remove_redundancy {tidybulk} | R Documentation

## Drop redundant elements (e.g., samples) for which feature (e.g., transcript/gene) abundances are correlated

### Description

remove_redundancy() takes as input a 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | for correlation method or | <DIMENSION 1> | <DIMENSION 2> | <...> | for reduced_dimensions method, and returns a 'tbl' with dropped elements (e.g., samples).

### Usage

    remove_redundancy(
      .data,
      .element = NULL,
      .feature = NULL,
      .abundance = NULL,
      method,
      of_samples = TRUE,
      correlation_threshold = 0.9,
      top = Inf,
      log_transform = FALSE,
      Dim_a_column,
      Dim_b_column
    )

    ## S4 method for signature 'spec_tbl_df'
    remove_redundancy(
      .data,
      .element = NULL,
      .feature = NULL,
      .abundance = NULL,
      method,
      of_samples = TRUE,
      correlation_threshold = 0.9,
      top = Inf,
      log_transform = FALSE,
      Dim_a_column = NULL,
      Dim_b_column = NULL
    )

    ## S4 method for signature 'tbl_df'
    remove_redundancy(
      .data,
      .element = NULL,
      .feature = NULL,
      .abundance = NULL,
      method,
      of_samples = TRUE,
      correlation_threshold = 0.9,
      top = Inf,
      log_transform = FALSE,
      Dim_a_column = NULL,
      Dim_b_column = NULL
    )

    ## S4 method for signature 'tidybulk'
    remove_redundancy(
      .data,
      .element = NULL,
      .feature = NULL,
      .abundance = NULL,
      method,
      of_samples = TRUE,
      correlation_threshold = 0.9,
      top = Inf,
      log_transform = FALSE,
      Dim_a_column = NULL,
      Dim_b_column = NULL
    )

    ## S4 method for signature 'SummarizedExperiment'
    remove_redundancy(
      .data,
      .element = NULL,
      .feature = NULL,
      .abundance = NULL,
      method,
      of_samples = TRUE,
      correlation_threshold = 0.9,
      top = Inf,
      log_transform = FALSE,
      Dim_a_column = NULL,
      Dim_b_column = NULL
    )

    ## S4 method for signature 'RangedSummarizedExperiment'
    remove_redundancy(
      .data,
      .element = NULL,
      .feature = NULL,
      .abundance = NULL,
      method,
      of_samples = TRUE,
      correlation_threshold = 0.9,
      top = Inf,
      log_transform = FALSE,
      Dim_a_column = NULL,
      Dim_b_column = NULL
    )

### Arguments

`.data` | A 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> |
`.element` | The name of the element column (normally samples).
`.feature` | The name of the feature column (normally transcripts/genes)
`.abundance` | The name of the column including the numerical value the clustering is based on (normally transcript abundance)
`method` | A character string. The cluster algorithm to use, ay the moment k-means is the only algorithm included.
`of_samples` | A boolean. In case the input is a tidybulk object, it indicates Whether the element column will be sample or transcript column
`correlation_threshold` | A real number between 0 and 1. For correlation based calculation.
`top` | An integer. How many top genes to select for correlation based method
`log_transform` | A boolean, whether the value should be log-transformed (e.g., TRUE for RNA sequencing data)
`Dim_a_column` | A character string. For reduced_dimension based calculation. The column of one principal component
`Dim_b_column` | A character string. For reduced_dimension based calculation. The column of another principal component

### Value

A tbl object with with dropped redundant elements (e.g., samples).

A tbl object with with dropped redundant elements (e.g., samples).

A tbl object with with dropped redundant elements (e.g., samples).

A tbl object with with dropped redundant elements (e.g., samples).

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

### Examples

     tidybulk::se_mini %>%
     identify_abundant() %>%
       remove_redundancy(
    	   .element = sample,
    	   .feature = transcript,
    	   	.abundance =  count,
    	   	method = "correlation"
    	   	)

    counts.MDS =
     tidybulk::se_mini %>%
     identify_abundant() %>%
      reduce_dimensions( method="MDS", .dims = 3)

    remove_redundancy(
    	counts.MDS,
    	Dim_a_column = `Dim1`,
    	Dim_b_column = `Dim2`,
    	.element = sample,
      method = "reduced_dimensions"
    )

[Package tidybulk version 1.4.0 Index]