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 (Morgan et al. 2020) and the tidyverse (Wickham et al. 2019). 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 × 5
## # Features=14599 | Samples=7 | Assays=counts
##    .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
## # ℹ 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 × 5
## # Features=1 | Samples=1 | Assays=counts
##   .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 × 5
## # Features=14599 | Samples=4 | Assays=counts
##    .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
## # ℹ 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 
## # ℹ 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 × 5
## # Features=14599 | Samples=7 | Assays=counts
##    .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
## # ℹ 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 × 5
## # Features=14599 | Samples=7 | Assays=counts
##    .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
## # ℹ 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 × 4
## # Features=14599 | Samples=7 | Assays=counts
##    .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
## # ℹ 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.   
## # ℹ 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 %>%
    ggplot(aes(counts + 1, group=.sample, color=`type`)) +
    geom_density() +
    scale_x_log10() +
    my_theme

Session Info

sessionInfo()

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] tidyr_1.3.1                     dplyr_1.1.4                    
##  [3] tidySummarizedExperiment_1.17.0 ttservice_0.4.1                
##  [5] SummarizedExperiment_1.37.0     Biobase_2.67.0                 
##  [7] GenomicRanges_1.59.0            GenomeInfoDb_1.43.1            
##  [9] IRanges_2.41.1                  S4Vectors_0.45.2               
## [11] BiocGenerics_0.53.3             generics_0.1.3                 
## [13] MatrixGenerics_1.19.0           matrixStats_1.4.1              
## [15] ggplot2_3.5.1                   knitr_1.49                     
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6            xfun_0.49               bslib_0.8.0            
##  [4] htmlwidgets_1.6.4       lattice_0.22-6          vctrs_0.6.5            
##  [7] tools_4.4.2             tibble_3.2.1            fansi_1.0.6            
## [10] pkgconfig_2.0.3         Matrix_1.7-1            data.table_1.16.2      
## [13] RColorBrewer_1.1-3      lifecycle_1.0.4         GenomeInfoDbData_1.2.13
## [16] farver_2.1.2            stringr_1.5.1           compiler_4.4.2         
## [19] munsell_0.5.1           htmltools_0.5.8.1       sys_3.4.3              
## [22] buildtools_1.0.0        sass_0.4.9              yaml_2.3.10            
## [25] lazyeval_0.2.2          plotly_4.10.4           pillar_1.9.0           
## [28] crayon_1.5.3            jquerylib_0.1.4         ellipsis_0.3.2         
## [31] cachem_1.1.0            DelayedArray_0.33.2     abind_1.4-8            
## [34] tidyselect_1.2.1        digest_0.6.37           stringi_1.8.4          
## [37] purrr_1.0.2             labeling_0.4.3          maketools_1.3.1        
## [40] fastmap_1.2.0           grid_4.4.2              colorspace_2.1-1       
## [43] cli_3.6.3               SparseArray_1.7.2       magrittr_2.0.3         
## [46] S4Arrays_1.7.1          utf8_1.2.4              withr_3.0.2            
## [49] scales_1.3.0            UCSC.utils_1.3.0        rmarkdown_2.29         
## [52] XVector_0.47.0          httr_1.4.7              evaluate_1.0.1         
## [55] viridisLite_0.4.2       rlang_1.1.4             glue_1.8.0             
## [58] jsonlite_1.8.9          R6_2.5.1                zlibbioc_1.52.0

References

Morgan, Martin, Valerie Obenchain, Jim Hester, and Hervé Pagès. 2020. SummarizedExperiment: SummarizedExperiment Container.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse.” Journal of Open Source Software 4 (43): 1686.

Overview of the tidySummarizedExperiment package