diffUTR
30 April 2024
Abstract
Showcases the use of the diffUTR package for streamlining analyses of differential exon usage and differential 3’ UTR usage.
Package
diffUTR 1.12.0
Contents
1 Introduction
1.1 Differential exon usage
diffUTR wraps around three methods for different exon usage analysis:
- The DEXSeq package (see
?DEXSeqWrapper) - An improved version of limma’s
diffSplicemethod (see?diffSpliceWrapper) - edgeR’s
diffSpliceDGEmethod (see?diffSpliceDGEWrapper)
All three wrappers have been designed to use the same input (the
RangedSummarizedExperiment object created by countFeatures – see below)
and produce highly similar outputs, so that they can all be used with the same
downstream plotting functions (Figure 1A).
Based on various benchmarks (e.g.
Sonenson et al. 2016;
see also our paper),
DEXSeq is the most accurate of the three methods,
and should therefore be the method of choice. It is however very slow and does
not scale well to larger sample sizes. We therefore suggest our improved
diffSplice2 method (?diffSpliceWrapper) when
DEXSeq is not an option.
Figure 1: A: Overview of the diffUTR workflow
B: Bin creation scheme.
1.2 Differential 3’ UTR usage
A chief difficulty in analyzing 3’ UTR usage is that most UTR variants are not
cataloged in standard transcript annotations, limiting the utility of standard
transcript-level quantification based on reference transcripts. diffUTR
leverages the differential exon usage methods for differential 3’ UTR analysis
using alternative poly-adenylation site databases to create additional UTR
bins (Figure 1B).
2 Getting started
2.1 Package installation
Install the package with:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("diffUTR")We load diffUTR as well as the SummarizedExperiment package on which it depends:
suppressPackageStartupMessages({
library(diffUTR)
library(SummarizedExperiment)
})2.2 Obtaining gene annotations
Prior to using diffUTR you will need a gene annotation. This can either be
provided as a gtf file (with relatively standard formatting), or as an
ensembldb object. For example, an EnsDb object for
the latest mouse annotation can be fetched as follows:
library(AnnotationHub)
ah <- AnnotationHub()
# obtain the identifier of the latest mouse ensembl annotation:
ahid <- rev(query(ah, c("EnsDb", "Mus musculus"))$ah_id)[1]
ensdb <- ah[[ahid]]This ensdb object could then be directly passed to the prepareBins function
(see below).
For the purpose of this vignette, we will use a reduced annotation containing only 100 mouse genes:
data(example_gene_annotation, package="diffUTR")
g <- example_gene_annotation
head(g)## GRanges object with 6 ranges and 6 metadata columns:
## seqnames ranges strand | gene_name type
## <Rle> <IRanges> <Rle> | <factor> <factor>
## ENSMUSE00000751848 1 36307733-36307836 + | Arid5a exon
## ENSMUSG00000037447 1 36307733-36324029 + | Arid5a gene
## ENSMUSE00001245300 1 36307754-36307836 + | Arid5a exon
## ENSMUSE00001257314 1 36307754-36307836 + | Arid5a exon
## ENSMUSE00001257314 1 36307754-36307836 + | Arid5a exon
## ENSMUSE00001257314 1 36307754-36307836 + | Arid5a exon
## gene_biotype tx_biotype tx
## <factor> <factor> <character>
## ENSMUSE00000751848 protein_coding protein_coding ENSMUST00000126413
## ENSMUSG00000037447 NA NA <NA>
## ENSMUSE00001245300 protein_coding protein_coding ENSMUST00000142319
## ENSMUSE00001257314 protein_coding retained_intron ENSMUST00000150747
## ENSMUSE00001257314 protein_coding processed_transcript ENSMUST00000145179
## ENSMUSE00001257314 protein_coding retained_intron ENSMUST00000124280
## gene
## <factor>
## ENSMUSE00000751848 ENSMUSG00000037447
## ENSMUSG00000037447 ENSMUSG00000037447
## ENSMUSE00001245300 ENSMUSG00000037447
## ENSMUSE00001257314 ENSMUSG00000037447
## ENSMUSE00001257314 ENSMUSG00000037447
## ENSMUSE00001257314 ENSMUSG00000037447
## -------
## seqinfo: 118 sequences from GRCm38 genome3 Workflow for differential exon usage (DEU) analysis
3.1 Preparing the annotation
Because exons partially overlap, they must be disjoined in non-overlapping bins
for the purpose of analysis. This is handled by the prepareBins function:
# If you know that your data will be stranded, use
# bins <- prepareBins(g, stranded=TRUE)
# Otherwise use
bins <- prepareBins(g)## Preparing annotation## Merging and disjoining bins3.2 Counting reads in bins
Counting reads (or fragments) overlapping bins is done using the
countFeatures function, with a vector of paths to bam files as input. Under
the hood, this calls the featureCounts function of the
Rsubread package, so any argument of that function
can be passed to countFeatures. For example:
bamfiles <- c("path/to/sample1.bam", "path/to/sample2.bam", ...)
rse <- countFeatures(bamfiles, bins, strandSpecific=2, nthreads=6, isPairedEnd=FALSE)Note that strandSpecific should be set correctly (i.e. to 0 if the data is
unstranded, and to 1 or 2 if stranded – see ?Rsubread::featureCounts), and
isPairedEnd=TRUE should be used if the data is paired-end.
For the purpose of this vignette, we load a pre-computed object containing data from Whipple et al. (2020):
data(example_bin_se, package="diffUTR")
rse <- example_bin_se
rse## class: RangedSummarizedExperiment
## dim: 920 6
## metadata(0):
## assays(1): counts
## rownames(920): ENSMUSG00000000552.1 ENSMUSG00000000552.11 ...
## ENSMUSG00000098794.6 ENSMUSG00000098794.7
## rowData names(7): gene_name type ... meanLogDensity geneAmbiguous
## colnames(6): CTRL1 CTRL2 ... LTP2 LTP3
## colData names(1): conditionThe output of countFeatures is a RangedSummarizedExperiment object (see
SummarizedExperiment) containing the samples’ counts
(as well as normalized assays) across bins, as well as all the bin information
in the rowRanges(se):
head(rowRanges(rse))## GRanges object with 6 ranges and 7 metadata columns:
## seqnames ranges strand | gene_name
## <Rle> <IRanges> <Rle> | <CharacterList>
## ENSMUSG00000000552.1 chr15 103340057-103340086 - | Zfp385a
## ENSMUSG00000000552.11 chr15 103313895-103314997 - | Zfp385a
## ENSMUSG00000000552.2 chr15 103340047-103340056 - | Zfp385a
## ENSMUSG00000000552.3 chr15 103333065-103333201 - | Zfp385a
## ENSMUSG00000000552.4 chr15 103320290-103320400 - | Zfp385a
## ENSMUSG00000000552.5 chr15 103317949-103318111 - | Zfp385a
## type gene meanLogCPM logWidth
## <factor> <factor> <numeric> <numeric>
## ENSMUSG00000000552.1 UTR ENSMUSG00000000552 0.134936 3.43399
## ENSMUSG00000000552.11 UTR/3UTR ENSMUSG00000000552 0.454942 7.00670
## ENSMUSG00000000552.2 CDS ENSMUSG00000000552 0.136078 2.39790
## ENSMUSG00000000552.3 CDS ENSMUSG00000000552 0.233634 4.92725
## ENSMUSG00000000552.4 CDS ENSMUSG00000000552 0.206431 4.71850
## ENSMUSG00000000552.5 CDS ENSMUSG00000000552 0.211493 5.09987
## meanLogDensity geneAmbiguous
## <numeric> <logical>
## ENSMUSG00000000552.1 0.03746455 FALSE
## ENSMUSG00000000552.11 0.00142892 FALSE
## ENSMUSG00000000552.2 0.10854836 FALSE
## ENSMUSG00000000552.3 0.00918315 FALSE
## ENSMUSG00000000552.4 0.01102348 FALSE
## ENSMUSG00000000552.5 0.00755899 FALSE
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthsIn this example only a subset of information was retained, but the original file stores any information present in the annotation (e.g. transcripts, biotype, etc.)
3.3 Differential analysis
For the purpose of this example, we will use the improved diffSplice method:
rse <- diffSpliceWrapper(rse, design = ~condition)## Testing coefficient conditionLTP## Total number of exons: 920
## Total number of genes: 80
## Number of genes with 1 exon: 0
## Mean number of exons in a gene: 12
## Max number of exons in a gene: 45The ~condition formula indicates that the samples should be split according
to the group column of colData(rse). Alternatively to the formula inferace,
a model.matrix can be directly passed. For more complex models involving
several terms, you will have to specify the coefficient to test using the
coef argument.
The bin-wise results of the the differential analysis have been saved in the
rowData of the object:
head(rowData(rse))## DataFrame with 6 rows and 11 columns
## gene_name type gene meanLogCPM
## <CharacterList> <factor> <factor> <numeric>
## ENSMUSG00000000552.1 Zfp385a UTR ENSMUSG00000000552 0.134936
## ENSMUSG00000000552.11 Zfp385a UTR/3UTR ENSMUSG00000000552 0.454942
## ENSMUSG00000000552.2 Zfp385a CDS ENSMUSG00000000552 0.136078
## ENSMUSG00000000552.3 Zfp385a CDS ENSMUSG00000000552 0.233634
## ENSMUSG00000000552.4 Zfp385a CDS ENSMUSG00000000552 0.206431
## ENSMUSG00000000552.5 Zfp385a CDS ENSMUSG00000000552 0.211493
## logWidth meanLogDensity geneAmbiguous logDensityRatio
## <numeric> <numeric> <logical> <numeric>
## ENSMUSG00000000552.1 3.43399 0.03746455 FALSE 2.070401
## ENSMUSG00000000552.11 7.00670 0.00142892 FALSE -1.214023
## ENSMUSG00000000552.2 2.39790 0.10854836 FALSE 3.170190
## ENSMUSG00000000552.3 4.92725 0.00918315 FALSE 0.650240
## ENSMUSG00000000552.4 4.71850 0.01102348 FALSE 0.833778
## ENSMUSG00000000552.5 5.09987 0.00755899 FALSE 0.454890
## coefficient bin.p.value bin.FDR
## <numeric> <numeric> <numeric>
## ENSMUSG00000000552.1 -0.3957715 0.371207 1
## ENSMUSG00000000552.11 -0.0974467 0.701927 1
## ENSMUSG00000000552.2 -0.4178833 0.344547 1
## ENSMUSG00000000552.3 0.0279535 0.932203 1
## ENSMUSG00000000552.4 0.2551920 0.468900 1
## ENSMUSG00000000552.5 0.2423841 0.487095 1The gene-wise aggregation has been saved in the object’s metadata:
perGene <- metadata(rse)$geneLevel
head(perGene)## DataFrame with 6 rows and 11 columns
## name nb.bins w.coef w.abs.coef w.sqWidth
## <character> <numeric> <numeric> <numeric> <integer>
## ENSMUSG00000038290 Smg6 44 1.116 1.559791 33
## ENSMUSG00000038268 Ovca2 3 1.432 1.432243 68
## ENSMUSG00000038894 Irs2 4 0.557 0.851392 57
## ENSMUSG00000033730 Egr3 12 0.344 0.842254 67
## ENSMUSG00000071076 Jund 6 0.657 1.166027 26
## ENSMUSG00000022174 Dad1 11 0.390 0.842301 64
## w.density sizeScore abs.sizeScore geneMeanDensity
## <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000038290 -6.174 0.043 0.049 0.011
## ENSMUSG00000038268 -11.421 0.946 0.946 0.005
## ENSMUSG00000038894 -7.889 0.194 0.283 0.021
## ENSMUSG00000033730 -8.224 0.163 0.188 0.011
## ENSMUSG00000071076 -6.169 0.113 0.230 0.017
## ENSMUSG00000022174 -6.169 0.142 0.158 0.050
## density.ratio q.value
## <numeric> <numeric>
## ENSMUSG00000038290 60.81827 4.73785e-39
## ENSMUSG00000038268 0.10500 9.54745e-07
## ENSMUSG00000038894 1.52049 9.93522e-07
## ENSMUSG00000033730 3.64790 2.98904e-06
## ENSMUSG00000071076 1.94223 4.50984e-06
## ENSMUSG00000022174 44.51854 5.69730e-06The results are described in more detail below.
4 Workflow for differential 3’ UTR usage analysis
4.1 Obtaining alternative poly-adenlyation sites and preparing the bins
The preparation of the bins is done as in the standard DEU case, except that an
additional argument should be given providing the alternative poly-adenylation
(APA) sites. These will be used to break and extend UTRs into further bins.
APA sites can be provided as a GenomicRanges object
or the path to a bed file containing the coordinates. For mouse, human and
C. elegans, an atlas of poly-A sites can be downloaded from
https://polyasite.unibas.ch/atlas
(). You can
download the mouse file:
download.file("https://polyasite.unibas.ch/download/atlas/2.0/GRCm38.96/atlas.clusters.2.0.GRCm38.96.bed.gz",
destfile="apa.mm38.bed.gz")
bins <- prepareBins(g, "apa.mm38.bed.gz")
# (Again, if you know that your data will be stranded, use `stranded=TRUE`)Unfortunately, the polyASite database does not contain APA sites for the rat. A somewhat older similar database, PolyA_DB, does include the rat, however with an obsolete annotation; for convenience we lifted it over to Rno6, and made it available in the package data:
data(rn6_PAS)
# bins <- prepareBins(g, rn6_PAS)If there is no APA sites database that includes your species of interest, you can still perform differential UTR analysis, however you will be limited to bins defined by the UTRs ends included in the gene annotation.
Note that if the gene annotation and the APA sites use different chromosome
notation styles, you can enforce a given notation using the chrStyle argument
of prepareBins. Beware however that there is no internal check that the genome
builds match – be sure that they do!
For the purpose of this example, we’ll just use the same bins as in the first
example.
4.2 Counting and differential analysis:
Counting reads in bins works as in the standard DEU case:
bamfiles <- c("path/to/sample1.bam", "path/to/sample2.bam", ...)
se <- countFeatures(bamfiles, bins, strandSpecific=2, nthreads=6, isPairedEnd=TRUE)Differential analysis also works in the same way as for DEU:
rse <- diffSpliceWrapper( rse, design = ~condition )## Testing coefficient conditionLTP## Total number of exons: 920
## Total number of genes: 80
## Number of genes with 1 exon: 0
## Mean number of exons in a gene: 12
## Max number of exons in a gene: 45By default, this will look for changes in any bin type. To look specifically
for changes in certain types of bins, you can perform the gene-level
aggregation using different filters. This can be done using the
geneLevelStats() function, which accepts the arguments excludeTypes and
includeTypes to decide which bin types to include in the gene-level estimates.
For example:
# we can then only look for changes in CDS bins:
cds <- geneLevelStats(rse, includeTypes="CDS", returnSE=FALSE)
head(cds)## DataFrame with 6 rows and 11 columns
## name nb.bins w.coef w.abs.coef w.sqWidth
## <character> <numeric> <numeric> <numeric> <integer>
## ENSMUSG00000071076 Jund 6 -0.758 0.758416 31
## ENSMUSG00000079037 Prnp 8 -0.430 0.429916 27
## ENSMUSG00000039114 Nrn1 13 1.516 1.551778 10
## ENSMUSG00000038290 Smg6 44 -0.720 0.720179 13
## ENSMUSG00000018102 Hist1h2bc 12 -0.393 0.392852 19
## ENSMUSG00000019505 Ubb 11 -0.234 0.234007 9
## w.density sizeScore abs.sizeScore geneMeanDensity
## <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000071076 -5.238 -0.184 0.184 0.017
## ENSMUSG00000079037 -2.317 -0.048 0.048 0.062
## ENSMUSG00000039114 -4.982 0.086 0.088 0.094
## ENSMUSG00000038290 -5.881 -0.008 0.008 0.011
## ENSMUSG00000018102 -4.727 -0.038 0.038 0.082
## ENSMUSG00000019505 0.256 -0.008 0.008 0.333
## density.ratio q.value
## <numeric> <numeric>
## ENSMUSG00000071076 3.70700 4.50984e-06
## ENSMUSG00000079037 96.80200 3.13531e-05
## ENSMUSG00000039114 5.04196 1.68484e-04
## ENSMUSG00000038290 74.39720 3.82690e-04
## ENSMUSG00000018102 25.09800 2.48807e-02
## ENSMUSG00000019505 783.23642 3.33429e-02# or only look for changes in UTR bins:
utr <- geneLevelStats(rse, includeTypes="UTR", returnSE=FALSE)
head(utr)## DataFrame with 6 rows and 11 columns
## name nb.bins w.coef w.abs.coef w.sqWidth
## <character> <numeric> <numeric> <numeric> <integer>
## ENSMUSG00000038894 Irs2 4 1.149 1.149478 59
## ENSMUSG00000022174 Dad1 11 0.360 0.888266 74
## ENSMUSG00000039114 Nrn1 13 1.246 1.246068 8
## ENSMUSG00000038290 Smg6 44 -0.887 0.886915 28
## ENSMUSG00000059991 Nptx2 12 0.439 0.786649 22
## ENSMUSG00000019505 Ubb 11 -0.230 0.230037 9
## w.density sizeScore abs.sizeScore geneMeanDensity
## <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000038894 -9.594 0.390 0.390 0.021
## ENSMUSG00000022174 -8.036 0.161 0.184 0.050
## ENSMUSG00000039114 -5.311 0.047 0.047 0.094
## ENSMUSG00000038290 -6.782 -0.013 0.013 0.011
## ENSMUSG00000059991 -7.579 0.078 0.102 0.031
## ENSMUSG00000019505 0.217 -0.008 0.008 0.333
## density.ratio q.value
## <numeric> <numeric>
## ENSMUSG00000038894 0.46500 9.93522e-07
## ENSMUSG00000022174 11.84141 5.69730e-06
## ENSMUSG00000039114 3.76888 1.58361e-03
## ENSMUSG00000038290 39.68855 7.34288e-03
## ENSMUSG00000059991 1.68266 1.01491e-02
## ENSMUSG00000019505 760.47865 3.33429e-02# or only look for changes in bins that _could be_ UTRs:
# geneLevelStats(rse, excludeTypes=c("CDS","non-coding"), returnSE=FALSE)5 Exploring the results
5.1 Top genes
plotTopGenes() provides gene-level statistic plots (similar to a ‘volcano
plot’), which come in two variations in terms of aggregated effect size
(x-axis). For standard DEU analysis, we take the weighted average of absolute
bin-level coefficients, using the bins’ -log10(p-value) as weights, to
produce gene-level estimates of effect sizes. For differential 3’ UTR usage,
where bins are expected to have consistent directions (i.e. lengthening or
shortening of the UTR) and where their size is expected to have a strong impact
on biological function, the signed bin-level coefficients are weighted both by
both size and significance to produce (signed) gene-level estimates of effect
sizes.
plotTopGenes(rse)
By default, the size of the points reflects the relative expression of the
genes, and the colour the relative expression of the significant bins with
respect to the gene (density ratio). This enables the rapid identification of
changes occuring in highly-expressed genes, as well as discount changes
happening in bins which tend not to be included in transcripts from that gene
(low density ratio). Note that it is possible to use filter by density ratio
during gene-level aggregation (see ?geneLevelStats).
The product is a ggplot object, and can be manipulated as such.
By default, the gene-level statistics computed use all bin types. However,
plotTopGenes also accepts directly the gene-level stats, as outputted by
geneLevelStats (see above), enabling the visualization of top genes
separately for CDS and UTRs, for instance:
# `utr` being the output of the above
# geneLevelStats(rse, includeTypes="UTR", returnSE=FALSE)
plotTopGenes(utr, diffUTR = TRUE)
Here we also indicated that we are in differential UTR mode, to use signed and width-weighted aggregation.
Note that when there are too many gene labels to be plotted nearby, ggrepel
will omit them and produce the warning above. To plot them nevertheless, you
can pass the argument max.overlaps with a higher value (see
?ggrepel::geom_text_repel).
5.2 Gene profiles
The package also offers two functions for plotting bin-level profiles for a specific gene. Before doing so, we generate normalized assays if this was not already done:
rse <- addNormalizedAssays(rse)deuBinPlot provides plots similar to those produced by
DEXSeq and limma, but
offering more flexibility. They can be plotted as overall bin statistics, per
condition, or per sample, and can plot various types of values. Importantly,
since all data and annotation is contained in the object, these can easily be
included in the plots, mapping for instance to colour or shape:
deuBinPlot(rse,"Jund")## Warning: The `guide` argument in `scale_*()` cannot be `FALSE`. This was deprecated in
## ggplot2 3.3.4.
## ℹ Please use "none" instead.
## ℹ The deprecated feature was likely used in the diffUTR package.
## Please report the issue at <https://github.com/ETHZ-INS/diffUTR>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
deuBinPlot(rse,"Jund", type="condition", colour="condition")
deuBinPlot(rse,"Jund", type="sample", colour="condition") # shows separate samples
And since the output is a ggplot, we can modify it at will:
library(ggplot2)
deuBinPlot(rse,"Jund",type="condition",colour="condition") +
guides(colour = guide_legend(override.aes = list(size = 3))) +
scale_colour_manual(values=c(CTRL="darkblue", LTP="red"))
Finally, geneBinHeatmap provides a compact, bin-per-sample heatmap
representation of a gene, allowing the simultaneous visualization of various
information:
geneBinHeatmap(rse, "Smg6")
Bins that are overlapping multiple genes will be flagged as such (here there are none).
We found these representations particularly useful to prioritize candidates
from differential bin usage analyses. For example, many genes show differential
usage of bins which are generally not included in most transcripts of that gene
(low count density), and are therefore less likely to be relevant (note that
it is possible to use filter by density ratio during gene-level aggregation –
see ?geneLevelStats). On the other hand, some genes show massive expression
of a single bin which might (or might not) represent a different, unannotated
transcript.
5.2.1 Overlaying with transcripts
The ggbio package has functionalities enabling to plot
bin-level information along side transcript tracks. While wrappers for this are
not included in the diffUTR package so as to keep dependencies low, the
function below would be one way of using this with rse objects from diffUTR
as well as the EnsDb object used to create the bins:
#' binTxPlot
#'
#' @param se A bin-wise SummarizedExperiment as produced by
#' \code{\link{countFeatures}} and including bin-level tests (i.e. having been
#' passed through one of the DEU wrappers such as
#' \code{\link{diffSpliceWrapper}} or \code{\link{DEXSeqWrapper}})
#' @param ensdb The `EnsDb` which was used to create the bins
#' @param gene The gene of interest
#' @param by The colData column of `se` used to split the samples
#' @param assayName The assay to plot
#' @param removeAmbiguous Logical; whether to remove bins that are
#' gene-ambiguous (i.e. overlap multiple genes).
#' @param size Size of the lines
#' @param ... Passed to `plotRangesLinkedToData`
#'
#' @return A `ggbio` `Tracks`
#' @importFrom AnnotationFilter GeneNameFilter GeneIdFilter
#' @importFrom ensembldb getGeneRegionTrackForGviz
#' @importFrom ggbio plotRangesLinkedToData autoplot
#' @export
binTxPlot <- function(se, ensdb, gene, by=NULL, assayName=c("logNormDensity"),
removeAmbiguous=TRUE, size=3, threshold=0.05, ...){
w <- diffUTR:::.matchGene(se, gene)
se <- sort(se[w,])
if(removeAmbiguous) se <- se[!rowData(se)$geneAmbiguous,]
if(length(w)==0) return(NULL)
if(!is.null(by)) by <- match.arg(by, colnames(colData(se)))
assayName <- match.arg(assayName, assayNames(se))
if(rowData(se)$gene[[1]]==gene){
filt <- GeneIdFilter(gene)
}else{
filt <- GeneNameFilter(gene)
}
gr <- ggbio::autoplot(ensdb, filt)
gr2 <- rowRanges(se)
if(!is.null(by)){
sp <- split(seq_len(ncol(se)), se[[by]])
for(f in names(sp))
mcols(gr2)[[f]] <- rowMeans(assays(se)[[assayName]][,sp[[f]],drop=FALSE])
y <- names(sp)
}else{
mcols(gr2)[[assayName]] <- rowMeans(assays(se)[[assayName]])
y <- assayName
}
ggbio::plotRangesLinkedToData(gr2, stat.y=y, stat.ylab=assayName,
annotation=list(gr),
size=size, ...) + ggtitle(gene)
}
# example usage (not run):
# binTxPlot(rse, ensdb, gene="Arid5a", by="condition")(Additionally requires the ggbio package)
***
sessionInfo()## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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
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## time zone: America/New_York
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##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggplot2_3.5.1 SummarizedExperiment_1.34.0
## [3] Biobase_2.64.0 GenomicRanges_1.56.0
## [5] GenomeInfoDb_1.40.0 IRanges_2.38.0
## [7] S4Vectors_0.42.0 BiocGenerics_0.50.0
## [9] MatrixGenerics_1.16.0 matrixStats_1.3.0
## [11] diffUTR_1.12.0 BiocStyle_2.32.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_1.8.8 shape_1.4.6.1
## [4] magrittr_2.0.3 magick_2.8.3 GenomicFeatures_1.56.0
## [7] farver_2.1.1 rmarkdown_2.26 GlobalOptions_0.1.2
## [10] BiocIO_1.14.0 zlibbioc_1.50.0 vctrs_0.6.5
## [13] Cairo_1.6-2 memoise_2.0.1 Rsamtools_2.20.0
## [16] RCurl_1.98-1.14 tinytex_0.50 htmltools_0.5.8.1
## [19] S4Arrays_1.4.0 progress_1.2.3 curl_5.2.1
## [22] SparseArray_1.4.0 sass_0.4.9 bslib_0.7.0
## [25] httr2_1.0.1 cachem_1.0.8 GenomicAlignments_1.40.0
## [28] lifecycle_1.0.4 iterators_1.0.14 pkgconfig_2.0.3
## [31] Matrix_1.7-0 R6_2.5.1 fastmap_1.1.1
## [34] GenomeInfoDbData_1.2.12 clue_0.3-65 digest_0.6.35
## [37] colorspace_2.1-0 AnnotationDbi_1.66.0 DESeq2_1.44.0
## [40] geneplotter_1.82.0 RSQLite_2.3.6 hwriter_1.3.2.1
## [43] labeling_0.4.3 filelock_1.0.3 fansi_1.0.6
## [46] httr_1.4.7 abind_1.4-5 compiler_4.4.0
## [49] withr_3.0.0 bit64_4.0.5 doParallel_1.0.17
## [52] BiocParallel_1.38.0 DBI_1.2.2 highr_0.10
## [55] biomaRt_2.60.0 rappdirs_0.3.3 DelayedArray_0.30.0
## [58] rjson_0.2.21 tools_4.4.0 glue_1.7.0
## [61] restfulr_0.0.15 grid_4.4.0 cluster_2.1.6
## [64] generics_0.1.3 gtable_0.3.5 ensembldb_2.28.0
## [67] hms_1.1.3 xml2_1.3.6 utf8_1.2.4
## [70] XVector_0.44.0 ggrepel_0.9.5 foreach_1.5.2
## [73] pillar_1.9.0 stringr_1.5.1 limma_3.60.0
## [76] genefilter_1.86.0 circlize_0.4.16 splines_4.4.0
## [79] dplyr_1.1.4 BiocFileCache_2.12.0 lattice_0.22-6
## [82] survival_3.6-4 rtracklayer_1.64.0 bit_4.0.5
## [85] annotate_1.82.0 tidyselect_1.2.1 ComplexHeatmap_2.20.0
## [88] locfit_1.5-9.9 Biostrings_2.72.0 knitr_1.46
## [91] bookdown_0.39 ProtGenerics_1.36.0 edgeR_4.2.0
## [94] xfun_0.43 DEXSeq_1.50.0 statmod_1.5.0
## [97] stringi_1.8.3 UCSC.utils_1.0.0 lazyeval_0.2.2
## [100] yaml_2.3.8 evaluate_0.23 codetools_0.2-20
## [103] tibble_3.2.1 BiocManager_1.30.22 cli_3.6.2
## [106] xtable_1.8-4 munsell_0.5.1 jquerylib_0.1.4
## [109] Rsubread_2.18.0 Rcpp_1.0.12 dbplyr_2.5.0
## [112] png_0.1-8 XML_3.99-0.16.1 parallel_4.4.0
## [115] blob_1.2.4 prettyunits_1.2.0 AnnotationFilter_1.28.0
## [118] bitops_1.0-7 viridisLite_0.4.2 scales_1.3.0
## [121] crayon_1.5.2 GetoptLong_1.0.5 rlang_1.1.3
## [124] KEGGREST_1.44.0