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filter PlySummarizedExperiment

Source: R/dplyr-filter.R
filter.Rd

The filter() function is used to subset an object, returning the observations that satisfy your conditions. An observation must return TRUE for all conditions within a context to be retained. Note, to guarantee a valid PlySummarizedExperiment is returned, filtering in the assays evaluation context is disabled.

Usage

# S3 method for class 'PlySummarizedExperiment'
filter(.data, ..., .preserve = FALSE)

Arguments

.data

An object Inheriting from PlySummarizedExperiment, the wrapper class for SummarizedExperiment objects

...

conditions to filter on. These must be wrapped in cols() and or rows()

.preserve

Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, i.e. the number of groups may change.

Value

an object inheriting PlySummarizedExperiment class

Examples

# example code
filter(
  se_simple,
  rows(length > 30),
  cols(condition == "drug")
)
#> # A SummarizedExperiment-tibble Abstraction: 3 × 2
#>   .features .samples | counts logcounts | gene  length direction | sample
#>   <chr>     <chr>    |  <int>     <dbl> | <chr>  <int> <chr>     | <chr> 
#> 1 row_3     col_3    |     15      2.71 | g3        60 +         | s3    
#> 2 row_4     col_3    |     10      2.30 | g4        39 -         | s3    
#> 3 row_5     col_3    |     12      2.48 | g5        37 +         | s3    
#> 4 row_3     col_4    |     20      3.00 | g3        60 +         | s4    
#> 5 row_4     col_4    |      3      1.10 | g4        39 -         | s4    
#> 6 row_5     col_4    |      5      1.61 | g5        37 +         | s4    
#> # ℹ 1 more variable: condition <chr>

filter(
  se_simple,
  rows(rowSums(.assays_asis$counts) > 40),
  cols(colSums(.assays_asis$counts) < 50)
)
#> # A SummarizedExperiment-tibble Abstraction: 3 × 3
#>   .features .samples | counts logcounts | gene  length direction | sample
#>   <chr>     <chr>    |  <int>     <dbl> | <chr>  <int> <chr>     | <chr> 
#> 1 row_3     col_2    |      6      1.79 | g3        60 +         | s2    
#> 2 row_4     col_2    |     17      2.83 | g4        39 -         | s2    
#> 3 row_5     col_2    |     13      2.56 | g5        37 +         | s2    
#> 4 row_3     col_3    |     15      2.71 | g3        60 +         | s3    
#> 5 row_4     col_3    |     10      2.30 | g4        39 -         | s3    
#> 6 row_5     col_3    |     12      2.48 | g5        37 +         | s3    
#> 7 row_3     col_4    |     20      3.00 | g3        60 +         | s4    
#> 8 row_4     col_4    |      3      1.10 | g4        39 -         | s4    
#> 9 row_5     col_4    |      5      1.61 | g5        37 +         | s4    
#> # ℹ 1 more variable: condition <chr>

# assay context is disabled
filter(
  se_simple,
  counts > 12
) |> try()
#> Error in .f(se(.data), ...) : Cannot filter in `assays` context
#> ✖ review expression indices 1 in dots
#> ℹ consider wrapping expressions in rows(...) or cols(...)

# convert to `data.frame` first
as.data.frame(se_simple) |>
  filter(counts > 12)
#>   .features .samples counts logcounts gene length direction sample condition
#> 1     row_1    col_1     14  2.639057   g1      1         -     s1     cntrl
#> 2     row_2    col_1     19  2.944439   g2     24         +     s1     cntrl
#> 3     row_3    col_1     16  2.772589   g3     60         +     s1     cntrl
#> 4     row_5    col_1     18  2.890372   g5     37         +     s1     cntrl
#> 5     row_4    col_2     17  2.833213   g4     39         -     s2     cntrl
#> 6     row_5    col_2     13  2.564949   g5     37         +     s2     cntrl
#> 7     row_3    col_3     15  2.708050   g3     60         +     s3      drug
#> 8     row_3    col_4     20  2.995732   g3     60         +     s4      drug