identify_abundant-methods: Identify abundant transcripts/genes

In stemangiola/ttBulk: Brings transcriptomics to the tidyverse

Identify abundant transcripts/genes

Description

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.

Usage

identify_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'
identify_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'
identify_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'
identify_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'
identify_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'
identify_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 considered abundant (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

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

This function uses 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 one experimental group (defined by factor_of_interest or design).

Value

Returns the input object with an additional logical column '.abundant' indicating which features 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 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 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

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 |> identify_abundant()

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

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

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