Brings SummarizedExperiment to the tidyverse!
website: stemangiola.github.io/tidySummarizedExperiment/
Please also have a look at
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.
| 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 |
|---|---|
tidy |
Add tidySummarizedExperiment invisible layer over a SummarizedExperiment object |
as_tibble |
Convert cell-wise information to a tbl_df |
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)
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)
## An object of class "SimpleAssays"
## Slot "data":
## List of length 1
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
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
sessionInfo()
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-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_1.6.1 SummarizedExperiment_1.26.1
## [3] Biobase_2.56.0 GenomicRanges_1.48.0
## [5] GenomeInfoDb_1.32.2 IRanges_2.30.0
## [7] S4Vectors_0.34.0 BiocGenerics_0.42.0
## [9] MatrixGenerics_1.8.0 matrixStats_0.62.0
## [11] ggplot2_3.3.6 knitr_1.39
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.2 xfun_0.31 purrr_0.3.4
## [4] lattice_0.20-45 colorspace_2.0-3 vctrs_0.4.1
## [7] generics_0.1.2 viridisLite_0.4.0 htmltools_0.5.2
## [10] utf8_1.2.2 plotly_4.10.0 rlang_1.0.2
## [13] pillar_1.7.0 glue_1.6.2 withr_2.5.0
## [16] DBI_1.1.2 RColorBrewer_1.1-3 GenomeInfoDbData_1.2.8
## [19] lifecycle_1.0.1 stringr_1.4.0 zlibbioc_1.42.0
## [22] munsell_0.5.0 gtable_0.3.0 htmlwidgets_1.5.4
## [25] evaluate_0.15 labeling_0.4.2 fastmap_1.1.0
## [28] fansi_1.0.3 highr_0.9 scales_1.2.0
## [31] DelayedArray_0.22.0 jsonlite_1.8.0 XVector_0.36.0
## [34] farver_2.1.0 digest_0.6.29 stringi_1.7.6
## [37] dplyr_1.0.9 grid_4.2.0 cli_3.3.0
## [40] tools_4.2.0 bitops_1.0-7 magrittr_2.0.3
## [43] RCurl_1.98-1.6 lazyeval_0.2.2 tibble_3.1.7
## [46] crayon_1.5.1 tidyr_1.2.0 pkgconfig_2.0.3
## [49] ellipsis_0.3.2 Matrix_1.4-1 data.table_1.14.2
## [52] assertthat_0.2.1 httr_1.4.3 R6_2.5.1
## [55] compiler_4.2.0