Rename columns in the sample or variable information tibble

These two functions provide a way to rename columns in the sample or variable information of an experiment() or SummarizedExperiment.

The same syntax as dplyr::rename() is used. For example, to rename the "group" column in the sample information tibble to "condition", use rename_col(exp, condition = group). Note that you can't rename the "sample" column in the sample information tibble, as well as the "variable" column in the variable information tibble. These two columns are used to link the sample or variable information tibble to the expression matrix.

Usage

rename_col(exp, ...)

rename_row(exp, ...)

Arguments

exp: An experiment() or SummarizedExperiment object.
...: <data-masking> Name pairs to rename. Use new_name = old_name to rename columns.

Value

An object of the same class as exp.

Identifier columns

For an experiment() object, sample is a physical column in sample_info, and variable is a physical column in var_info.

For a SummarizedExperiment, sample and variable identifiers live in colnames(exp) and rownames(exp), rather than in SummarizedExperiment::colData() or SummarizedExperiment::rowData(). Observation verbs expose colnames(exp) as a virtual .sample column, and variable verbs expose rownames(exp) as a virtual .variable column. These dot-prefixed names distinguish dimension identifiers from regular metadata columns. After the operation, the virtual column is removed and its values are written back to the corresponding dimension names.

Consequently, sample in colData(exp) and variable in rowData(exp) remain ordinary metadata columns. The names .sample and .variable are reserved; an input containing either name in the corresponding metadata raises an error rather than overwriting that column.

Examples

toy_exp <- real_experiment
toy_exp
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 12 samples, 4262 variables
#> ℹ Glycan type: N
#> ℹ Row data fields: peptide <chr>, peptide_site <int>, protein <chr>, protein_site <int>, gene <chr>, glycan_composition <comp>, glycan_structure <struct>
#> ℹ Column data fields: group <fct>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>

# Rename columns in sample information tibble
rename_col(toy_exp, condition = group)
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 12 samples, 4262 variables
#> ℹ Glycan type: N
#> ℹ Row data fields: peptide <chr>, peptide_site <int>, protein <chr>, protein_site <int>, gene <chr>, glycan_composition <comp>, glycan_structure <struct>
#> ℹ Column data fields: condition <fct>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>

# Rename columns in variable information tibble
rename_row(toy_exp, composition = glycan_composition)
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 12 samples, 4262 variables
#> ℹ Glycan type: N
#> ℹ Row data fields: peptide <chr>, peptide_site <int>, protein <chr>, protein_site <int>, gene <chr>, composition <comp>, glycan_structure <struct>
#> ℹ Column data fields: group <fct>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>