Brings SummarizedExperiment to the tidyverse!
website: stemangiola.github.io/tidySummarizedExperiment/
Please also have a look at
- tidySingleCellExperiment for tidy manipulation of SingleCellExperiment objects
- tidyseurat for tidy manipulation of Seurat objects
- tidybulk for tidy analysis of RNA sequencing data
- nanny for tidy high-level data analysis and manipulation
- tidygate for adding custom gate information to your tibble
- tidyHeatmap for heatmaps produced with tidy principles
Introduction
tidySummarizedExperiment provides a bridge between Bioconductor SummarizedExperiment [@morgan2020summarized] and the tidyverse [@wickham2019welcome]. It creates an invisible layer that enables viewing the Bioconductor SummarizedExperiment object as a tidyverse tibble, and provides SummarizedExperiment-compatible dplyr, tidyr, ggplot and plotly functions. This allows users to get the best of both Bioconductor and tidyverse worlds.
Functions/utilities available
| SummarizedExperiment-compatible Functions | Description |
|---|---|
all |
After all tidySummarizedExperiment is a SummarizedExperiment object, just better |
| tidyverse Packages | Description |
|---|---|
dplyr |
Almost all dplyr APIs like for any tibble |
tidyr |
Almost all tidyr APIs like for any tibble |
ggplot2 |
ggplot like for any tibble |
plotly |
plot_ly like for any tibble |
| Utilities | Description |
|---|---|
as_tibble |
Convert cell-wise information to a tbl_df |
Installation
if (!requireNamespace("BiocManager", quietly=TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("tidySummarizedExperiment")
From Github (development)
devtools::install_github("stemangiola/tidySummarizedExperiment")
Load libraries used in the examples.
library(ggplot2)
library(tidySummarizedExperiment)
Create tidySummarizedExperiment, the best of both worlds!
This is a SummarizedExperiment object but it is evaluated as a tibble. So it is fully compatible both with SummarizedExperiment and tidyverse APIs.
pasilla_tidy <- tidySummarizedExperiment::pasilla
It looks like a tibble
pasilla_tidy
## # A SummarizedExperiment-tibble abstraction: 102,193 × 7
## # [90mFeatures=14599 | Samples=7 | Assays=counts[0m
## .feature .sample counts condition type
## <chr> <chr> <int> <chr> <chr>
## 1 FBgn0000003 untrt1 0 untreated single_end
## 2 FBgn0000008 untrt1 92 untreated single_end
## 3 FBgn0000014 untrt1 5 untreated single_end
## 4 FBgn0000015 untrt1 0 untreated single_end
## 5 FBgn0000017 untrt1 4664 untreated single_end
## 6 FBgn0000018 untrt1 583 untreated single_end
## 7 FBgn0000022 untrt1 0 untreated single_end
## 8 FBgn0000024 untrt1 10 untreated single_end
## 9 FBgn0000028 untrt1 0 untreated single_end
## 10 FBgn0000032 untrt1 1446 untreated single_end
## # … with 40 more rows
But it is a SummarizedExperiment object after all
assays(pasilla_tidy)
## List of length 1
## names(1): counts
Tidyverse commands
We can use tidyverse commands to explore the tidy SummarizedExperiment object.
We can use slice to choose rows by position, for example to choose the first row.
pasilla_tidy %>%
slice(1)
## # A SummarizedExperiment-tibble abstraction: 1 × 1
## # [90mFeatures=1 | Samples=1 | Assays=counts[0m
## .feature .sample counts condition type
## <chr> <chr> <int> <chr> <chr>
## 1 FBgn0000003 untrt1 0 untreated single_end
We can use filter to choose rows by criteria.
pasilla_tidy %>%
filter(condition == "untreated")
## # A SummarizedExperiment-tibble abstraction: 58,396 × 4
## # [90mFeatures=14599 | Samples=4 | Assays=counts[0m
## .feature .sample counts condition type
## <chr> <chr> <int> <chr> <chr>
## 1 FBgn0000003 untrt1 0 untreated single_end
## 2 FBgn0000008 untrt1 92 untreated single_end
## 3 FBgn0000014 untrt1 5 untreated single_end
## 4 FBgn0000015 untrt1 0 untreated single_end
## 5 FBgn0000017 untrt1 4664 untreated single_end
## 6 FBgn0000018 untrt1 583 untreated single_end
## 7 FBgn0000022 untrt1 0 untreated single_end
## 8 FBgn0000024 untrt1 10 untreated single_end
## 9 FBgn0000028 untrt1 0 untreated single_end
## 10 FBgn0000032 untrt1 1446 untreated single_end
## # … with 40 more rows
We can use select to choose columns.
pasilla_tidy %>%
select(.sample)
## # A tibble: 102,193 × 1
## .sample
## <chr>
## 1 untrt1
## 2 untrt1
## 3 untrt1
## 4 untrt1
## 5 untrt1
## 6 untrt1
## 7 untrt1
## 8 untrt1
## 9 untrt1
## 10 untrt1
## # … with 102,183 more rows
We can use count to count how many rows we have for each sample.
pasilla_tidy %>%
count(.sample)
## # A tibble: 7 × 2
## .sample n
## <chr> <int>
## 1 trt1 14599
## 2 trt2 14599
## 3 trt3 14599
## 4 untrt1 14599
## 5 untrt2 14599
## 6 untrt3 14599
## 7 untrt4 14599
We can use distinct to see what distinct sample information we have.
pasilla_tidy %>%
distinct(.sample, condition, type)
## # A tibble: 7 × 3
## .sample condition type
## <chr> <chr> <chr>
## 1 untrt1 untreated single_end
## 2 untrt2 untreated single_end
## 3 untrt3 untreated paired_end
## 4 untrt4 untreated paired_end
## 5 trt1 treated single_end
## 6 trt2 treated paired_end
## 7 trt3 treated paired_end
We could use rename to rename a column. For example, to modify the type column name.
pasilla_tidy %>%
rename(sequencing=type)
## # A SummarizedExperiment-tibble abstraction: 102,193 × 7
## # [90mFeatures=14599 | Samples=7 | Assays=counts[0m
## .feature .sample counts condition sequencing
## <chr> <chr> <int> <chr> <chr>
## 1 FBgn0000003 untrt1 0 untreated single_end
## 2 FBgn0000008 untrt1 92 untreated single_end
## 3 FBgn0000014 untrt1 5 untreated single_end
## 4 FBgn0000015 untrt1 0 untreated single_end
## 5 FBgn0000017 untrt1 4664 untreated single_end
## 6 FBgn0000018 untrt1 583 untreated single_end
## 7 FBgn0000022 untrt1 0 untreated single_end
## 8 FBgn0000024 untrt1 10 untreated single_end
## 9 FBgn0000028 untrt1 0 untreated single_end
## 10 FBgn0000032 untrt1 1446 untreated single_end
## # … with 40 more rows
We could use mutate to create a column. For example, we could create a new type column that contains single
and paired instead of single_end and paired_end.
pasilla_tidy %>%
mutate(type=gsub("_end", "", type))
## # A SummarizedExperiment-tibble abstraction: 102,193 × 7
## # [90mFeatures=14599 | Samples=7 | Assays=counts[0m
## .feature .sample counts condition type
## <chr> <chr> <int> <chr> <chr>
## 1 FBgn0000003 untrt1 0 untreated single
## 2 FBgn0000008 untrt1 92 untreated single
## 3 FBgn0000014 untrt1 5 untreated single
## 4 FBgn0000015 untrt1 0 untreated single
## 5 FBgn0000017 untrt1 4664 untreated single
## 6 FBgn0000018 untrt1 583 untreated single
## 7 FBgn0000022 untrt1 0 untreated single
## 8 FBgn0000024 untrt1 10 untreated single
## 9 FBgn0000028 untrt1 0 untreated single
## 10 FBgn0000032 untrt1 1446 untreated single
## # … with 40 more rows
We could use unite to combine multiple columns into a single column.
pasilla_tidy %>%
unite("group", c(condition, type))
## # A SummarizedExperiment-tibble abstraction: 102,193 × 7
## # [90mFeatures=14599 | Samples=7 | Assays=counts[0m
## .feature .sample counts group
## <chr> <chr> <int> <chr>
## 1 FBgn0000003 untrt1 0 untreated_single_end
## 2 FBgn0000008 untrt1 92 untreated_single_end
## 3 FBgn0000014 untrt1 5 untreated_single_end
## 4 FBgn0000015 untrt1 0 untreated_single_end
## 5 FBgn0000017 untrt1 4664 untreated_single_end
## 6 FBgn0000018 untrt1 583 untreated_single_end
## 7 FBgn0000022 untrt1 0 untreated_single_end
## 8 FBgn0000024 untrt1 10 untreated_single_end
## 9 FBgn0000028 untrt1 0 untreated_single_end
## 10 FBgn0000032 untrt1 1446 untreated_single_end
## # … with 40 more rows
We can also combine commands with the tidyverse pipe %>%.
For example, we could combine group_by and summarise to get the total counts for each sample.
pasilla_tidy %>%
group_by(.sample) %>%
summarise(total_counts=sum(counts))
## # A tibble: 7 × 2
## .sample total_counts
## <chr> <int>
## 1 trt1 18670279
## 2 trt2 9571826
## 3 trt3 10343856
## 4 untrt1 13972512
## 5 untrt2 21911438
## 6 untrt3 8358426
## 7 untrt4 9841335
We could combine group_by, mutate and filter to get the transcripts with mean count > 0.
pasilla_tidy %>%
group_by(.feature) %>%
mutate(mean_count=mean(counts)) %>%
filter(mean_count > 0)
## # A tibble: 86,513 × 6
## # Groups: .feature [12,359]
## .feature .sample counts condition type mean_count
## <chr> <chr> <int> <chr> <chr> <dbl>
## 1 FBgn0000003 untrt1 0 untreated single_end 0.143
## 2 FBgn0000008 untrt1 92 untreated single_end 99.6
## 3 FBgn0000014 untrt1 5 untreated single_end 1.43
## 4 FBgn0000015 untrt1 0 untreated single_end 0.857
## 5 FBgn0000017 untrt1 4664 untreated single_end 4672.
## 6 FBgn0000018 untrt1 583 untreated single_end 461.
## 7 FBgn0000022 untrt1 0 untreated single_end 0.143
## 8 FBgn0000024 untrt1 10 untreated single_end 7
## 9 FBgn0000028 untrt1 0 untreated single_end 0.429
## 10 FBgn0000032 untrt1 1446 untreated single_end 1085.
## # … with 86,503 more rows
Plotting
my_theme <-
list(
scale_fill_brewer(palette="Set1"),
scale_color_brewer(palette="Set1"),
theme_bw() +
theme(
panel.border=element_blank(),
axis.line=element_line(),
panel.grid.major=element_line(size=0.2),
panel.grid.minor=element_line(size=0.1),
text=element_text(size=12),
legend.position="bottom",
aspect.ratio=1,
strip.background=element_blank(),
axis.title.x=element_text(margin=margin(t=10, r=10, b=10, l=10)),
axis.title.y=element_text(margin=margin(t=10, r=10, b=10, l=10))
)
)
We can treat pasilla_tidy as a normal tibble for plotting.
Here we plot the distribution of counts per sample.
pasilla_tidy %>%
tidySummarizedExperiment::ggplot(aes(counts + 1, group=.sample, color=`type`)) +
geom_density() +
scale_x_log10() +
my_theme
Session Info
sessionInfo()
## R version 4.2.2 (2022-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] tidySummarizedExperiment_1.8.1 SummarizedExperiment_1.28.0
## [3] Biobase_2.58.0 GenomicRanges_1.50.2
## [5] GenomeInfoDb_1.34.9 IRanges_2.32.0
## [7] S4Vectors_0.36.2 BiocGenerics_0.44.0
## [9] MatrixGenerics_1.10.0 matrixStats_0.63.0
## [11] ggplot2_3.4.1 knitr_1.42
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 xfun_0.37 purrr_1.0.1
## [4] lattice_0.20-45 colorspace_2.1-0 vctrs_0.5.2
## [7] generics_0.1.3 htmltools_0.5.4 viridisLite_0.4.1
## [10] utf8_1.2.3 plotly_4.10.1 rlang_1.1.0
## [13] pillar_1.8.1 glue_1.6.2 withr_2.5.0
## [16] RColorBrewer_1.1-3 GenomeInfoDbData_1.2.9 lifecycle_1.0.3
## [19] stringr_1.5.0 zlibbioc_1.44.0 munsell_0.5.0
## [22] gtable_0.3.1 htmlwidgets_1.6.1 evaluate_0.20
## [25] labeling_0.4.2 fastmap_1.1.1 fansi_1.0.4
## [28] highr_0.10 scales_1.2.1 DelayedArray_0.24.0
## [31] jsonlite_1.8.4 XVector_0.38.0 farver_2.1.1
## [34] digest_0.6.31 stringi_1.7.12 dplyr_1.1.0
## [37] grid_4.2.2 cli_3.6.0 tools_4.2.2
## [40] bitops_1.0-7 magrittr_2.0.3 RCurl_1.98-1.10
## [43] lazyeval_0.2.2 tibble_3.2.0 tidyr_1.3.0
## [46] pkgconfig_2.0.3 ellipsis_0.3.2 Matrix_1.5-3
## [49] data.table_1.14.8 httr_1.4.5 R6_2.5.1
## [52] compiler_4.2.2