join_features() extracts and joins information for specified features

Arguments

.data

A SpatialExperiment object

features

A vector of feature identifiers to join

all

If TRUE return all

exclude_zeros

If TRUE exclude zero values

shape

Format of the returned table "long" or "wide"

...

Parameters to pass to join wide, i.e. assay name to extract feature abundance from and gene prefix, for shape="wide"

Value

An object containing the information.for the specified features

Details

This function extracts information for specified features and returns the information in either long or wide format.

Examples

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 |>
    join_features(features = "ENSMUSG00000025900")
#> tidySpatialExperiment says: A data frame is returned for independent data 
#>               analysis.
#> # A tibble: 99 × 7
#>    .cell      in_tissue array_row array_col sample_id .feature .abundance_counts
#>    <chr>      <lgl>         <int>     <int> <chr>     <chr>                <dbl>
#>  1 AAACAACGA… FALSE             0        16 section1  ENSMUSG…                 0
#>  2 AAACAAGTA… TRUE             50       102 section1  ENSMUSG…                 0
#>  3 AAACAATCT… TRUE              3        43 section1  ENSMUSG…                 0
#>  4 AAACACCAA… TRUE             59        19 section1  ENSMUSG…                 0
#>  5 AAACAGAGC… TRUE             14        94 section1  ENSMUSG…                 0
#>  6 AAACAGCTT… FALSE            43         9 section1  ENSMUSG…                 0
#>  7 AAACAGGGT… FALSE            47        13 section1  ENSMUSG…                 0
#>  8 AAACAGTGT… FALSE            73        43 section1  ENSMUSG…                 0
#>  9 AAACATGGT… FALSE            62         0 section1  ENSMUSG…                 0
#> 10 AAACATTTC… FALSE            61        97 section1  ENSMUSG…                 0
#> # ℹ 89 more rows