Slice sample or variable information — slice

Slice the sample or variable information of an experiment() or SummarizedExperiment.

These functions provide row-wise slicing operations similar to dplyr's slice functions. They select rows by position or based on values in specified columns, and update the expression matrix accordingly to match the new selection.

slice_col() and slice_row(): Select rows by position
slice_head_col() and slice_head_row(): Select first n rows
slice_tail_col() and slice_tail_row(): Select last n rows
slice_sample_col() and slice_sample_row(): Select random n rows
slice_max_col() and slice_max_row(): Select rows with highest values
slice_min_col() and slice_min_row(): Select rows with lowest values

Usage

slice_col(exp, ...)

slice_row(exp, ...)

slice_head_col(exp, n, prop)

slice_head_row(exp, n, prop)

slice_tail_col(exp, n, prop)

slice_tail_row(exp, n, prop)

slice_sample_col(exp, n, prop, weight_by = NULL, replace = FALSE)

slice_sample_row(exp, n, prop, weight_by = NULL, replace = FALSE)

slice_max_col(exp, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

slice_max_row(exp, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

slice_min_col(exp, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

slice_min_row(exp, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

Arguments

exp: An experiment() or SummarizedExperiment object.
...: <data-masking> For slice_*(), integer row positions. For slice_max() and slice_min(), variables to order by. Other arguments passed to the corresponding dplyr function.
n: For slice_head(), slice_tail(), slice_sample(), slice_max(), and slice_min(), the number of rows to select.
prop: For slice_head(), slice_tail(), slice_sample(), slice_max(), and slice_min(), the proportion of rows to select.
weight_by: For slice_sample(), sampling weights.
replace: For slice_sample(), should sampling be with replacement?
order_by: For slice_max() and slice_min(), variable to order by.
with_ties: For slice_max() and slice_min(), should ties be kept?
na_rm: For slice_max() and slice_min(), should missing values be removed?

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

# Add values used for slicing to a bundled experiment
exp <- real_experiment |>
  mutate_col(score = seq_len(dplyr::n())) |>
  mutate_row(value = seq_len(dplyr::n()))

# Select specific rows by position
slice_col(exp, 1, 3, 5)
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 3 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>, value <int>
#> ℹ Column data fields: group <fct>, score <int>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>

# Select first 3 samples
slice_head_col(exp, n = 3)
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 3 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>, value <int>
#> ℹ Column data fields: group <fct>, score <int>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>

# Select last 2 variables
slice_tail_row(exp, n = 2)
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 12 samples, 2 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>, value <int>
#> ℹ Column data fields: group <fct>, score <int>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>

# Select 2 random samples
slice_sample_col(exp, n = 2)
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 2 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>, value <int>
#> ℹ Column data fields: group <fct>, score <int>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>

# Select samples with highest scores
slice_max_col(exp, order_by = score, n = 2)
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 2 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>, value <int>
#> ℹ Column data fields: group <fct>, score <int>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>

# Select variables with lowest values
slice_min_row(exp, order_by = value, n = 2)
#> 
#> ── GlycoproteomicSE ────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 12 samples, 2 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>, value <int>
#> ℹ Column data fields: group <fct>, score <int>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>