A brief overview of the tidySpatialExperiment package - demonstrating the SpatialExperiment-tibble abstraction, compatibility with the tidyverse ecosystem, compatibility with the tidyomics ecosystem and a few helpful utility functions.
tidySpatialExperiment 1.1.4
tidySpatialExperiment provides a bridge between the
SpatialExperiment
package and the tidyverse ecosystem. It
creates an invisible layer that allows you to interact with a
SpatialExperiment
object as if it were a tibble; enabling the use of
functions from dplyr,
tidyr,
ggplot2 and
plotly. But, underneath, your data
remains a SpatialExperiment
object.
tidySpatialExperiment also provides five additional utility functions.
If you would like to learn more about tidySpatialExperiment and tidyomics, the following links are a good place to start:
The tidyomics ecosystem also includes packages for:
Working with genomic features:
Working with transcriptomic features:
SummarizedExperiment
objects.SingleCellExperiment
objects.Seurat
objects.Working with cytometry features:
And a few associated packages:
Package | Functions available |
---|---|
SpatialExperiment |
All |
dplyr |
arrange ,bind_rows , bind_cols , distinct , filter , group_by , summarise , select , mutate , rename , left_join , right_join , inner_join , slice , sample_n , sample_frac , count , add_count |
tidyr |
nest , unnest , unite , separate , extract , pivot_longer |
ggplot2 |
ggplot |
plotly |
plot_ly |
Utility | Description |
---|---|
as_tibble |
Convert cell data to a tbl_df |
join_features |
Append feature data to cell data |
aggregate_cells |
Aggregate cell-feature abundance into a pseudobulk SummarizedExperiment object |
rectangle |
Select cells in a rectangular region of space |
ellipse |
Select cells in an elliptical region of space |
gate_spatial |
|
gate_programmatic |
You can install the stable version of tidySpatialExperiment from Bioconductor.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("tidySpatialExperiment")
Or, you can install the development version of tidySpatialExperiment from GitHub.
if (!requireNamespace("pak", quietly = TRUE))
install.packages("pak")
pak::pak("william-hutchison/tidySpatialExperiment")
Here, we attach tidySpatialExperiment and an example SpatialExperiment
object.
# Load example SpatialExperiment object
library(tidySpatialExperiment)
example(read10xVisium)
A SpatialExperiment
object represents assay-feature values as rows and
cells as columns. Additional information about the cells is stored in the
reducedDims
, colData
and spatialCoords
slots.
tidySpatialExperiment provides a SpatialExperiment-tibble abstraction,
representing cells as rows and cell data as columns, in accordance with the
tidy observation-variable convention. The cell data is made up of information stored in the colData
and spatialCoords
slots.
The default view is now of the SpatialExperiment-tibble abstraction.
spe
## # A SpatialExperiment-tibble abstraction: 50 × 7
## # Features = 50 | Cells = 50 | 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 40 more rows
## # ℹ 1 more variable: pxl_row_in_fullres <int>
But, our data maintains its status as a SpatialExperiment
object.
Therefore, we have access to all SpatialExperiment
functions.
spe |>
colData() |>
head()
## DataFrame with 6 rows and 4 columns
## in_tissue array_row array_col sample_id
## <logical> <integer> <integer> <character>
## AAACAACGAATAGTTC-1 FALSE 0 16 section1
## AAACAAGTATCTCCCA-1 TRUE 50 102 section1
## AAACAATCTACTAGCA-1 TRUE 3 43 section1
## AAACACCAATAACTGC-1 TRUE 59 19 section1
## AAACAGAGCGACTCCT-1 TRUE 14 94 section1
## AAACAGCTTTCAGAAG-1 FALSE 43 9 section1
spe |>
spatialCoords() |>
head()
## pxl_col_in_fullres pxl_row_in_fullres
## AAACAACGAATAGTTC-1 2312 1252
## AAACAAGTATCTCCCA-1 8230 7237
## AAACAATCTACTAGCA-1 4170 1611
## AAACACCAATAACTGC-1 2519 8315
## AAACAGAGCGACTCCT-1 7679 2927
## AAACAGCTTTCAGAAG-1 1831 6400
spe |>
imgData()
## DataFrame with 1 row and 4 columns
## sample_id image_id data scaleFactor
## <character> <character> <list> <numeric>
## 1 section1 lowres #### 0.0510334
Most functions from dplyr are available for use with the
SpatialExperiment-tibble abstraction. For example, filter()
can be used
to filter cells by a variable of interest.
spe |>
filter(array_col < 5)
## # A SpatialExperiment-tibble abstraction: 3 × 7
## # Features = 50 | Cells = 3 | Assays = counts
## .cell in_tissue array_row array_col sample_id pxl_col_in_fullres
## <chr> <lgl> <int> <int> <chr> <int>
## 1 AAACATGGTGAGAGGA-1 FALSE 62 0 section1 1212
## 2 AAACGAAGATGGAGTA-1 FALSE 58 4 section1 1487
## 3 AAAGAATGACCTTAGA-1 FALSE 64 2 section1 1349
## # ℹ 1 more variable: pxl_row_in_fullres <int>
And mutate
can be used to add new variables, or modify the value of an
existing variable.
spe |>
mutate(in_region = c(in_tissue & array_row < 10))
## # A SpatialExperiment-tibble abstraction: 50 × 8
## # Features = 50 | Cells = 50 | Assays = counts
## .cell in_tissue array_row array_col sample_id in_region pxl_col_in_fullres
## <chr> <lgl> <int> <int> <chr> <lgl> <int>
## 1 AAACAAC… FALSE 0 16 section1 FALSE 2312
## 2 AAACAAG… TRUE 50 102 section1 FALSE 8230
## 3 AAACAAT… TRUE 3 43 section1 TRUE 4170
## 4 AAACACC… TRUE 59 19 section1 FALSE 2519
## 5 AAACAGA… TRUE 14 94 section1 FALSE 7679
## 6 AAACAGC… FALSE 43 9 section1 FALSE 1831
## 7 AAACAGG… FALSE 47 13 section1 FALSE 2106
## 8 AAACAGT… FALSE 73 43 section1 FALSE 4170
## 9 AAACATG… FALSE 62 0 section1 FALSE 1212
## 10 AAACATT… FALSE 61 97 section1 FALSE 7886
## # ℹ 40 more rows
## # ℹ 1 more variable: pxl_row_in_fullres <int>
Most functions from tidyr are also available. Here, nest()
is used to
group the data by sample_id
, and unnest()
is used to ungroup the data.
# Nest the SpatialExperiment object by sample_id
spe_nested <-
spe |>
nest(data = -sample_id)
# View the nested SpatialExperiment object
spe_nested
## # A tibble: 1 × 2
## sample_id data
## <chr> <list>
## 1 section1 <SptlExpr[,50]>
# Unnest the nested SpatialExperiment objects
spe_nested |>
unnest(data)
## # A SpatialExperiment-tibble abstraction: 50 × 7
## # Features = 50 | Cells = 50 | 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 40 more rows
## # ℹ 1 more variable: pxl_row_in_fullres <int>
The ggplot()
function can be used to create a plot directly from a
SpatialExperiment
object. This example also demonstrates how tidy
operations can be combined to build up more complex analysis.
spe |>
filter(sample_id == "section1" & in_tissue) |>
# Add a column with the sum of feature counts per cell
mutate(count_sum = purrr::map_int(.cell, ~
spe[, .x] |>
counts() |>
sum()
)) |>
# Plot with tidySpatialExperiment and ggplot2
ggplot(aes(x = reorder(.cell, count_sum), y = count_sum)) +
geom_point() +
coord_flip()
The plot_ly()
function can also be used to create a plot from a
SpatialExperiment
object.
spe |>
filter(sample_id == "section1") |>
plot_ly(
x = ~ array_col,
y = ~ array_row,
color = ~ in_tissue,
type = "scatter"
)
The tidyomics ecosystem places an emphasis on interacting with cell
data. To interact with feature data, the join_features()
function can be
used to append assay-feature values to cell data.
# Join feature data in wide format, preserving the SpatialExperiment object
spe |>
join_features(features = c("ENSMUSG00000025915", "ENSMUSG00000042501"), shape = "wide") |>
head()
## # A SpatialExperiment-tibble abstraction: 50 × 9
## # Features = 6 | Cells = 50 | Assays = counts
## .cell in_tissue array_row array_col sample_id ENSMUSG00000025915
## <chr> <lgl> <int> <int> <chr> <dbl>
## 1 AAACAACGAATAGTTC-1 FALSE 0 16 section1 0
## 2 AAACAAGTATCTCCCA-1 TRUE 50 102 section1 0
## 3 AAACAATCTACTAGCA-1 TRUE 3 43 section1 0
## 4 AAACACCAATAACTGC-1 TRUE 59 19 section1 0
## 5 AAACAGAGCGACTCCT-1 TRUE 14 94 section1 0
## 6 AAACAGCTTTCAGAAG-1 FALSE 43 9 section1 0
## 7 AAACAGGGTCTATATT-1 FALSE 47 13 section1 0
## 8 AAACAGTGTTCCTGGG-1 FALSE 73 43 section1 0
## 9 AAACATGGTGAGAGGA-1 FALSE 62 0 section1 0
## 10 AAACATTTCCCGGATT-1 FALSE 61 97 section1 0
## # ℹ 40 more rows
## # ℹ 3 more variables: ENSMUSG00000042501 <dbl>, pxl_col_in_fullres <int>,
## # pxl_row_in_fullres <int>
# Join feature data in long format, discarding the SpatialExperiment object
spe |>
join_features(features = c("ENSMUSG00000025915", "ENSMUSG00000042501"), shape = "long") |>
head()
## tidySpatialExperiment says: A data frame is returned for independent data
## analysis.
## # A tibble: 6 × 7
## .cell in_tissue array_row array_col sample_id .feature .abundance_counts
## <chr> <lgl> <int> <int> <chr> <chr> <dbl>
## 1 AAACAACGAA… FALSE 0 16 section1 ENSMUSG… 0
## 2 AAACAACGAA… FALSE 0 16 section1 ENSMUSG… 0
## 3 AAACAAGTAT… TRUE 50 102 section1 ENSMUSG… 0
## 4 AAACAAGTAT… TRUE 50 102 section1 ENSMUSG… 1
## 5 AAACAATCTA… TRUE 3 43 section1 ENSMUSG… 0
## 6 AAACAATCTA… TRUE 3 43 section1 ENSMUSG… 0
Sometimes, it is necessary to aggregate the gene-transcript abundance from a group of cells into a single value. For example, when comparing groups of cells across different samples with fixed-effect models.
The aggregate_cells()
function can be used to aggregate cells by a
specified variable and assay, returning a SummarizedExperiment
object.
spe |>
aggregate_cells(in_tissue, assays = "counts")
## class: SummarizedExperiment
## dim: 50 2
## metadata(0):
## assays(1): counts
## rownames(50): ENSMUSG00000002459 ENSMUSG00000005886 ...
## ENSMUSG00000104217 ENSMUSG00000104328
## rowData names(1): feature
## colnames(2): FALSE TRUE
## colData names(3): in_tissue .aggregated_cells sample_id
The ellipse()
and rectangle()
functions can be used to select cells by
their position in space.
spe |>
filter(sample_id == "section1") |>
mutate(in_ellipse = ellipse(array_col, array_row, c(20, 40), c(20, 20))) |>
ggplot(aes(x = array_col, y = array_row, colour = in_ellipse)) +
geom_point()
For the interactive selection of cells in space, tidySpatialExperiment experiment provides gate()
. This function uses tidygate, shiny and plotly to launch an interactive plot overlaying cells in position with image data. Additional parameters can be used to specify point colour, shape, size and alpha, either with a column in the SpatialExperiment object or a constant value.
spe_gated <-
spe |>
gate(colour = "in_tissue", alpha = 0.8)