ttBulk: Brings transcriptomics to the tidyverse

keep_abundant

R Documentation

Filter to keep only abundant transcripts/genes

Description

Filters the data to keep only transcripts/genes that are consistently expressed above a threshold across samples. This is a filtering version of identify_abundant() that removes low-abundance features instead of just marking them.

Usage

keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  design = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'spec_tbl_df'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  design = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'tbl_df'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  design = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'tidybulk'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  design = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'SummarizedExperiment'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  design = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'RangedSummarizedExperiment'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  design = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

Arguments

`.data`	A 'tbl' or 'SummarizedExperiment' object containing transcript/gene abundance data
`.sample`	The name of the sample column
`.transcript`	The name of the transcript/gene column
`.abundance`	The name of the transcript/gene abundance column
`factor_of_interest`	The name of the column containing groups/conditions for filtering. Used by edgeR's filterByExpr to define sample groups.
`design`	A design matrix for more complex experimental designs. If provided, this is passed to filterByExpr instead of factor_of_interest.
`minimum_counts`	A positive number specifying the minimum counts per million (CPM) threshold for a transcript to be kept (default = 10)
`minimum_proportion`	A number between 0 and 1 specifying the minimum proportion of samples that must exceed the minimum_counts threshold (default = 0.7)

Details

\lifecycle

questioning

This function uses edgeR's filterByExpr() function to identify and keep consistently expressed features. A feature is kept if it has CPM > minimum_counts in at least minimum_proportion of samples in at least one experimental group (defined by factor_of_interest or design).

This function is similar to identify_abundant() but instead of adding an .abundant column, it filters out the low-abundance features directly.

Value

Returns a filtered version of the input object containing only the features that passed the abundance threshold criteria.

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A 'SummarizedExperiment' object

References

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

# Basic usage
se_mini |> keep_abundant()

# With custom thresholds
se_mini |> keep_abundant(
  minimum_counts = 5,
  minimum_proportion = 0.5
)

# Using a factor of interest
se_mini |> keep_abundant(factor_of_interest = condition)

stemangiola/ttBulk documentation built on April 12, 2025, 8:43 p.m.