The filter() function is used to subset a data frame,
retaining all rows that satisfy your conditions.
To be retained, the row must produce a value of TRUE for all conditions.
Note that when a condition evaluates to NA
the row will be dropped, unlike base subsetting with [.
# S3 method for class 'SpatialExperiment'
filter(.data, ..., .preserve = FALSE)A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
<data-masking> Expressions that
return a logical value, and are defined in terms of the variables in
.data. If multiple expressions are included, they are combined with the
& operator. Only rows for which all conditions evaluate to TRUE are
kept.
Relevant when the .data input is grouped.
If .preserve = FALSE (the default), the grouping structure
is recalculated based on the resulting data, otherwise the grouping is kept as is.
An object of the same type as .data. The output has the following properties:
Rows are a subset of the input, but appear in the same order.
Columns are not modified.
The number of groups may be reduced (if .preserve is not TRUE).
Data frame attributes are preserved.
The filter() function is used to subset the rows of
.data, applying the expressions in ... to the column values to determine which
rows should be retained. It can be applied to both grouped and ungrouped data (see group_by() and
ungroup()). However, dplyr is not yet smart enough to optimise the filtering
operation on grouped datasets that do not need grouped calculations. For this
reason, filtering is often considerably faster on ungrouped data.
There are many functions and operators that are useful when constructing the expressions used to filter the data:
Because filtering expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped filtering:
With the grouped equivalent:
In the ungrouped version, filter() compares the value of mass in each row to
the global average (taken over the whole data set), keeping only the rows with
mass greater than this global average. In contrast, the grouped version calculates
the average mass separately for each gender group, and keeps rows with mass greater
than the relevant within-gender average.
This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.
The following methods are currently available in loaded packages:
dplyr (data.frame, ts), plotly (plotly), tidySingleCellExperiment (SingleCellExperiment), tidySpatialExperiment (SpatialExperiment)
.
example(read10xVisium)
#>
#> rd10xV> dir <- system.file(
#> rd10xV+ file.path("extdata", "10xVisium"),
#> rd10xV+ package = "SpatialExperiment")
#>
#> rd10xV> sample_ids <- c("section1", "section2")
#>
#> rd10xV> samples <- file.path(dir, sample_ids, "outs")
#>
#> rd10xV> list.files(samples[1])
#> [1] "raw_feature_bc_matrix" "spatial"
#>
#> rd10xV> list.files(file.path(samples[1], "spatial"))
#> [1] "scalefactors_json.json" "tissue_lowres_image.png"
#> [3] "tissue_positions_list.csv"
#>
#> rd10xV> file.path(samples[1], "raw_feature_bc_matrix")
#> [1] "/__w/_temp/Library/SpatialExperiment/extdata/10xVisium/section1/outs/raw_feature_bc_matrix"
#>
#> rd10xV> (spe <- read10xVisium(samples, sample_ids,
#> rd10xV+ type = "sparse", data = "raw",
#> rd10xV+ images = "lowres", load = FALSE))
#> # A SpatialExperiment-tibble abstraction: 99 × 7
#> # Features = 50 | Cells = 99 | Assays = counts
#> .cell in_tissue array_row array_col sample_id pxl_col_in_fullres
#> <chr> <lgl> <int> <int> <chr> <int>
#> 1 AAACAACGAATAGTTC-1 FALSE 0 16 section1 2312
#> 2 AAACAAGTATCTCCCA-1 TRUE 50 102 section1 8230
#> 3 AAACAATCTACTAGCA-1 TRUE 3 43 section1 4170
#> 4 AAACACCAATAACTGC-1 TRUE 59 19 section1 2519
#> 5 AAACAGAGCGACTCCT-1 TRUE 14 94 section1 7679
#> 6 AAACAGCTTTCAGAAG-1 FALSE 43 9 section1 1831
#> 7 AAACAGGGTCTATATT-1 FALSE 47 13 section1 2106
#> 8 AAACAGTGTTCCTGGG-1 FALSE 73 43 section1 4170
#> 9 AAACATGGTGAGAGGA-1 FALSE 62 0 section1 1212
#> 10 AAACATTTCCCGGATT-1 FALSE 61 97 section1 7886
#> # ℹ 89 more rows
#> # ℹ 1 more variable: pxl_row_in_fullres <int>
#>
#> rd10xV> # base directory 'outs/' from Space Ranger can also be omitted
#> rd10xV> samples2 <- file.path(dir, sample_ids)
#>
#> rd10xV> (spe2 <- read10xVisium(samples2, sample_ids,
#> rd10xV+ type = "sparse", data = "raw",
#> rd10xV+ images = "lowres", load = FALSE))
#> # A SpatialExperiment-tibble abstraction: 99 × 7
#> # Features = 50 | Cells = 99 | Assays = counts
#> .cell in_tissue array_row array_col sample_id pxl_col_in_fullres
#> <chr> <lgl> <int> <int> <chr> <int>
#> 1 AAACAACGAATAGTTC-1 FALSE 0 16 section1 2312
#> 2 AAACAAGTATCTCCCA-1 TRUE 50 102 section1 8230
#> 3 AAACAATCTACTAGCA-1 TRUE 3 43 section1 4170
#> 4 AAACACCAATAACTGC-1 TRUE 59 19 section1 2519
#> 5 AAACAGAGCGACTCCT-1 TRUE 14 94 section1 7679
#> 6 AAACAGCTTTCAGAAG-1 FALSE 43 9 section1 1831
#> 7 AAACAGGGTCTATATT-1 FALSE 47 13 section1 2106
#> 8 AAACAGTGTTCCTGGG-1 FALSE 73 43 section1 4170
#> 9 AAACATGGTGAGAGGA-1 FALSE 62 0 section1 1212
#> 10 AAACATTTCCCGGATT-1 FALSE 61 97 section1 7886
#> # ℹ 89 more rows
#> # ℹ 1 more variable: pxl_row_in_fullres <int>
#>
#> rd10xV> # tabulate number of spots mapped to tissue
#> rd10xV> cd <- colData(spe)
#>
#> rd10xV> table(
#> rd10xV+ in_tissue = cd$in_tissue,
#> rd10xV+ sample_id = cd$sample_id)
#> sample_id
#> in_tissue section1 section2
#> FALSE 28 27
#> TRUE 22 22
#>
#> rd10xV> # view available images
#> rd10xV> imgData(spe)
#> DataFrame with 2 rows and 4 columns
#> sample_id image_id data scaleFactor
#> <character> <character> <list> <numeric>
#> 1 section1 lowres #### 0.0510334
#> 2 section2 lowres #### 0.0510334
spe |>
filter(in_tissue == TRUE)
#> # A SpatialExperiment-tibble abstraction: 44 × 7
#> # Features = 50 | Cells = 44 | Assays = counts
#> .cell in_tissue array_row array_col sample_id pxl_col_in_fullres
#> <chr> <lgl> <int> <int> <chr> <int>
#> 1 AAACAAGTATCTCCCA-1 TRUE 50 102 section1 8230
#> 2 AAACAATCTACTAGCA-1 TRUE 3 43 section1 4170
#> 3 AAACACCAATAACTGC-1 TRUE 59 19 section1 2519
#> 4 AAACAGAGCGACTCCT-1 TRUE 14 94 section1 7679
#> 5 AAACCGGGTAGGTACC-1 TRUE 42 28 section1 3138
#> 6 AAACCGTTCGTCCAGG-1 TRUE 52 42 section1 4101
#> 7 AAACCTCATGAAGTTG-1 TRUE 37 19 section1 2519
#> 8 AAACGAAGAACATACC-1 TRUE 6 64 section1 5615
#> 9 AAACGAGACGGTTGAT-1 TRUE 35 79 section1 6647
#> 10 AAACGGTTGCGAACTG-1 TRUE 67 59 section1 5271
#> # ℹ 34 more rows
#> # ℹ 1 more variable: pxl_row_in_fullres <int>