Overview of the tidySummarizedExperiment package

Stefano Mangiola

2025-10-02

Source: vignettes/introduction.Rmd

Lifecycle:maturing

Brings SummarizedExperiment to the tidyverse!

website: stemangiola.github.io/tidySummarizedExperiment/

Please also have a look at

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)

remotes::install_github("stemangiola/tidySummarizedExperiment")

Load libraries used in the examples.

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
## class: SummarizedExperiment 
## dim: 14599 7 
## metadata(0):
## assays(1): counts
## rownames(14599): FBgn0000003 FBgn0000008 ... FBgn0261574 FBgn0261575
## rowData names(0):
## colnames(7): untrt1 untrt2 ... trt2 trt3
## colData names(2): condition type

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 %>%
    dplyr::slice(1)
## class: SummarizedExperiment 
## dim: 1 1 
## metadata(1): latest_join_scope_report
## assays(1): counts
## rownames(1): FBgn0000003
## rowData names(0):
## colnames(1): untrt1
## colData names(2): condition type

We can use filter to choose rows by criteria.

pasilla_tidy %>%
    filter(condition == "untreated")
## class: SummarizedExperiment 
## dim: 14599 4 
## metadata(1): latest_filter_scope_report
## assays(1): counts
## rownames(14599): FBgn0000003 FBgn0000008 ... FBgn0261574 FBgn0261575
## rowData names(0):
## colnames(4): untrt1 untrt2 untrt3 untrt4
## colData names(2): condition type

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 %>%
    dplyr::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 %>%
    dplyr::rename(sequencing=type)
## class: SummarizedExperiment 
## dim: 14599 7 
## metadata(0):
## assays(1): counts
## rownames(14599): FBgn0000003 FBgn0000008 ... FBgn0261574 FBgn0261575
## rowData names(0):
## colnames(7): untrt1 untrt2 ... trt2 trt3
## colData names(2): condition sequencing

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))
## class: SummarizedExperiment 
## dim: 14599 7 
## metadata(1): latest_mutate_scope_report
## assays(1): counts
## rownames(14599): FBgn0000003 FBgn0000008 ... FBgn0261574 FBgn0261575
## rowData names(0):
## colnames(7): untrt1 untrt2 ... trt2 trt3
## colData names(2): condition type

We could use unite to combine multiple columns into a single column.

pasilla_tidy %>%
    unite("group", c(condition, type))
## class: SummarizedExperiment 
## dim: 14599 7 
## metadata(0):
## assays(1): counts
## rownames(14599): FBgn0000003 FBgn0000008 ... FBgn0261574 FBgn0261575
## rowData names(0):
## colnames(7): untrt1 untrt2 ... trt2 trt3
## colData names(1): group

We can use append_samples to combine multiple SummarizedExperiment objects by samples. It is equivalent to cbind but it is a tidyverse-like function.

# Create two subsets of the data
pasilla_subset1 <- pasilla_tidy %>%
    filter(condition == "untreated")

pasilla_subset2 <- pasilla_tidy %>%
    filter(condition == "treated")

# Combine them using append_samples
combined_data <- append_samples(pasilla_subset1, pasilla_subset2)
combined_data
## class: SummarizedExperiment 
## dim: 14599 7 
## metadata(2): latest_filter_scope_report latest_filter_scope_report
## assays(1): counts
## rownames(14599): FBgn0000003 FBgn0000008 ... FBgn0261574 FBgn0261575
## rowData names(0):
## colnames(7): untrt1 untrt2 ... trt2 trt3
## colData names(2): condition type

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 

## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 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=en_US.UTF-8    
##  [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: 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                     tidySummarizedExperiment_1.19.7
##  [3] ttservice_0.5.3                 SummarizedExperiment_1.39.2    
##  [5] Biobase_2.69.1                  GenomicRanges_1.61.5           
##  [7] Seqinfo_0.99.2                  IRanges_2.43.4                 
##  [9] S4Vectors_0.47.4                BiocGenerics_0.55.1            
## [11] generics_0.1.4                  MatrixGenerics_1.21.0          
## [13] matrixStats_1.5.0               dplyr_1.1.4                    
## [15] ggplot2_4.0.0                   knitr_1.50                     
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6        xfun_0.53           bslib_0.9.0        
##  [4] htmlwidgets_1.6.4   lattice_0.22-7      vctrs_0.6.5        
##  [7] tools_4.5.1         tibble_3.3.0        pkgconfig_2.0.3    
## [10] Matrix_1.7-4        data.table_1.17.8   RColorBrewer_1.1-3 
## [13] S7_0.2.0            desc_1.4.3          lifecycle_1.0.4    
## [16] compiler_4.5.1      farver_2.1.2        stringr_1.5.2      
## [19] textshaping_1.0.3   prettydoc_0.4.1     htmltools_0.5.8.1  
## [22] sass_0.4.10         yaml_2.3.10         lazyeval_0.2.2     
## [25] plotly_4.11.0       pillar_1.11.1       pkgdown_2.1.3      
## [28] crayon_1.5.3        jquerylib_0.1.4     ellipsis_0.3.2     
## [31] DelayedArray_0.35.3 cachem_1.1.0        abind_1.4-8        
## [34] tidyselect_1.2.1    digest_0.6.37       stringi_1.8.7      
## [37] purrr_1.1.0         labeling_0.4.3      fastmap_1.2.0      
## [40] grid_4.5.1          cli_3.6.5           SparseArray_1.9.1  
## [43] magrittr_2.0.4      S4Arrays_1.9.1      utf8_1.2.6         
## [46] withr_3.0.2         scales_1.4.0        rmarkdown_2.30     
## [49] XVector_0.49.1      httr_1.4.7          ragg_1.5.0         
## [52] evaluate_1.0.5      viridisLite_0.4.2   rlang_1.1.6        
## [55] glue_1.8.0          jsonlite_2.0.0      R6_2.6.1           
## [58] systemfonts_1.2.3   fs_1.6.6

References

Morgan, Martin, Valerie Obenchain, Jim Hester, and Hervé Pagès. 2020. SummarizedExperiment: SummarizedExperiment Container.
Wickham, Hadley, Mara Averick, Jennifer Bryan, et al. 2019. “Welcome to the Tidyverse.” Journal of Open Source Software 4 (43): 1686.