tidyprint

tidyprint is an R package that provides multiple printing styles for SummarizedExperiment objects. You can choose among:

1. SummarizedExperiment (default): Standard R/SummarizedExperiment printing style.

2. tidySummarizedExperiment: Tidyverse-friendly tibble abstraction.

3. plyxp: Top/bottom row truncation for large datasets.

4. alternative_1: Newly designed tibble abstraction, combines styles from tidySummarizedExperiment and plyxp

Depending on your workflow and desired console output, tidyprint makes it easy to switch between these printing styles.

---

1. Installation

You need the `` package to install from GitHub. If you don’t have it, install via:

install.packages("devtools")

Then install tidyprint from GitHub:

devtools::install_github("tidyomics/tidyprint")

---

2. Demo

Below is an example demonstrating how to use tidyprint with a sample SummarizedExperiment object.

2.1 Load Required Packages

library(dplyr)
library(tidyr)

# Now load tidyprint

library(tidyprint)

# Example SummarizedExperiment data from the airway package

data(se_airway)
se_airway
#> class: SummarizedExperiment 
#> dim: 38694 8 
#> metadata(0):
#> assays(1): counts
#> rownames(38694): ENSG00000000003 ENSG00000000005 ... ENSG00000283120
#>   ENSG00000283123
#> rowData names(0):
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(3): dex celltype geo_id

---

2.2 SummarizedExperiment (Default)

By default, print() on a SummarizedExperiment displays the standard SummarizedExperiment info:

# The default style:

se_airway %>% print()
#> class: SummarizedExperiment 
#> dim: 38694 8 
#> metadata(0):
#> assays(1): counts
#> rownames(38694): ENSG00000000003 ENSG00000000005 ... ENSG00000283120
#>   ENSG00000283123
#> rowData names(0):
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(3): dex celltype geo_id

# or equivalently:

se_airway %>% print(design = 1)
#> class: SummarizedExperiment 
#> dim: 38694 8 
#> metadata(0):
#> assays(1): counts
#> rownames(38694): ENSG00000000003 ENSG00000000005 ... ENSG00000283120
#>   ENSG00000283123
#> rowData names(0):
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(3): dex celltype geo_id

se_airway %>% print(design = "SummarizedExperiment")
#> class: SummarizedExperiment 
#> dim: 38694 8 
#> metadata(0):
#> assays(1): counts
#> rownames(38694): ENSG00000000003 ENSG00000000005 ... ENSG00000283120
#>   ENSG00000283123
#> rowData names(0):
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(3): dex celltype geo_id

2.3 tidySummarizedExperiment

Use the “tidySummarizedExperiment” design to view your data in a tidy-friendly tibble format:

# Tidy SummarizedExperiment print:

se_airway %>% print(design = 2)
#> # A SummarizedExperiment-tibble abstraction: 309,552 × 6
#> # Features=38694 | Samples=8 | Assays=counts
#>    .feature        .sample    counts dex     celltype geo_id    
#>    <chr>           <chr>       <dbl> <chr>   <chr>    <chr>     
#>  1 ENSG00000000003 SRR1039508    723 control N61311   GSM1275862
#>  2 ENSG00000000005 SRR1039508      0 control N61311   GSM1275862
#>  3 ENSG00000000419 SRR1039508    467 control N61311   GSM1275862
#>  4 ENSG00000000457 SRR1039508    347 control N61311   GSM1275862
#>  5 ENSG00000000460 SRR1039508     96 control N61311   GSM1275862
#>  6 ENSG00000000938 SRR1039508      0 control N61311   GSM1275862
#>  7 ENSG00000000971 SRR1039508   3413 control N61311   GSM1275862
#>  8 ENSG00000001036 SRR1039508   2328 control N61311   GSM1275862
#>  9 ENSG00000001084 SRR1039508    670 control N61311   GSM1275862
#> 10 ENSG00000001167 SRR1039508    426 control N61311   GSM1275862
#> # ℹ 40 more rows

# or equivalently:

se_airway %>% print(design = "tidySummarizedExperiment")
#> # A SummarizedExperiment-tibble abstraction: 309,552 × 6
#> # Features=38694 | Samples=8 | Assays=counts
#>    .feature        .sample    counts dex     celltype geo_id    
#>    <chr>           <chr>       <dbl> <chr>   <chr>    <chr>     
#>  1 ENSG00000000003 SRR1039508    723 control N61311   GSM1275862
#>  2 ENSG00000000005 SRR1039508      0 control N61311   GSM1275862
#>  3 ENSG00000000419 SRR1039508    467 control N61311   GSM1275862
#>  4 ENSG00000000457 SRR1039508    347 control N61311   GSM1275862
#>  5 ENSG00000000460 SRR1039508     96 control N61311   GSM1275862
#>  6 ENSG00000000938 SRR1039508      0 control N61311   GSM1275862
#>  7 ENSG00000000971 SRR1039508   3413 control N61311   GSM1275862
#>  8 ENSG00000001036 SRR1039508   2328 control N61311   GSM1275862
#>  9 ENSG00000001084 SRR1039508    670 control N61311   GSM1275862
#> 10 ENSG00000001167 SRR1039508    426 control N61311   GSM1275862
#> # ℹ 40 more rows

2.4 plyxp

For a more compact view (top and bottom rows), similar to a plyxp/tidyverse style:

se_airway %>% print(design = 3)
#> # A tibble: 10 × 9
#>    .features       .samples   `|` counts `|` `|` dex     celltype geo_id    
#>    <chr>           <chr>      <|>  <dbl> <|> <|> <chr>   <chr>    <chr>     
#>  1 ENSG00000000003 SRR1039508 |      723 |   |   control N61311   GSM1275862
#>  2 ENSG00000000005 SRR1039508 |        0 |   |   control N61311   GSM1275862
#>  3 ENSG00000000419 SRR1039508 |      467 |   |   control N61311   GSM1275862
#>  4 ENSG00000000457 SRR1039508 |      347 |   |   control N61311   GSM1275862
#>  5 ENSG00000000460 SRR1039508 |       96 |   |   control N61311   GSM1275862
#>  6 ENSG00000283115 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#>  7 ENSG00000283116 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#>  8 ENSG00000283119 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#>  9 ENSG00000283120 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 10 ENSG00000283123 SRR1039521 |        0 |   |   treated N061011  GSM1275875

# or equivalently:

se_airway %>% print(design = "plyxp")
#> # A tibble: 10 × 9
#>    .features       .samples   `|` counts `|` `|` dex     celltype geo_id    
#>    <chr>           <chr>      <|>  <dbl> <|> <|> <chr>   <chr>    <chr>     
#>  1 ENSG00000000003 SRR1039508 |      723 |   |   control N61311   GSM1275862
#>  2 ENSG00000000005 SRR1039508 |        0 |   |   control N61311   GSM1275862
#>  3 ENSG00000000419 SRR1039508 |      467 |   |   control N61311   GSM1275862
#>  4 ENSG00000000457 SRR1039508 |      347 |   |   control N61311   GSM1275862
#>  5 ENSG00000000460 SRR1039508 |       96 |   |   control N61311   GSM1275862
#>  6 ENSG00000283115 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#>  7 ENSG00000283116 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#>  8 ENSG00000283119 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#>  9 ENSG00000283120 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 10 ENSG00000283123 SRR1039521 |        0 |   |   treated N061011  GSM1275875

You can also limit the number of displayed rows by setting n_print (or a similar argument in your code):

se_airway %>% print(design = "plyxp", n_print = 5)
#> # A tibble: 5 × 9
#>   .features       .samples   `|` counts `|` `|` dex     celltype geo_id    
#>   <chr>           <chr>      <|>  <dbl> <|> <|> <chr>   <chr>    <chr>     
#> 1 ENSG00000000003 SRR1039508 |      723 |   |   control N61311   GSM1275862
#> 2 ENSG00000000005 SRR1039508 |        0 |   |   control N61311   GSM1275862
#> 3 ENSG00000000419 SRR1039508 |      467 |   |   control N61311   GSM1275862
#> 4 ENSG00000283120 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 5 ENSG00000283123 SRR1039521 |        0 |   |   treated N061011  GSM1275875

2.5 alternative_1

For a more compact view (top and bottom rows), similar to a plyxp/tidyverse style with tidySummarizedExperiment header and customised row_id:

se_airway %>% print(design = 4)
#> # A SummarizedExperiment-tibble abstraction: 309,552 × 9
#> # Features=38694 | Samples=8 | Assays=counts
#>        .features       .samples   `|` counts `|` `|` dex     celltype geo_id    
#>        <chr>           <chr>      <|>  <dbl> <|> <|> <chr>   <chr>    <chr>     
#>      1 ENSG00000000003 SRR1039508 |      723 |   |   control N61311   GSM1275862
#>      2 ENSG00000000005 SRR1039508 |        0 |   |   control N61311   GSM1275862
#>      3 ENSG00000000419 SRR1039508 |      467 |   |   control N61311   GSM1275862
#>      4 ENSG00000000457 SRR1039508 |      347 |   |   control N61311   GSM1275862
#>      5 ENSG00000000460 SRR1039508 |       96 |   |   control N61311   GSM1275862
#> 309548 ENSG00000283115 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 309549 ENSG00000283116 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 309550 ENSG00000283119 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 309551 ENSG00000283120 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 309552 ENSG00000283123 SRR1039521 |        0 |   |   treated N061011  GSM1275875

# or

se_airway %>% print(design = "alternative_1")
#> # A SummarizedExperiment-tibble abstraction: 309,552 × 9
#> # Features=38694 | Samples=8 | Assays=counts
#>        .features       .samples   `|` counts `|` `|` dex     celltype geo_id    
#>        <chr>           <chr>      <|>  <dbl> <|> <|> <chr>   <chr>    <chr>     
#>      1 ENSG00000000003 SRR1039508 |      723 |   |   control N61311   GSM1275862
#>      2 ENSG00000000005 SRR1039508 |        0 |   |   control N61311   GSM1275862
#>      3 ENSG00000000419 SRR1039508 |      467 |   |   control N61311   GSM1275862
#>      4 ENSG00000000457 SRR1039508 |      347 |   |   control N61311   GSM1275862
#>      5 ENSG00000000460 SRR1039508 |       96 |   |   control N61311   GSM1275862
#> 309548 ENSG00000283115 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 309549 ENSG00000283116 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 309550 ENSG00000283119 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 309551 ENSG00000283120 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 309552 ENSG00000283123 SRR1039521 |        0 |   |   treated N061011  GSM1275875

You can also limit the number of displayed rows by setting n_print (or a similar argument in your code):

se_airway %>% print(design = "alternative_1", n_print = 5)
#> # A SummarizedExperiment-tibble abstraction: 309,552 × 9
#> # Features=38694 | Samples=8 | Assays=counts
#>        .features       .samples   `|` counts `|` `|` dex     celltype geo_id    
#>        <chr>           <chr>      <|>  <dbl> <|> <|> <chr>   <chr>    <chr>     
#>      1 ENSG00000000003 SRR1039508 |      723 |   |   control N61311   GSM1275862
#>      2 ENSG00000000005 SRR1039508 |        0 |   |   control N61311   GSM1275862
#>      3 ENSG00000000419 SRR1039508 |      467 |   |   control N61311   GSM1275862
#> 309551 ENSG00000283120 SRR1039521 |        0 |   |   treated N061011  GSM1275875
#> 309552 ENSG00000283123 SRR1039521 |        0 |   |   treated N061011  GSM1275875

Session info

sessionInfo()
#> R version 4.4.0 (2024-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Red Hat Enterprise Linux 9.4 (Plow)
#> 
#> Matrix products: default
#> BLAS/LAPACK: FlexiBLAS OPENBLAS;  LAPACK version 3.10.1
#> 
#> locale:
#>  [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
#>  [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
#>  [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Australia/Melbourne
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] tidyprint_0.0.1 tidyr_1.3.1     dplyr_1.1.4    
#> 
#> loaded via a namespace (and not attached):
#>  [1] utf8_1.2.4                  sass_0.4.9                 
#>  [3] generics_0.1.3              SparseArray_1.6.1          
#>  [5] stringi_1.8.4               lattice_0.22-6             
#>  [7] digest_0.6.37               magrittr_2.0.3             
#>  [9] evaluate_1.0.3              grid_4.4.0                 
#> [11] fastmap_1.2.0               rprojroot_2.0.4            
#> [13] jsonlite_1.8.9              Matrix_1.7-2               
#> [15] GenomeInfoDb_1.42.3         httr_1.4.7                 
#> [17] fansi_1.0.6                 purrr_1.0.4                
#> [19] UCSC.utils_1.2.0            jquerylib_0.1.4            
#> [21] abind_1.4-8                 cli_3.6.4                  
#> [23] rlang_1.1.5                 crayon_1.5.3               
#> [25] XVector_0.46.0              Biobase_2.66.0             
#> [27] withr_3.0.2                 cachem_1.1.0               
#> [29] DelayedArray_0.32.0         yaml_2.3.10                
#> [31] S4Arrays_1.6.0              tools_4.4.0                
#> [33] GenomeInfoDbData_1.2.13     SummarizedExperiment_1.36.0
#> [35] BiocGenerics_0.52.0         vctrs_0.6.5                
#> [37] R6_2.6.0                    matrixStats_1.5.0          
#> [39] stats4_4.4.0                lifecycle_1.0.4            
#> [41] stringr_1.5.1               zlibbioc_1.52.0            
#> [43] S4Vectors_0.44.0            IRanges_2.40.0             
#> [45] pkgconfig_2.0.3             pillar_1.10.1              
#> [47] bslib_0.9.0                 glue_1.8.0                 
#> [49] xfun_0.50                   tibble_3.2.1               
#> [51] GenomicRanges_1.58.0        tidyselect_1.2.1           
#> [53] rstudioapi_0.17.1           MatrixGenerics_1.18.1      
#> [55] knitr_1.49                  htmltools_0.5.8.1          
#> [57] rmarkdown_2.29              compiler_4.4.0