[1],
  design = NULL,
  formula_design = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7,
  minimum_count_per_million = NULL,
  minimum_category = 1,
  force = FALSE,
  coerce_design = FALSE,
  ...
)

# S4 method for class 'RangedSummarizedExperiment'
identify_abundant_per_category(
  .data,
  abundance = assayNames(.data)[1],
  design = NULL,
  formula_design = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7,
  minimum_count_per_million = NULL,
  minimum_category = 1,
  force = FALSE,
  coerce_design = FALSE,
  ...
)

Arguments

.data

A `SummarizedExperiment` object containing transcript/gene abundance data

abundance

The name of the transcript/gene abundance column (character, preferred)

design

A design matrix for more complex experimental designs.

formula_design

A formula to generate the design matrix. Overrides `design` when both are provided

minimum_counts

The minimum count threshold for a feature to be considered abundant

minimum_proportion

The minimum proportion of samples in which a feature must be abundant

minimum_count_per_million

Minimum CPM cutoff to use for filtering. If provided, this will override the minimum_counts parameter. Default is NULL.

minimum_category

The minimum number of categories/experimental groups that have sufficient abundance samples.

force

Should existing .abundant column be replaced; defaults to FALSE.

coerce_design

Should non-categorical design matrix be coerced; defaults to FALSE.

...

Further arguments taken for compatibility, but are not used.

Value

Returns the input object with an additional logical column `.abundant` indicating which features passed the abundance threshold criteria.

Details

This function provides an alternative filtering solution for edgeR's filterByExpr() function to identify consistently expressed features. A feature is considered abundant if it has CPM > minimum_counts in at least minimum_proportion of samples in at least minimum_category experimental groups (defined by design/formula_design).

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

McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research, 40(10), 4288-4297. DOI: 10.1093/bioinformatics/btp616

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


# Basic usage
airway |> identify_abundant_per_category()
#> Warning: Neither formula_design and design provided; build design from formula ~ 1 .
#> class: RangedSummarizedExperiment 
#> dim: 63677 8 
#> metadata(1): ''
#> assays(1): counts
#> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
#>   ENSG00000273493
#> rowData names(11): gene_id gene_name ... symbol .abundant
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(10): SampleName cell ... BioSample condition

# With custom thresholds
airway |> identify_abundant_per_category(
  minimum_counts = 5,
  minimum_proportion = 0.5
)
#> Warning: Neither formula_design and design provided; build design from formula ~ 1 .
#> class: RangedSummarizedExperiment 
#> dim: 63677 8 
#> metadata(1): ''
#> assays(1): counts
#> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
#>   ENSG00000273493
#> rowData names(11): gene_id gene_name ... symbol .abundant
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(10): SampleName cell ... BioSample condition

# Using a factor of interest
airway |> identify_abundant_per_category(formula_design = ~condition)
#> class: RangedSummarizedExperiment 
#> dim: 63677 8 
#> metadata(1): ''
#> assays(1): counts
#> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
#>   ENSG00000273493
#> rowData names(11): gene_id gene_name ... symbol .abundant
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(10): SampleName cell ... BioSample condition