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).
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SEtools")
Or, to install the latest development version:
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.
## 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:
If more than one assay is present, one can specify a different scaling behavior for each assay:
Differences to the cbind
method include prefixes added
to column names, optional scaling, handling of metadata (e.g. for
sechm
)
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:
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...
A single SE can also be aggregated by using the aggSE
function:
## Aggregation methods for each assay:
## counts: sum; logcpm: expsum
## class: SummarizedExperiment
## dim: 26 10
## metadata(0):
## assays(2): counts logcpm
## rownames(26): 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.
This is similar to scuttle::sumCountsAcrossFeatures
, but
preserves other SE slots.
## R version 4.4.1 (2024-06-14)
## 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] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] SEtools_1.19.0 sechm_1.13.1
## [3] ComplexHeatmap_2.21.0 SummarizedExperiment_1.35.1
## [5] Biobase_2.65.1 GenomicRanges_1.57.1
## [7] GenomeInfoDb_1.41.1 IRanges_2.39.2
## [9] S4Vectors_0.43.2 BiocGenerics_0.51.1
## [11] MatrixGenerics_1.17.0 matrixStats_1.3.0
## [13] BiocStyle_2.33.1
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 rlang_1.1.4 magrittr_2.0.3
## [4] clue_0.3-65 GetoptLong_1.0.5 RSQLite_2.3.7
## [7] compiler_4.4.1 mgcv_1.9-1 png_0.1-8
## [10] vctrs_0.6.5 sva_3.53.0 stringr_1.5.1
## [13] pkgconfig_2.0.3 shape_1.4.6.1 crayon_1.5.3
## [16] fastmap_1.2.0 XVector_0.45.0 ca_0.71.1
## [19] utf8_1.2.4 rmarkdown_2.28 UCSC.utils_1.1.0
## [22] bit_4.0.5 xfun_0.47 zlibbioc_1.51.1
## [25] cachem_1.1.0 jsonlite_1.8.8 blob_1.2.4
## [28] highr_0.11 DelayedArray_0.31.11 BiocParallel_1.39.0
## [31] parallel_4.4.1 cluster_2.1.6 R6_2.5.1
## [34] bslib_0.8.0 stringi_1.8.4 RColorBrewer_1.1-3
## [37] limma_3.61.9 genefilter_1.87.0 jquerylib_0.1.4
## [40] Rcpp_1.0.13 iterators_1.0.14 knitr_1.48
## [43] splines_4.4.1 Matrix_1.7-0 abind_1.4-5
## [46] yaml_2.3.10 TSP_1.2-4 doParallel_1.0.17
## [49] codetools_0.2-20 curl_5.2.2 lattice_0.22-6
## [52] tibble_3.2.1 KEGGREST_1.45.1 evaluate_0.24.0
## [55] Rtsne_0.17 survival_3.7-0 zip_2.3.1
## [58] Biostrings_2.73.1 circlize_0.4.16 pillar_1.9.0
## [61] BiocManager_1.30.25 foreach_1.5.2 ggplot2_3.5.1
## [64] munsell_0.5.1 scales_1.3.0 xtable_1.8-4
## [67] glue_1.7.0 pheatmap_1.0.12 maketools_1.3.0
## [70] tools_4.4.1 sys_3.4.2 data.table_1.16.0
## [73] openxlsx_4.2.7 annotate_1.83.0 locfit_1.5-9.10
## [76] registry_0.5-1 XML_3.99-0.17 buildtools_1.0.0
## [79] seriation_1.5.6 AnnotationDbi_1.67.0 edgeR_4.3.14
## [82] colorspace_2.1-1 nlme_3.1-166 GenomeInfoDbData_1.2.12
## [85] randomcoloR_1.1.0.1 cli_3.6.3 fansi_1.0.6
## [88] S4Arrays_1.5.7 V8_5.0.0 gtable_0.3.5
## [91] DESeq2_1.45.3 sass_0.4.9 digest_0.6.37
## [94] SparseArray_1.5.31 rjson_0.2.22 memoise_2.0.1
## [97] htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7
## [100] GlobalOptions_0.1.2 statmod_1.5.0 bit64_4.0.5