identify_abundant {tidybulk} | R Documentation

## find abundant transcripts

### 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

'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 '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::se_mini
    	)

[Package tidybulk version 1.4.0 Index]