identify_abundant-methods: find abundant transcripts

In tidybulk: Brings transcriptomics to the tidyverse

Description

identify_abundant() takes as input a 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | and returns a 'tbl' with additional columns for the statistics from the hypothesis test.

Usage

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

Arguments

.data	A 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> |
.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 of the factor of interest. This is used for defining sample groups for the filtering process. It uses the filterByExpr function from edgeR.
minimum_counts	A real positive number. It is the threshold of count per million that is used to filter transcripts/genes out from the scaling procedure.
minimum_proportion	A real positive number between 0 and 1. It is the threshold of proportion of samples for each transcripts/genes that have to be characterised by a cmp bigger than the threshold to be included for scaling procedure.

Details

\lifecycle

maturing

At the moment this function uses edgeR (DOI: 10.1093/bioinformatics/btp616)

Underlying method: edgeR::filterByExpr( data, min.count = minimum_counts, group = string_factor_of_interest, min.prop = minimum_proportion )

Value

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

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

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

A 'tbl' 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

Examples

	identify_abundant(
	tidybulk::counts_mini,
	    sample,
	    transcript,
	    `count`
	)

tidybulk documentation built on April 7, 2021, 6 p.m.