Getting a subset of an experiment() or SummarizedExperiment by filtering
samples or variables.
The same syntax as dplyr::filter() is used.
For example, to get a subset of an experiment keeping only "HC" samples,
use filter_col(exp, group == "HC").
This actually calls dplyr::filter() on the sample information tibble
with condition group == "HC",
and then updates the expression matrix accordingly.
Arguments
- exp
An
experiment()orSummarizedExperimentobject.- ...
<
data-masking> Expression to filter samples or variables. passed todplyr::filter()internally.- .drop_levels
Logical. If
TRUE, drop unused factor levels for columns referenced in the filtering expressions.
Details
One difference between filter_col() or filter_row() and dplyr::filter is that,
when filtering on factor columns, unused levels can be dropped by setting
.drop_levels to TRUE.
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
library(SummarizedExperiment)
# Add a variable annotation to a bundled experiment
exp <- real_experiment |>
mutate_row(type = "glycopeptide")
# Filter samples
sub_exp_1 <- filter_col(exp, group == "H")
colData(sub_exp_1)
#> DataFrame with 3 rows and 1 column
#> group
#> <factor>
#> H1 H
#> H2 H
#> H3 H
# Filter variables
sub_exp_2 <- filter_row(exp, type == "glycopeptide")
rowData(sub_exp_2)
#> DataFrame with 4262 rows and 8 columns
#> peptide
#> <character>
#> P08185-176-Hex(5)HexNAc(4)NeuAc(2) NKTQGK
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-1 HSHNNNSSDLHPHK
#> P04196-344-Hex(5)HexNAc(4) HSHNNNSSDLHPHK
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-2 HSHNNNSSDLHPHK
#> P10909-291-Hex(6)HexNAc(5)-1 HNSTGCLR
#> ... ...
#> P09871-174-Hex(5)HexNAc(4)NeuAc(1) NCGVNCSGDVFTALIGEIAS..
#> P02763-93-Hex(7)HexNAc(6)dHex(1)NeuAc(4)-2 QDQCIYNTTYLNVQR
#> P02790-453-Hex(6)HexNAc(5)NeuAc(3)-2 ALPQPQNVTSLLGCTH
#> P01008-187-Hex(12)HexNAc(2) SLTFNETYQDISELVYGAK
#> P01023-1424-Hex(5)HexNAc(4)dHex(1)NeuAc(1)-2 VSNQTLSLFFTVLQDVPVR
#> peptide_site protein
#> <integer> <character>
#> P08185-176-Hex(5)HexNAc(4)NeuAc(2) 1 P08185
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-1 5 P04196
#> P04196-344-Hex(5)HexNAc(4) 5 P04196
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-2 5 P04196
#> P10909-291-Hex(6)HexNAc(5)-1 2 P10909
#> ... ... ...
#> P09871-174-Hex(5)HexNAc(4)NeuAc(1) 5 P09871
#> P02763-93-Hex(7)HexNAc(6)dHex(1)NeuAc(4)-2 7 P02763
#> P02790-453-Hex(6)HexNAc(5)NeuAc(3)-2 7 P02790
#> P01008-187-Hex(12)HexNAc(2) 5 P01008
#> P01023-1424-Hex(5)HexNAc(4)dHex(1)NeuAc(1)-2 3 P01023
#> protein_site gene
#> <integer> <character>
#> P08185-176-Hex(5)HexNAc(4)NeuAc(2) 176 SERPINA6
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-1 344 HRG
#> P04196-344-Hex(5)HexNAc(4) 344 HRG
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-2 344 HRG
#> P10909-291-Hex(6)HexNAc(5)-1 291 CLU
#> ... ... ...
#> P09871-174-Hex(5)HexNAc(4)NeuAc(1) 174 C1S
#> P02763-93-Hex(7)HexNAc(6)dHex(1)NeuAc(4)-2 93 ORM1
#> P02790-453-Hex(6)HexNAc(5)NeuAc(3)-2 453 HPX
#> P01008-187-Hex(12)HexNAc(2) 187 SERPINC1
#> P01023-1424-Hex(5)HexNAc(4)dHex(1)NeuAc(1)-2 1424 A2M
#> glycan_composition
#> <glyrepr_composition>
#> P08185-176-Hex(5)HexNAc(4)NeuAc(2) Hex(5)HexNAc(4)NeuAc..
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-1 Hex(5)HexNAc(4)NeuAc..
#> P04196-344-Hex(5)HexNAc(4) Hex(5)HexNAc(4)
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-2 Hex(5)HexNAc(4)NeuAc..
#> P10909-291-Hex(6)HexNAc(5)-1 Hex(6)HexNAc(5)
#> ... ...
#> P09871-174-Hex(5)HexNAc(4)NeuAc(1) Hex(5)HexNAc(4)NeuAc..
#> P02763-93-Hex(7)HexNAc(6)dHex(1)NeuAc(4)-2 Hex(7)HexNAc(6)dHex(..
#> P02790-453-Hex(6)HexNAc(5)NeuAc(3)-2 Hex(6)HexNAc(5)NeuAc..
#> P01008-187-Hex(12)HexNAc(2) Hex(12)HexNAc(2)
#> P01023-1424-Hex(5)HexNAc(4)dHex(1)NeuAc(1)-2 Hex(5)HexNAc(4)dHex(..
#> glycan_structure
#> <glyrepr_structure>
#> P08185-176-Hex(5)HexNAc(4)NeuAc(2) NeuAc(??-?)Hex(??-?)..
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-1 NeuAc(??-?)Hex(??-?)..
#> P04196-344-Hex(5)HexNAc(4) Hex(??-?)HexNAc(??-?..
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-2 NeuAc(??-?)Hex(??-?)..
#> P10909-291-Hex(6)HexNAc(5)-1 Hex(??-?)HexNAc(??-?..
#> ... ...
#> P09871-174-Hex(5)HexNAc(4)NeuAc(1) NeuAc(??-?)Hex(??-?)..
#> P02763-93-Hex(7)HexNAc(6)dHex(1)NeuAc(4)-2 NeuAc(??-?)Hex(??-?)..
#> P02790-453-Hex(6)HexNAc(5)NeuAc(3)-2 NeuAc(??-?)Hex(??-?)..
#> P01008-187-Hex(12)HexNAc(2) Hex(??-?)Hex(??-?)He..
#> P01023-1424-Hex(5)HexNAc(4)dHex(1)NeuAc(1)-2 NeuAc(??-?)Hex(??-?)..
#> type
#> <character>
#> P08185-176-Hex(5)HexNAc(4)NeuAc(2) glycopeptide
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-1 glycopeptide
#> P04196-344-Hex(5)HexNAc(4) glycopeptide
#> P04196-344-Hex(5)HexNAc(4)NeuAc(1)-2 glycopeptide
#> P10909-291-Hex(6)HexNAc(5)-1 glycopeptide
#> ... ...
#> P09871-174-Hex(5)HexNAc(4)NeuAc(1) glycopeptide
#> P02763-93-Hex(7)HexNAc(6)dHex(1)NeuAc(4)-2 glycopeptide
#> P02790-453-Hex(6)HexNAc(5)NeuAc(3)-2 glycopeptide
#> P01008-187-Hex(12)HexNAc(2) glycopeptide
#> P01023-1424-Hex(5)HexNAc(4)dHex(1)NeuAc(1)-2 glycopeptide
# Use pipe
sub_exp_3 <- exp |>
filter_col(group == "H") |>
filter_row(type == "glycopeptide")
sub_exp_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>, type <chr>
#> ℹ Column data fields: group <fct>
#> ℹ Metadata fields: exp_type <chr>, glycan_type <chr>, quant_method <chr>