Contents

1 Getting started

The SEtools package is a set of convenience functions for the Bioconductor class SummarizedExperiment. It facilitates merging, melting, and plotting SummarizedExperiment objects.

1.1 Package installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("SEtools")

NOTE that the heatmap-related functions have been moved to a standalone package, sechm.

Or, to install the latest development version:

BiocManager::install("plger/SEtools")

1.2 Example data

To showcase the main functions, we will use an example object which contains (a subset of) whole-hippocampus RNAseq of mice after different stressors:

suppressPackageStartupMessages({
  library(SummarizedExperiment)
  library(SEtools)
})
data("SE", package="SEtools")
SE
## class: SummarizedExperiment 
## dim: 100 20 
## metadata(0):
## assays(2): counts logcpm
## rownames(100): Egr1 Nr4a1 ... CH36-200G6.4 Bhlhe22
## rowData names(2): meanCPM meanTPM
## colnames(20): HC.Homecage.1 HC.Homecage.2 ... HC.Swim.4 HC.Swim.5
## colData names(2): Region Condition

This is taken from Floriou-Servou et al., Biol Psychiatry 2018.

1.3 Merging and aggregating SEs

se1 <- SE[,1:10]
se2 <- SE[,11:20]
se3 <- mergeSEs( list(se1=se1, se2=se2) )
se3
## class: SummarizedExperiment 
## dim: 100 20 
## metadata(3): se1 se2 anno_colors
## assays(2): counts logcpm
## rownames(100): AC139063.2 Actr6 ... Zfp667 Zfp930
## rowData names(2): meanCPM meanTPM
## colnames(20): se1.HC.Homecage.1 se1.HC.Homecage.2 ... se2.HC.Swim.4
##   se2.HC.Swim.5
## colData names(3): Dataset Region Condition

All assays were merged, along with rowData and colData slots.

By default, row z-scores are calculated for each object when merging. This can be prevented with:

se3 <- mergeSEs( list(se1=se1, se2=se2), do.scale=FALSE)

If more than one assay is present, one can specify a different scaling behavior for each assay:

se3 <- mergeSEs( list(se1=se1, se2=se2), use.assays=c("counts", "logcpm"), do.scale=c(FALSE, TRUE))

1.3.1 Merging by rowData columns

It is also possible to merge by rowData columns, which are specified through the mergeBy argument. In this case, one can have one-to-many and many-to-many mappings, in which case two behaviors are possible:

  • By default, all combinations will be reported, which means that the same feature of one object might appear multiple times in the output because it matches multiple features of another object.
  • If a function is passed through aggFun, the features of each object will by aggregated by mergeBy using this function before merging.
rowData(se1)$metafeature <- sample(LETTERS,nrow(se1),replace = TRUE)
rowData(se2)$metafeature <- sample(LETTERS,nrow(se2),replace = TRUE)
se3 <- mergeSEs( list(se1=se1, se2=se2), do.scale=FALSE, mergeBy="metafeature", aggFun=median)
## Aggregating the objects by metafeature
## Merging...
sechm::sechm(se3, features=row.names(se3))

1.3.2 Aggregating a SE

A single SE can also be aggregated by using the aggSE function:

se1b <- aggSE(se1, by = "metafeature")
## Aggregation methods for each assay:
## counts: sum; logcpm: expsum
se1b
## class: SummarizedExperiment 
## dim: 25 10 
## metadata(0):
## assays(2): counts logcpm
## rownames(25): A B ... Y Z
## rowData names(0):
## colnames(10): HC.Homecage.1 HC.Homecage.2 ... HC.Handling.4
##   HC.Handling.5
## colData names(2): Region Condition

If the aggregation function(s) are not specified, aggSE will try to guess decent aggregation functions from the assay names.


1.4 Other convenience functions

Calculate an assay of log-foldchanges to the controls:

SE <- log2FC(SE, fromAssay="logcpm", controls=SE$Condition=="Homecage")



Session info

## R version 4.2.1 (2022-06-23)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.0
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] SEtools_1.12.0              sechm_1.6.0                
##  [3] SummarizedExperiment_1.28.0 Biobase_2.58.0             
##  [5] GenomicRanges_1.50.1        GenomeInfoDb_1.34.2        
##  [7] IRanges_2.32.0              S4Vectors_0.36.0           
##  [9] BiocGenerics_0.44.0         MatrixGenerics_1.10.0      
## [11] matrixStats_0.62.0          BiocStyle_2.26.0           
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.16             colorspace_2.0-3       rjson_0.2.21          
##   [4] ellipsis_0.3.2         circlize_0.4.15        XVector_0.38.0        
##   [7] GlobalOptions_0.1.2    clue_0.3-61            bit64_4.0.5           
##  [10] AnnotationDbi_1.60.0   fansi_1.0.3            codetools_0.2-18      
##  [13] splines_4.2.1          doParallel_1.0.17      cachem_1.0.6          
##  [16] geneplotter_1.76.0     knitr_1.39             jsonlite_1.8.0        
##  [19] Cairo_1.6-0            annotate_1.76.0        cluster_2.1.3         
##  [22] png_0.1-7              pheatmap_1.0.12        BiocManager_1.30.18   
##  [25] compiler_4.2.1         httr_1.4.3             assertthat_0.2.1      
##  [28] Matrix_1.4-1           fastmap_1.1.0          limma_3.54.0          
##  [31] cli_3.3.0              htmltools_0.5.2        tools_4.2.1           
##  [34] gtable_0.3.0           glue_1.6.2             GenomeInfoDbData_1.2.8
##  [37] dplyr_1.0.9            V8_4.2.0               Rcpp_1.0.9            
##  [40] jquerylib_0.1.4        vctrs_0.4.1            Biostrings_2.66.0     
##  [43] nlme_3.1-158           iterators_1.0.14       xfun_0.31             
##  [46] stringr_1.4.0          openxlsx_4.2.5         lifecycle_1.0.1       
##  [49] XML_3.99-0.10          edgeR_3.40.0           zlibbioc_1.44.0       
##  [52] scales_1.2.0           TSP_1.2-1              parallel_4.2.1        
##  [55] RColorBrewer_1.1-3     ComplexHeatmap_2.14.0  yaml_2.3.5            
##  [58] curl_4.3.2             memoise_2.0.1          ggplot2_3.3.6         
##  [61] sass_0.4.1             stringi_1.7.8          RSQLite_2.2.14        
##  [64] highr_0.9              randomcoloR_1.1.0.1    genefilter_1.80.0     
##  [67] foreach_1.5.2          seriation_1.3.5        zip_2.2.0             
##  [70] BiocParallel_1.32.1    shape_1.4.6            rlang_1.0.4           
##  [73] pkgconfig_2.0.3        bitops_1.0-7           evaluate_0.15         
##  [76] lattice_0.20-45        purrr_0.3.4            bit_4.0.4             
##  [79] tidyselect_1.1.2       magrittr_2.0.3         bookdown_0.27         
##  [82] DESeq2_1.38.0          R6_2.5.1               magick_2.7.3          
##  [85] generics_0.1.3         DelayedArray_0.24.0    DBI_1.1.3             
##  [88] mgcv_1.8-40            pillar_1.7.0           survival_3.3-1        
##  [91] KEGGREST_1.38.0        RCurl_1.98-1.7         tibble_3.1.7          
##  [94] crayon_1.5.1           utf8_1.2.2             rmarkdown_2.14        
##  [97] GetoptLong_1.0.5       locfit_1.5-9.6         grid_4.2.1            
## [100] sva_3.46.0             data.table_1.14.2      blob_1.2.3            
## [103] digest_0.6.29          xtable_1.8-4           munsell_0.5.0         
## [106] registry_0.5-1         bslib_0.3.1