identify_abundant-methods: find abundant transcripts

In stemangiola/tidybulk: Brings transcriptomics to the tidyverse

find abundant transcripts

Description

identify_abundant() 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 returns a consistent object (to the input) with additional columns for the statistics from the hypothesis test.

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' (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
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

'r lifecycle::badge("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 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 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

Examples

	identify_abundant(
	tidybulk::se_mini
	)

stemangiola/tidybulk documentation built on Dec. 17, 2024, 11:12 p.m.