Overview
1 May 2024
Abstract
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.
Package
tidySpatialExperiment 1.0.0
Contents
1 tidySpatialExperiment - part of tidyomics
Resources to help you get started with tidySpatialExperiment and tidyomics:
The tidyomics ecosystem includes packages for:
Working with genomic features:
- plyranges, for tidy manipulation of genomic range data.
- nullranges, for tidy generation of genomic ranges representing the null hypothesis.
- plyinteractions, for tidy manipulation of genomic interaction data.
Working with transcriptomic features:
- tidySummarizedExperiment, for tidy manipulation of SummarizedExperiment objects.
- tidySingleCellExperiment, for tidy manipulation of SingleCellExperiment objects.
- tidyseurat, for tidy manipulation of Seurat objects.
- tidybulk, for bulk RNA-seq analysis.
Working with cytometry features:
- tidytof, for tidy manipulation of high-dimensional cytometry data.
2 Introduction
tidySpatialExperiment provides a bridge between the SpatialExperiment [@righelli2022spatialexperiment] package and the tidyverse [@wickham2019welcome] 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.
2.1 Functions and utilities
| 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 rectangular region of space |
ellipse |
Select elliptical region of space |
2.2 Installation
You can install the stable version of tidySpatialExperiment from Bioconductor with:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("tidySpatialExperiment")You can install the development version of tidySpatialExperiment from GitHub with:
if (!requireNamespace("devtools", quietly=TRUE))
install.packages("devtools")
devtools::install_github("william-hutchison/tidySpatialExperiment")2.3 Load data
Here, we attach tidySpatialExperiment and an example SpatialExperiment object.
# Load example SpatialExperiment object
library(tidySpatialExperiment)
example(read10xVisium)2.4 SpatialExperiment-tibble abstraction
A SpatialExperiment object represents observations (cells) as columns
and variables (features) as rows, as is the Bioconductor convention.
Additional information about the cells is accessed through the
reducedDims, colData and spatialCoords functions.
tidySpatialExperiment provides a SpatialExperiment-tibble abstraction,
representing cells as rows and features as columns, as is the
tidyverse convention. colData and spatialCoords are appended as
columns to the same abstraction, allowing easy interaction with this
additional data.
The default view is now of the SpatialExperiment-tibble abstraction.
spe## # A SpatialExperiment-tibble abstraction: 99 × 7
## # [90mFeatures=50 | Cells=99 | Assays=counts[0m
## .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>However, 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 section1spe |>
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 6400spe |>
imgData()## 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 #### 0.05103343 Integration with the tidyverse ecosystem
3.1 Manipulate with dplyr
Most functions from dplyr are available for use with the
SpatialExperiment-tibble abstraction. For example, filter can be used
to select cells by a variable of interest.
spe |>
filter(array_col < 5)## # A SpatialExperiment-tibble abstraction: 6 × 7
## # [90mFeatures=50 | Cells=6 | Assays=counts[0m
## .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
## 4 AAACATGGTGAGAGGA-1 FALSE 62 0 section2 1212
## 5 AAACGAAGATGGAGTA-1 FALSE 58 4 section2 1487
## 6 AAAGAATGACCTTAGA-1 FALSE 64 2 section2 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: 99 × 8
## # [90mFeatures=50 | Cells=99 | Assays=counts[0m
## .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 89 more rows
## # ℹ 1 more variable: pxl_row_in_fullres <int>3.2 Tidy with tidyr
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: 2 × 2
## sample_id data
## <chr> <list>
## 1 section1 <SptlExpr[,50]>
## 2 section2 <SptlExpr[,49]># Unnest the nested SpatialExperiment objects
spe_nested |>
unnest(data)## # A SpatialExperiment-tibble abstraction: 99 × 7
## # [90mFeatures=50 | Cells=99 | Assays=counts[0m
## .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>3.3 Plot with ggplot2
The ggplot function can be used to create a plot from a
SpatialExperiment object. This example also demonstrates how tidy
operations can be combined to build up more complex analysis. It should
be noted that helper functions such aes are not included and should be
imported from ggplot2.
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(ggplot2::aes(x = reorder(.cell, count_sum), y = count_sum)) +
ggplot2::geom_point() +
ggplot2::coord_flip()
3.4 Plot with plotly
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"
)
plotly demonstration
4 Integration with the tidyomics ecosystem
4.1 Interactively select cells with tidygate
Different packages from the tidyomics ecosystem are easy to use together. Here, tidygate is used to interactively gate cells based on their array location.
spe_regions <-
spe |>
filter(sample_id == "section1") |>
mutate(region = tidygate::gate_chr(array_col, array_row))