SEtools
15 April 2025
Abstract
Showcases the use of SEtools to merge objects of the SummarizedExperiment class.
Package
SEtools 1.22.0
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.
NOTE that the heatmap-related and melting functions have been moved to a standalone package, sechm.
The old sehm function of SEtools should be considered deprecated, and most SEtools functions are conserved for legacy/reproducibility reasons (or until they find a better home).
1.1 Package installation
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SEtools")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 ConditionThis 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 ConditionAll 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))Differences to the cbind method include prefixes added to column names, optional scaling, handling of metadata (e.g. for sechm)
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 bymergeByusing 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: expsumse1b## 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 ConditionIf the aggregation function(s) are not specified, aggSE will try to guess decent aggregation functions from the assay names.
This is similar to scuttle::sumCountsAcrossFeatures, but preserves other SE slots.
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 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] SEtools_1.22.0 sechm_1.16.0
## [3] ComplexHeatmap_2.24.0 SummarizedExperiment_1.38.0
## [5] Biobase_2.68.0 GenomicRanges_1.60.0
## [7] GenomeInfoDb_1.44.0 IRanges_2.42.0
## [9] S4Vectors_0.46.0 BiocGenerics_0.54.0
## [11] generics_0.1.3 MatrixGenerics_1.20.0
## [13] matrixStats_1.5.0 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 rlang_1.1.6 magrittr_2.0.3
## [4] clue_0.3-66 GetoptLong_1.0.5 RSQLite_2.3.9
## [7] compiler_4.5.0 mgcv_1.9-3 png_0.1-8
## [10] vctrs_0.6.5 sva_3.56.0 stringr_1.5.1
## [13] pkgconfig_2.0.3 shape_1.4.6.1 crayon_1.5.3
## [16] fastmap_1.2.0 magick_2.8.6 XVector_0.48.0
## [19] ca_0.71.1 rmarkdown_2.29 UCSC.utils_1.4.0
## [22] tinytex_0.57 bit_4.6.0 xfun_0.52
## [25] cachem_1.1.0 jsonlite_2.0.0 blob_1.2.4
## [28] DelayedArray_0.34.0 BiocParallel_1.42.0 parallel_4.5.0
## [31] cluster_2.1.8.1 R6_2.6.1 bslib_0.9.0
## [34] stringi_1.8.7 RColorBrewer_1.1-3 limma_3.64.0
## [37] genefilter_1.90.0 jquerylib_0.1.4 Rcpp_1.0.14
## [40] bookdown_0.43 iterators_1.0.14 knitr_1.50
## [43] splines_4.5.0 Matrix_1.7-3 tidyselect_1.2.1
## [46] abind_1.4-8 yaml_2.3.10 TSP_1.2-4
## [49] doParallel_1.0.17 codetools_0.2-20 curl_6.2.2
## [52] lattice_0.22-7 tibble_3.2.1 KEGGREST_1.48.0
## [55] evaluate_1.0.3 Rtsne_0.17 survival_3.8-3
## [58] zip_2.3.2 Biostrings_2.76.0 circlize_0.4.16
## [61] pillar_1.10.2 BiocManager_1.30.25 foreach_1.5.2
## [64] ggplot2_3.5.2 munsell_0.5.1 scales_1.3.0
## [67] xtable_1.8-4 glue_1.8.0 pheatmap_1.0.12
## [70] tools_4.5.0 data.table_1.17.0 openxlsx_4.2.8
## [73] annotate_1.86.0 locfit_1.5-9.12 registry_0.5-1
## [76] XML_3.99-0.18 Cairo_1.6-2 seriation_1.5.7
## [79] AnnotationDbi_1.70.0 edgeR_4.6.0 colorspace_2.1-1
## [82] nlme_3.1-168 GenomeInfoDbData_1.2.14 randomcoloR_1.1.0.1
## [85] cli_3.6.4 S4Arrays_1.8.0 dplyr_1.1.4
## [88] V8_6.0.3 gtable_0.3.6 DESeq2_1.48.0
## [91] sass_0.4.10 digest_0.6.37 SparseArray_1.8.0
## [94] rjson_0.2.23 memoise_2.0.1 htmltools_0.5.8.1
## [97] lifecycle_1.0.4 httr_1.4.7 GlobalOptions_0.1.2
## [100] statmod_1.5.0 bit64_4.6.0-1