In ETHZ-INS/diffUTR: diffUTR: Streamlining differential exon and 3' UTR usage

library(BiocStyle)

Introduction

Differential exon usage

diffUTR wraps around three methods for different exon usage analysis:

• The r BiocStyle::Biocpkg("DEXSeq") package (see ?DEXSeqWrapper)
• An improved version of r BiocStyle::Biocpkg("limma")'s diffSplice method (see ?diffSpliceWrapper)
• r BiocStyle::Biocpkg("edgeR")'s diffSpliceDGE method (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), r BiocStyle::Biocpkg("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 r BiocStyle::Biocpkg("DEXSeq") is not an option.

knitr::include_graphics(system.file('docs', 'figure1.svg', package = 'diffUTR'))

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).

Getting started

Package installation

Install the package with:

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

We load diffUTR as well as the r BiocStyle::Biocpkg("SummarizedExperiment") package on which it depends:

suppressPackageStartupMessages({
  library(diffUTR)
  library(SummarizedExperiment)
})

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 r BiocStyle::Biocpkg("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)

Workflow for differential exon usage (DEU) analysis

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)

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 r BiocStyle::Biocpkg("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

The output of countFeatures is a RangedSummarizedExperiment object (see r BiocStyle::Biocpkg("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))

In 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.)

Differential analysis

For the purpose of this example, we will use the improved diffSplice method:

rse <- diffSpliceWrapper(rse, design = ~condition)

The ~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))

The gene-wise aggregation has been saved in the object's metadata:

perGene <- metadata(rse)$geneLevel
head(perGene)

The results are described in more detail below.

Workflow for differential 3' UTR usage analysis

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 r BiocStyle::Biocpkg("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 (\link[Herrmann et al. 2020]{https://doi.org/10.1093/nar/gkz918}). 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.

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 )

By 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)
# or only look for changes in UTR bins:
utr <- geneLevelStats(rse, includeTypes="UTR", returnSE=FALSE)
head(utr)
# or only look for changes in bins that _could be_ UTRs:
# geneLevelStats(rse, excludeTypes=c("CDS","non-coding"), returnSE=FALSE)

Exploring the results

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).

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 r BiocStyle::Biocpkg("DEXSeq") and r BiocStyle::Biocpkg("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")
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.

Overlaying with transcripts

The r BiocStyle::Biocpkg("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 r BiocStyle::Biocpkg("ggbio") package)

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sessionInfo()

ETHZ-INS/diffUTR documentation built on March 18, 2023, 8:54 a.m.