SEtools
26 October 2021
Abstract
Showcases the use of SEtools to merge objects of the SummarizedExperiment class, melt them, and plot annotated heatmaps from them.Package
SEtools 1.8.0
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.
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 Heatmaps
There are two main wrappers for plotting heatmaps from SummarizedExperiment objects:
- the
sehmfunction uses the pheatmap engine - the
sechmfunction uses the ComplexHeatmap engine
Both functions were made to function very similarly, but the sechm function is especially useful to combine heatmaps (for instance, from different SummarizedExperiment objects). We’ll showcase sehm (the main functionalities being replicable with sechm), and will then provide examples of multiple heatmaps.
1.3.1 sehm
The sehm function simplifies the generation of heatmaps from SummarizedExperiment. It uses pheatmap, so any argument supported by it can in principle be passed:
g <- c("Egr1", "Nr4a1", "Fos", "Egr2", "Sgk1", "Arc", "Dusp1", "Fosb", "Sik1")
sehm(SE, genes=g)## Warning: 'sehm' is deprecated; please use sechm::sechm instead## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
sehm(SE, assayName="logcpm", genes=g, do.scale=TRUE)## Warning: 'sehm' is deprecated; please use sechm::sechm instead## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
When scaling data, the function will automatically center the colour scale around zero, and handle the extreme values (0.5% percentile on each side) in a non-linear fashion to retain a useful visualization.
This behavior can be manually controlled via the breaks parameter (either setting it to FALSE, to a percentile until which the scale should be linear, of manually inputting breaks).
Annotation from the object’s rowData and colData can be plotted simply by specifying the column name (some will be shown by default if found):
sehm(SE, assayName="logcpm", genes=g, do.scale=TRUE, anno_rows="meanTPM")## Warning: 'sehm' is deprecated; please use sechm::sechm instead## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
These can also be used to create gaps:
sehm(SE, genes=g, do.scale=TRUE, anno_rows="meanTPM", gaps_at="Condition")## Warning: 'sehm' is deprecated; please use sechm::sechm instead## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
The specific assay to use for plotting can be specified with the assayName argument.
1.3.1.1 Row/column ordering
By default, rows are sorted not with hierarchical clustering, but from the angle on a MDS plot, which tends to give nicer results than bottom-up hierarchical clustering. This can be disabled using sortRowsOn=NULL or cluster_rows=TRUE (to avoid any row reordering and use the order given, use sortRowsOn=NULL, cluster_rows=FALSE). Column clustering is disabled by default, but this can be changed with cluster_cols=TRUE.
It is common to cluster features into groups, and such a clustering can be used simultaneously with row sorting using the toporder argument. For instance:
lfcs <- assays(SE)$logcpm-rowMeans(assays(SE)$logcpm[,which(SE$Condition=="Homecage")])
rowData(SE)$cluster <- as.character(kmeans(lfcs,4)$cluster)
sehm(SE, genes=g, do.scale=TRUE, anno_rows="cluster", toporder="cluster", gaps_at="Condition")## Warning: 'sehm' is deprecated; please use sechm::sechm instead## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
1.3.1.2 Default arguments
For some arguments (for instance colors), if they are not specified in the function call, SEtools will try to see whether the object itself contains it, or whether the corresponding global options have been set, before using default colors. This means that if, in the context of a given project, the same colors are repeatedly being used, they can be specified a single time, and all subsequent plots will be affected.
Storing colors in the object:
metadata(SE)$hmcols <- c("purple","white","gold")
# or something fancier, like:
# metadata(SE)$hmcols <- colorspace::diverging_hcl(palette="Berlin", n=101)
ancols <- list( Condition=c( Homecage="#DB918B",
Handling="#B86FD3",
Restraint="#A9CED5",
Swim="#B5DF7C" ) )
metadata(SE)$anno_colors <- ancols
sehm(SE, g, do.scale = TRUE)## Warning: 'sehm' is deprecated; please use sechm::sechm instead## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
Using the global options:
options("SEtools_def_hmcols"=c("white","grey","black"))
options("SEtools_def_anno_colors"=ancols)
sehm(SE, g, do.scale = TRUE)## Warning: 'sehm' is deprecated; please use sechm::sechm instead## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
At the moment, the following arguments can be set as global options:
assayName, hmcols, anno_columns, anno_rows, anno_colors, gaps_at, breaks.
Options must be set with the prefix SEtools_def_, followed by the name of the argument.
To remove the predefined colors:
resetAllSEtoolsOptions()
metadata(SE)$hmcols <- NULL
metadata(SE)$anno_colors <- NULLIn order of priority, the arguments in the function call trump the object’s metadata, which trumps the global options.
1.3.2 sechm and crossHm
The sechm function works like the sehm function, but the fact that it outputs a Heatmap object from ComplexHeatmap means that these can be easily combined:
sechm(SE, g, do.scale = TRUE) + sechm(SE, g, do.scale = FALSE)## Warning: `sechm` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sechm`) instead## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
## Warning: `sechm` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sechm`) instead## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead## Warning: Row names of heatmap 2 are not consistent with the main heatmap (1). It
## may lead to wrong conclusion of your data. Please double check.
However, doing so involves manual work to ensure that the labels and colors are nice and coherent, and that the rows names match. As a convenience, we provide the crossHm function to handle these issues. crossHm works with a list of SummarizedExperiment objects:
# we build another SE object:
SE2 <- SE
assays(SE2)$logcpm <- jitter(assays(SE2)$logcpm, factor=1000)
crossHm(list(SE1=SE, SE2=SE2), g, do.scale = TRUE)## Warning: `crossHm` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::crossHm`) instead## Using assay logcpm
## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead## Warning: `sechm` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sechm`) instead
## Warning: `sechm` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sechm`) instead
A unique color scale can be enforced:
crossHm(list(SE1=SE, SE2=SE2), g, do.scale = TRUE, uniqueScale = TRUE)## Warning: `crossHm` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::crossHm`) instead## Using assay logcpm
## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead## Warning: `sechm` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sechm`) instead
## Warning: `sechm` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sechm`) instead
1.4 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(3): meanCPM meanTPM cluster
## 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))1.4.1 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...sehm(se3, genes=row.names(se3))## Warning: 'sehm' is deprecated; please use sechm::sechm instead## Using assay logcpm## Warning: `sortRows` has been moved and will eventually be removed from this package.
## Please use the version from the 'sechm' package (`sechm::sortRows`) instead
1.4.2 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: 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 ConditionIf the aggregation function(s) are not specified, aggSE will try to guess decent aggregation functions from the assay names.
1.5 Melting SE
To facilitate plotting features with ggplot2, the meltSE function combines assay values along with row/column data:
d <- meltSE(SE, genes=g[1:4])
head(d)## feature sample Region Condition counts logcpm
## 1 Egr1 HC.Homecage.1 HC Homecage 1581.0 4.4284969
## 2 Nr4a1 HC.Homecage.1 HC Homecage 750.0 3.6958917
## 3 Fos HC.Homecage.1 HC Homecage 91.4 1.7556317
## 4 Egr2 HC.Homecage.1 HC Homecage 15.1 0.5826999
## 5 Egr1 HC.Homecage.2 HC Homecage 1423.0 4.4415828
## 6 Nr4a1 HC.Homecage.2 HC Homecage 841.0 3.9237691suppressPackageStartupMessages(library(ggplot2))
ggplot(d, aes(Condition, counts, fill=Condition)) + geom_violin() +
facet_wrap(~feature, scale="free")
Figure 1: An example ggplot created from a melted SE
1.6 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 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
##
## 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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggplot2_3.3.5 SEtools_1.8.0
## [3] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [5] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [7] IRanges_2.28.0 S4Vectors_0.32.0
## [9] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [11] matrixStats_0.61.0 BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_2.0-2 rjson_0.2.20
## [4] ellipsis_0.3.2 circlize_0.4.13 XVector_0.34.0
## [7] GlobalOptions_0.1.2 clue_0.3-60 farver_2.1.0
## [10] bit64_4.0.5 AnnotationDbi_1.56.0 fansi_0.5.0
## [13] codetools_0.2-18 splines_4.1.1 doParallel_1.0.16
## [16] cachem_1.0.6 geneplotter_1.72.0 knitr_1.36
## [19] jsonlite_1.7.2 annotate_1.72.0 cluster_2.1.2
## [22] png_0.1-7 pheatmap_1.0.12 BiocManager_1.30.16
## [25] compiler_4.1.1 httr_1.4.2 assertthat_0.2.1
## [28] Matrix_1.3-4 fastmap_1.1.0 limma_3.50.0
## [31] htmltools_0.5.2 tools_4.1.1 gtable_0.3.0
## [34] glue_1.4.2 GenomeInfoDbData_1.2.7 dplyr_1.0.7
## [37] V8_3.4.2 Rcpp_1.0.7 jquerylib_0.1.4
## [40] vctrs_0.3.8 Biostrings_2.62.0 nlme_3.1-153
## [43] iterators_1.0.13 xfun_0.27 stringr_1.4.0
## [46] openxlsx_4.2.4 lifecycle_1.0.1 XML_3.99-0.8
## [49] edgeR_3.36.0 zlibbioc_1.40.0 scales_1.1.1
## [52] TSP_1.1-11 parallel_4.1.1 RColorBrewer_1.1-2
## [55] ComplexHeatmap_2.10.0 yaml_2.2.1 curl_4.3.2
## [58] memoise_2.0.0 sass_0.4.0 stringi_1.7.5
## [61] RSQLite_2.2.8 highr_0.9 randomcoloR_1.1.0.1
## [64] genefilter_1.76.0 foreach_1.5.1 seriation_1.3.1
## [67] zip_2.2.0 BiocParallel_1.28.0 shape_1.4.6
## [70] rlang_0.4.12 pkgconfig_2.0.3 bitops_1.0-7
## [73] evaluate_0.14 lattice_0.20-45 purrr_0.3.4
## [76] labeling_0.4.2 bit_4.0.4 tidyselect_1.1.1
## [79] magrittr_2.0.1 bookdown_0.24 DESeq2_1.34.0
## [82] R6_2.5.1 magick_2.7.3 generics_0.1.1
## [85] DelayedArray_0.20.0 DBI_1.1.1 withr_2.4.2
## [88] mgcv_1.8-38 pillar_1.6.4 survival_3.2-13
## [91] KEGGREST_1.34.0 RCurl_1.98-1.5 tibble_3.1.5
## [94] crayon_1.4.1 utf8_1.2.2 rmarkdown_2.11
## [97] GetoptLong_1.0.5 locfit_1.5-9.4 grid_4.1.1
## [100] sva_3.42.0 data.table_1.14.2 blob_1.2.2
## [103] digest_0.6.28 xtable_1.8-4 munsell_0.5.0
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