## Overview

### Description

In this workshop, we will cover a newly developed suite of tools for performing analysis of allelic expression in Bioconductor. You will learn about how we use upstream tools like g2gtools and Salmon to quantify allelic expression for RNA-seq, and then how to explore the data in R using the fishpond package. Altogether we call this suite of tools SEESAW for “Statistical Estimation Of allelic Expression Using Salmon And sWish”.

### Background and other methods

The SEESAW set of methods for quantifying and analyzing allelic data is similar to mmseq, in which uncertainty of assignment of reads to isoforms and alleles is estimated, stored, and using in statistical testing. It is also similar to EMASE, which provides quantification at isoform and allele level; SEESAW also use the same upstream pipeline as EMASE for construction of diploid reference transcripts.

Our approach differs from existing methods for detecting allelic imbalance (AI) in RNA-seq data such as WASP or the Bioconductor package AllelicImbalance, in that we do not focus on pileup of reads on particular SNPs, but instead integrate the isoform- and allele-level information contained in reads aligning to entire transcripts (including both SNP and indel variation). However, these existing methods can be used across patients with different genomes, while the SEESAW methods are designed for analyzing replicate samples with the same diploid genomes (e.g. F1 crosses, or time series of human cell lines).

A recent method for isoform- and allele-level testing is the Bioconductor package PAIRADISE, which quantifies reads that contain both allelic and isoform-level information: those that contain both a heterozygous SNP and a splice juntion. Another relevant recently developed method is ASEP, which allows for gene-level allelic imbalance (AI) detection, accounting for unknown genotype of other nearby regulatory variants. PAIRADISE and ASEP are therefore applicable across populations of genotypically diverse individuals. We link to these publications for background reading below.

### Pre-requisites

• Some basic background of RNA biology, e.g. what are genes, what are isoforms/transcripts?
• Some familiarity with RNA-seq data, e.g. what are reads?
• Some familiarity with working with SummarizedExperiment objects will help.

### Participation

The format is lecture + lab, with time for data exploration.

### R / Bioconductor packages used

• SummarizedExperiment
• fishpond
• plyranges
• pheatmap
• Gviz

### Time outline

An example for a 45-minute workshop:

Activity Time
Intro to allelic quant 15m
Exploration of data in R 15m
Testing and visualization 15m

### Learning goals

• understand why analyzing allelic expression can be useful for learning about cis genetic regulation
• understand differences between approaches to allelic quantification
• understand difference among types of allelic imbalance (AI) tests, e.g. “global”, “differential”, and “dynamic” imbalance

### Learning objectives

• import allelic quantification data into R
• exploratory analysis of allelic data with uncertainty
• statistical testing for allelic imbalance across samples
• exploration of testing results with heatmaps and genome browser plots

## What is allelic expression

Let us assume for this workshop that we are studying a diploid organism, and we focus on the genes with two copies (so genes on the autosome, and e.g. chrX for XX individuals). We do not consider here allelic quantification in cases of complex copy number such as tumor gene expression.

In most RNA-seq pipelines, we are concerned with total expression of genes or transcripts, where we do not distinguish between RNA transcripts from either of the two chromosomes. Suppose the light blue region highlighted above is a gene, and there are two alleles (B and b) which differ by one or more genetic variants. In the case that there are genetic variants falling within the exons of the gene, some RNA-seq reads may contain information that allows us to determine the origin of the RNA transcript, whether from the B chromosome or the b chromosome. Typically, RNA-seq analysis pipelines just are concerned with counting reads aligning to the light blue region, regardless of which chromosome. Some pipelines will attempt to identify which splice isoforms these align to, but do not attempt to distinguish between alleles.

In SEESAW (and mmseq and EMASE) we attempt to distinguish both allele and isoform from the RNA-seq reads. Again, note that, while there is always expression from two alleles, only in the middle two cases above, with one or genetic variants falling in the exons of the highlighted gene, can we recover allelic information from RNA-seq reads. In this workshop, we will therefore look at two counts per sample, one count for the a2 or reference allele, and the other count for the a1 or alternate allele. In our case, the reference will be the C57BL/6J (or “B6”) mouse genome, and the alternate allele will be the CAST/EiJ mouse genome (we will focus on the B6xCAST F1 hybrid mouse). The mouse pre-osteoblast like cells went through differentiation induced with an osteoblast differentiation cocktail and RNA was collected every two days from day 2 to day 18 post differentiation (nine time points).

## Allelic imbalance

The expression of the two alleles may differ for a variety of reasons. Imagine a single cell with two copies of a gene that may be transcribed. You may be familiar with the concept of “transcriptional bursting”:

Mammalian gene expression is inherently stochastic, and results in discrete bursts of RNA molecules that are synthesized from each allele.

-Larsson et al. “Genomic encoding of transcriptional burst kinetics.” Nature 565, 251–254 (2019) https://doi.org/10.1038/s41586-018-0836-1. (Other papers describing the stochastic nature of expression from each allele are the SCALE and scBASE method papers.)

We might expect that these stochastic differences between the two alleles would average out if we look at many cells, as in bulk RNA-seq. But in some cases, we may see systematic differences between the two alleles, which we call allelic imbalance (AI). Some reasons why:

1. Interesting biological differences
2. Technical artifacts

As in Figure 1 of Castel et al. (2015), the largest differences seen in allelic imbalance are likely mapping bias or genotyping errors (e.g. #2 above). If we map reads to the reference genome, then we will generate a systematic bias where reads from the alternate allele will tend to have lower counts. Likewise, if one of the two genomes has lower quality, even if we map reads to both genomes or transcriptomes (as in SEESAW) we will see a systematic bias towards the higher quality genome. Here we assume both genomes are equally free of errors, but assessing systematic bias is an important part of allelic analysis (e.g. histograms or MA plots of the difference between the mapping rate of the two alleles across all genes).

However, of the type #1 above, there are some possibilities for the source of allelic differences in RNA abundance:

• Epigenetic (e.g. the common example of chrX inactivation)
• Nonsense-mediated decay (NMD)
• Cis-genetic regulation (cGR)

In the first case, one allele is genomically imprinted, which may involve DNA methylation and histone modifications depending on the species. In the second and third case, RNA is often produced from both alleles, but one is degraded at a higher rate (NMD) or produced at a higher rate (cGR).

Our motivation in developing SEESAW was particular on detecting cGR, as we are interested in how non-coding variants cis to a gene may affect transcription. These could affect transcription through alteration of transcription factor binding sites (TFBS) or through changes to splicing. While both TFBS and splicing changes are of interest, in this workshop we will focus on non-coding variation that affects TFBS and therefore modulates transcriptional activity at the promoter.

We therefore will focus on cases where there is allelic imbalance of a transcript, and we can detect this imbalance with RNA-seq (center two cases in the above diagram), and we hypothesize that this imbalance is the result of a heterozygous variant in some regulatory region of the gene. As we only focus here on RNA-seq, we will not have additional data that allows us to locate the source of the regulatory change across the two alleles. For this, it would be useful to have complementary ATAC-seq or ChIP-seq (if a particular TF is of interest).

## SEESAW pipeline

At the beginning of the workshop, we will give an overview of the steps that need to take place outside of R, before we import the allelic data into Bioconductor. These are outlined as the first parts of the pipeline in the diagram below. These are also described in detail in the allelic vignette for the fishpond package.

The input to the pipeline of methods is RNA-seq data (FASTQ files), information about the diploid reference genome (e.g. VCF file), and information about the isoforms (e.g. GTF file). The SEESAW suite of methods is currently designed to work for experiments with multiple replicates of samples with the same diploid genome, e.g. F1 crosses, or time series of human cell lines.

The R code below will begin at the center blue square with importAllelicCounts(), assuming that diploid quantification with bootstrap replicates for uncertainty has already been performed.

## Importing allelic counts

The following five un-evaluated code chunks were used to import the allelic quantification from Salmon into R. These are un-evaluated, as the original data is too large to include in this package.

First, we define coldata and txps, which describe the sample metadata, and the genomic ranges of the transcripts that were quantified (the haploid reference transcripts). We do not yet have a convenient way to describe the location of the transcripts in the diploid genome, so we still rely on a single reference genome here, although a diploid genome was used for quantifying allelic expression.

library(AnnotationHub)
library(ensembldb)
# files points to quant.sf files in original Salmon directories
# other vectors here describe the samples
coldata <- data.frame(cross, day, files, names)
ah <- AnnotationHub()
#query(ah, c("EnsDb","102","Mus musculus"))
txps <- transcripts(edb)

This is how a sample coldata looks like:

cross day files names
1 129xB6 2 /path/to/quant.sf 129xB6-d02
2 129xB6 4 /path/to/quant.sf 129xB6-d04
3 CASTxB6 2 /path/to/quant.sf CASTxB6-d02
4 CASTxB6 4 /path/to/quant.sf CASTxB6-d04

We can group the transcripts to the gene level with the following group_id:

library(plyranges)
txps <- txps %>%
select(tx_id, group_id=gene_id)

The following code chunk imports the allelic data, creating a “wide” SummarizedExperiment object which will have 2x the number of columns as the samples listed in coldata. We specify the strings that distinguish the a1 and a2 allele in the transcript FASTA file (these are suffices that follow the transcript name, separated with an underscore, e.g. ENST123_ref and ENST123_alt. By convention, the a2 allele describes the reference allele. In the gse object (gene-level SummarizedExperiment), the a2 allelic counts will appear as the first columns followed by the a1 allelic counts. Here we supply txps as a GRanges object with a metadata column group_id that describes how we collapse transcript-level counts.

library(fishpond)
gse <- importAllelicCounts(
coldata, a1="alt", a2="ref",
format="wide", tx2gene=txps,
)

Finally, we performed minimal filtering on the features:

# filtering out lowly expressed features:
keep <- rowSums(assay(gse) >= 10) >= 6
gse <- gse[keep,]

Alternatively, we can group transcript-level counts to the transcription start site (TSS) level using the following code chunk to define txps. makeTx2Tss() is a convenience function in fishpond. The maxgap=50 argument means we will group together transcripts that have TSS that fall within 50bp of each other.

txps <- makeTx2Tss(edb, maxgap=50) %>%
select(tx_id, gene_id, group_id, tss)

Q: why would we want to collapse to TSS level?

## Mouse osteoblast differentiation

We have two pre-packaged datasets as part of this workshop package, one where allelic counts from the osteoblast RNA-seq experiment are summarized to the gene level, and another where they are summarized to the TSS level.

The datasets are described in the man pages (see References tab). Briefly, mouse osteoblast cells were differentiated over a time course from day 2 to day 18. The data has been subset to just the genes on chr1. A reference for the experiment is:

Kemp JP, Medina-Gomez C, Estrada K, St Pourcain B et al. Phenotypic dissection of bone mineral density reveals skeletal site specificity and facilitates the identification of novel loci in the genetic regulation of bone mass attainment. PLoS Genet 2014 Jun;10(6):e1004423. PMID: 24945404 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063697/

The RNA-seq reads were processed via the quantification part of SEESAW as described above. Only data from chromosome 1 is included in the pre-packaged datasets for simplicity of the workshop.

Q: why would we be interested in allelic expression during osteoblast differentiation?

## Explore allelic counts

We first load the gene-level dataset created with importAllelicCounts():

library(SummarizedExperiment)
library(Bioc2022AllelicExpression)
data(osteoblast_gene_chr1)
osteoblast_gene_chr1
#> class: RangedSummarizedExperiment
#> dim: 1211 36
#> assays(33): counts abundance ... infRep29 infRep30
#> rownames(1211): ENSMUSG00000001138 ENSMUSG00000001143 ...
#>   ENSMUSG00000118219 ENSMUSG00000118607
#> rowData names(9): gene_id gene_name ... symbol entrezid
#> colnames(36): 129xB6-d02-a2 129xB6-d04-a2 ... CASTxB6-d16-a1
#>   CASTxB6-d18-a1
#> colData names(3): allele cross day
g <- osteoblast_gene_chr1

colData(g)
#> DataFrame with 36 rows and 3 columns
#>                  allele    cross       day
#>                <factor> <factor> <numeric>
#> 129xB6-d02-a2        a2   129xB6         2
#> 129xB6-d04-a2        a2   129xB6         4
#> 129xB6-d06-a2        a2   129xB6         6
#> 129xB6-d08-a2        a2   129xB6         8
#> 129xB6-d10-a2        a2   129xB6        10
#> ...                 ...      ...       ...
#> CASTxB6-d10-a1       a1  CASTxB6        10
#> CASTxB6-d12-a1       a1  CASTxB6        12
#> CASTxB6-d14-a1       a1  CASTxB6        14
#> CASTxB6-d16-a1       a1  CASTxB6        16
#> CASTxB6-d18-a1       a1  CASTxB6        18

For each sample and each allele, we have counts and also bootstrap replicates. The bootstrap replicates are from Salmon, by specifying --numBootstraps 30 during quantification.

Q: Why do we need bootstrap replicates for allelic analysis (or isoform-level analysis)?

We can use fishpond functions to do some basic exploration of the allelic counts.

The counts assay provides the read counts for features x samples, as Salmon is used for quantification these are estimated counts that are non-negative but fractional, not integer. The abundance assay gives estimates in Transcripts Per Million (TPM). The length assay gives the average effective transcript length for a given transcript or a given gene. Finally the infRep’s are the bootstrap samples for the counts.

library(fishpond)
assayNames(g)
#>  [1] "counts"    "abundance" "length"    "infRep1"   "infRep2"   "infRep3"
#>  [7] "infRep4"   "infRep5"   "infRep6"   "infRep7"   "infRep8"   "infRep9"
#> [13] "infRep10"  "infRep11"  "infRep12"  "infRep13"  "infRep14"  "infRep15"
#> [19] "infRep16"  "infRep17"  "infRep18"  "infRep19"  "infRep20"  "infRep21"
#> [25] "infRep22"  "infRep23"  "infRep24"  "infRep25"  "infRep26"  "infRep27"
#> [31] "infRep28"  "infRep29"  "infRep30"

getTrace obtains a trace of the inferential replicates of counts for one feature and one sample (multiple features/samples can also be returned). Plotting the trace in the histogram helps us to visualize the distribution of the estimated counts for the day 2 sample of the CASTxB6 cross, reference allele (a2). We can see for this particular sample, the counts have roughly the same mean and variance, indicating that there is not much uncertainty (minimal bootstrap variance will roughly follow what is expected for a Poisson random variable).

dat <- getTrace(g, idx=1, samp_idx="CASTxB6-d02-a2")
hist(dat$count, border="white", col="grey50", xlab="estimated count, a2") c( mu=mean(dat$count), var=var(dat$count) ) #> mu var #> 94.31868 80.09537 We can also visualize the bootstrap distributions for all samples for a given gene using plotInfReps(). Here we have a boxplot of inferential replicates for gene ENSMUSG00000004110, grouped by allele. plotInfReps(g, idx=1, x="allele", legend=TRUE) We can also group the allelic counts ordered by sample, e.g.: gcast <- g[,g$cross == "CASTxB6"]
plotInfReps(gcast, idx=1, x="allele", cov="day", legend=TRUE)

In the above plot, the samples are ordered by sample, as if day x allele were a categorical variable. We can also plot day on its original continuous scale. To do so, we use points and lines to represent the bootstrap distribution instead of boxplots. To draw the line, we need to first compute the bootstrap variance, which can be computed with the computeInfRV() function (InfRV = “inferential relative variance”, a useful diagnostic for comparing uncertainty across features):

gcast <- computeInfRV(gcast)
plotInfReps(gcast, idx=1, x="day", cov="allele",
shiftX=.2, legend=TRUE)

## Filter non-informative features

Some features have no sequence difference between the two alleles. One easy way to detect these is to look at a single bootstrap replicate, e.g. the first one infRep1. If this bootstrap sample gives the same count to each allele, across all samples, this implies either that the sequence was identical across alleles, or there were no reads covering the part of the transcript sequence that contains a difference across alleles. We remove these from the dataset, as they are non-informative for allelic imbalance analysis.

n <- ncol(gcast)/2
rep1a1 <- assay(gcast, "infRep1")[, gcast$allele == "a1" ] rep1a2 <- assay(gcast, "infRep1")[, gcast$allele == "a2" ]
someInfo <- rowSums(abs(rep1a1 - rep1a2) < 1) < n
table(someInfo)
#> someInfo
#> FALSE  TRUE
#>    50  1161
gcast <- gcast[ someInfo, ]

We can examine a rough estimate of the log fold change across samples to see if there is a systematic bias. Here, the samples with higher sequencing depth will have greater influence on the log fold change. This is better estimated by the swish() function by specifying the pairs of counts per sample. For large count genes, the log fold change appears centered on LFC=0.

a1 <- rowSums(assay(gcast, "counts")[, gcast$allele == "a1"]) a2 <- rowSums(assay(gcast, "counts")[, gcast$allele == "a2"])
rough_lfc <- log2(a1+1) - log2(a2+1)
rough_total <- a1 + a2 + 2
rough_ratio <- (a1 + 1) / rough_total
plot(log10(rough_total), rough_lfc); abline(h=0,col="red")

hist(rough_ratio[rough_total > 1e3], breaks=100,
col="grey50", border="white")
abline(v=0.5, col="red")

Q: what might we see if one of the parental genomes/genotypes was poorly annotated?

## Testing for allelic imbalance

Here we switch to using TSS-level allelic counts, which may give different AI signal than gene-level allelic counts. Both can be analyzed using SEESAW, but we have some additional plots that are particularly useful for isoform- or TSS-level allelic analysis.

Q: how could TSS-level AI signal differ from gene-level?

Here, we have grouped transcripts together by TSS using +/- 50bp tolerance (so forming fewer groups by allowing +/- 50bp between TSS to join them into a group).

data(osteoblast_tss_chr1)
tss <- osteoblast_tss_chr1
tcast <- tss[,tss$cross == "CASTxB6"] We first filter out non-informative features as shown in the previous step: n <- ncol(tcast)/2 rep1a1 <- assay(tcast, "infRep1")[, tcast$allele == "a1" ]
rep1a2 <- assay(tcast, "infRep1")[, tcast$allele == "a2" ] someInfo <- rowSums(abs(rep1a1 - rep1a2) < 1) < n table(someInfo) #> someInfo #> FALSE TRUE #> 166 2104 tcast <- tcast[ someInfo, ] Running swish generally involves scaling the inferential replicates, filtering out rows with insufficient counts, and calculating the statistics (log fold changes, test statistics, p-values and q-values). Here, we skip scaling the counts (to account for sequencing depth) since we are comparing the counts of the two alleles within samples. By default, labelKeep will label features with a minimal count of 10 in at least 3 samples. Here we are performing two types of allelic imbalance testing. Global AI tests for consistent allelic imbalance across all samples. Dynamic AI tests for non-zero correlation between the log allelic fold change and a continuous variable (in this case, the day). tcast <- labelKeep(tcast) tcast <- tcast[mcols(tcast)$keep,]
# for plots later
tcast <- computeInfRV(tcast)

The tests involve permutation, and so it takes about 12 seconds in this case (on a single core Linux machine).

set.seed(1)
global <- swish(tcast, x="allele", pair="day")

The dynamic test here takes about 1.5 seconds. The reason it is faster than the analysis above is that, for technical reasons, in this type of Swish test we do not have to recompute ranks, which is the major bottleneck.

set.seed(1)
dy <- swish(tcast, x="allele", pair="day",
cov="day", cor="pearson")

Q: why do we specify day twice in the dynamic analysis?

The results are stored in mcols() in each case. We can examine how many TSS groups are detected as having global or dynamic AI in a 5% FDR set:

table(mcols(global)$qvalue < 0.05) #> #> FALSE TRUE #> 1548 485 table(mcols(dy)$qvalue < 0.05)
#>
#> FALSE  TRUE
#>  2016    17

Q: why are there fewer dynamic positive results?

## Plotting test results

We can visualize the relationship between allelic log fold change versus mean expression through MA plots. Blue points are TSS groups in the 5% FDR set. One can specify the threshold by using alpha = ... option.

plotMASwish(global, alpha=.05, main="Global MA Plot")

The average LFC over all samples is plotted also for the dynamic test, although the correlation is the test statistic used for computing p-values and q-values. Hence the most extreme LFC are not in the 5% FDR set.

plotMASwish(dy, alpha=.05, main="Dynamic MA Plot")

## Global AI visualization

We now want to visualize the TSS group analysis in a genomic context. To do so, first we must download the mouse Ensembl annotations. We do so using AnnotationHub:

library(AnnotationHub)
library(ensembldb)
ah <- AnnotationHub()
#> snapshotDate(): 2022-07-22
edb <- ah[["AH89211"]]
#> loading from cache

The plotAllelicGene() function in fishpond can be used to visualize Swish test results ($$-log_{10}(q-value)$$ and log2FC), allelic proportions and isoform proportions (calculated from TPM) within the context of the gene model. One can also indicate which gene to plot using gene symbol, e.g. symbol="Soat1". Either TxDb or EnsDb can be use to include the gene model at the top of the plot.

plotAllelicGene(global, gene="ENSMUSG00000026600",
db=edb, labels=list(a2="B6",a1="CAST"))

Q: which isoform has higher abundance?

We can verify the plotAllelicGene() figure with plotInfReps(). We can see B6 has a higher count than CAST in majority of the samples. One can customize the label of alleles in the function with the following code, switching from a2, a1 to B6, CAST.

tcast$allele_new <- tcast$allele
levels(tcast$allele_new) <- c("B6", "CAST") idx <- match("ENSMUSG00000026600", mcols(tcast)$gene_id)
plotInfReps(tcast, idx=idx, x="allele_new", cov="day",
legend=TRUE, legendPos="topright")

We can also use a heatmap to visualize the difference in expression across alleles for the TSS groups, using plotAllelicHeatmap(), which itself calls pheatmap(). Darker green in the annotation bar represents a more significant qvalue in differential testing (for allelic imbalance). A color toward red indicates more of the a1 allele (CAST) while a color toward blue indicates more of a2 (B6).

idx <- mcols(global)$gene_id == "ENSMUSG00000026600" dat <- data.frame(minusLogQ=-log10(mcols(global)$qvalue[idx]),
row.names=rownames(global)[idx])
colnames(global) <- c(paste0("Day ",seq(2,18, by= 2), "-a2"),
paste0("Day ",seq(2,18, by= 2), "-a1"))
plotAllelicHeatmap(global, idx=idx, annotation_row=dat)
#> using posterior mean for calculating ratio

## Dynamic AI visualization

plotInfReps() is a useful function for visualizing how allelic counts changes over a continuous covariate, such as day. Using lines can help us better visualize the trend.

plotCurves <- function(dy, idx) {
cols <- c("dodgerblue","goldenrod4")
plotInfReps(dy, idx=idx, x="day", cov="allele", shiftX=0.01)
lines(dy$day[1:9], assay(dy, "mean")[idx,1:9], col=cols[1]) lines(dy$day[10:18], assay(dy, "mean")[idx,10:18], col=cols[2])
legend("topleft", legend=c("B6", "CAST"), pch=15, col=cols, inset=.05)
}
idx <- "ENSMUSG00000026185-1"
plotCurves(dy, idx)

idx <- "ENSMUSG00000026185-2"
plotCurves(dy, idx)

Q: What do you notice with the two infRV plots? HINT: Is the allelic imbalance in the same direction?

plotAllelicGene() can also be used to visualize time-series allelic imbalance data. We first need to group the timepoints (or else the plot would have no room as there are 9 distinct time points). In this example, we are grouping Day 2-6, Day 8-12, and Day 14-18 together. How best to group the timepoints depends on either the biological system of a strategy to demonstrate maximal differences in the ratio per group (e.g. by looking at plotInfReps() plot).

The plot shows allelic proportions with grouped timepoints, as well as isoform proportion. We can see an increasing in B6 expression over time for the first transcript group and an increasing in CAST expression with time for the second transcript group.

# this makes integer bins
dy$time_bins <- cut(dy$day,breaks=c(2,6,12,18),
include.lowest=TRUE, labels=FALSE)
# come up with useful time group labels
time_labels <- c("Day02-06","Day08-12","Day14-18")
# use new time group labels
dy$time_bins <- time_labels[ dy$time_bins ]
gene <- "ENSMUSG00000026185"
plotAllelicGene(dy, gene=gene, db=edb, cov="time_bins",
qvalue=FALSE, log2FC=FALSE,
tpmFilter=.1, isoPropFilter=.01,
labels=list(a2="B6",a1="CAST"))

The same gene, but looking at infReps:

idx <- which(mcols(dy)$gene_id == gene) par(mfrow=c(2,1), mar=c(3,4.5,1.5,1)) plotCurves(dy, idx[1]) plotCurves(dy, idx[2]) Q: What type of plot might help to visualize time-series allelic data across multiple transcripts and multiple samples? We can add a time bar on the top of a heatmap to indicate time by defining col_dat. idx <- which(mcols(dy)$gene_id == "ENSMUSG00000026185")
dat <- data.frame(minusLogQ=-log10(mcols(dy)$pvalue[idx]), row.names=rownames(dy)[idx]) colnames(dy) <- c(paste0("Day ",seq(2,18, by= 2), "-a2"), paste0("Day ",seq(2,18, by= 2), "-a1")) row_dat <- data.frame(minusLogQ=-log10(mcols(dy)$qvalue[idx]),
row.names=rownames(dy)[idx])
col_dat <- data.frame(time=dy\$day[1:9],
row.names=paste0("Day ",seq(2,18, by= 2)))
plotAllelicHeatmap(dy, idx=idx,
annotation_row=row_dat,
annotation_col=col_dat,
cluster_rows=FALSE)
#> using posterior mean for calculating ratio

## Going further

This was a brief exploration of visualization and statistical inference of allelic expression data using Bioconductor packages. Some points for further analysis:

• Lots of plots - In our development of SEESAW, we found that allelic analysis within gene benefits greatly from examining many types of plots. There are many layers of information (isoform, allele, time points, etc.) along with measures of significance or uncertainty, and no single plot captures all the information. We recommend looking at many plots for genes of interest, to determine if the correct signals are being captured / if any signal is present by not captured by the design.
• Looking at aligned reads - We additionally recommend to make use of IGV or other genome browsers to examine distributions of reads along transcripts. This requires an additional mapping step (mapping reads with either a SNP-aware aligner, or using WASP to remove reads susceptible to allelic mapping bias. In the mouse osteoblast dataset, we used WASP to generate the merged BAM files (Step 6 of the mapping pipeline) containing mappability-filtered aligned reads, and then often would visualize these aligned reads using IGV, for genes of interest. This alignment pipeline is then separate from the Salmon quantification pipeline described above, and more for quality control and examination of reads, haplotypes, and gene models.
• Causes of allelic imbalance - here we focused on detecting imbalance in the abundance of isoforms/genes across alleles. Systematic differences in the expression level of the two alleles could have a variety of upstream causes, as described at the beginning of this document. One type of upstream cause is genetic variation residing in cis-regulatory elements (CRE). One additional dataset that can be produced and examined alongside allelic RNA-seq is allelic ATAC-seq or ChIP-seq. Observing commensurate changes in the accessibility or binding at CRE local to the promoter of the gene (and perhaps linked by chromatin interaction data) would be further evidence for a regulatory relationship between CRE and transcription.

More details can be found at the allelic vignette of the fishpond package:

https://mikelove.github.io/fishpond/articles/allelic.html

We expect to also post a preprint describing the methods a few weeks following BioC2022 conference.

Acknowledgements:

• Osteoblast project:
• Cheryl Ackert-Bicknell, Gary Churchill, Matthew Vincent, KB Choi
• SEESAW methods development:
• Rob Patro, Noor Pratap Singh, Mohsen Zakeri
• Financial support:
• NHGRI R01-HG009937
• NCI Cancer Genomics Training Grant

## Session info

sessionInfo()
#> R version 4.2.0 (2022-04-22)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#>
#> Matrix products: default
#>
#> 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
#> [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] ensembldb_2.21.2                AnnotationFilter_1.21.0
#>  [3] GenomicFeatures_1.49.5          AnnotationDbi_1.59.1
#>  [5] AnnotationHub_3.5.0             BiocFileCache_2.5.0
#>  [7] dbplyr_2.2.1                    fishpond_2.3.6
#>  [9] Bioc2022AllelicExpression_0.0.3 SummarizedExperiment_1.27.1
#> [11] Biobase_2.57.1                  GenomicRanges_1.49.0
#> [13] GenomeInfoDb_1.33.3             IRanges_2.31.0
#> [15] S4Vectors_0.35.1                BiocGenerics_0.43.1
#> [17] MatrixGenerics_1.9.1            matrixStats_0.62.0
#>
#> loaded via a namespace (and not attached):
#>   [1] backports_1.4.1               Hmisc_4.7-0
#>   [3] systemfonts_1.0.4             plyr_1.8.7
#>   [5] lazyeval_0.2.2                splines_4.2.0
#>   [7] BiocParallel_1.31.10          ggplot2_3.3.6
#>   [9] digest_0.6.29                 htmltools_0.5.3
#>  [11] fansi_1.0.3                   checkmate_2.1.0
#>  [13] magrittr_2.0.3                memoise_2.0.1
#>  [15] svMisc_1.2.3                  BSgenome_1.65.2
#>  [17] cluster_2.1.3                 Biostrings_2.65.1
#>  [19] pkgdown_2.0.6                 prettyunits_1.1.1
#>  [21] jpeg_0.1-9                    colorspace_2.0-3
#>  [23] blob_1.2.3                    rappdirs_0.3.3
#>  [25] textshaping_0.3.6             xfun_0.31
#>  [27] dplyr_1.0.9                   crayon_1.5.1
#>  [29] RCurl_1.98-1.7                jsonlite_1.8.0
#>  [31] VariantAnnotation_1.43.2      survival_3.3-1
#>  [33] glue_1.6.2                    gtable_0.3.0
#>  [35] zlibbioc_1.43.0               XVector_0.37.0
#>  [37] DelayedArray_0.23.0           SingleCellExperiment_1.19.0
#>  [39] abind_1.4-5                   scales_1.2.0
#>  [41] pheatmap_1.0.12               DBI_1.1.3
#>  [43] Rcpp_1.0.9                    htmlTable_2.4.1
#>  [45] xtable_1.8-4                  progress_1.2.2
#>  [47] foreign_0.8-82                bit_4.0.4
#>  [49] Formula_1.2-4                 htmlwidgets_1.5.4
#>  [51] httr_1.4.3                    RColorBrewer_1.1-3
#>  [53] ellipsis_0.3.2                farver_2.1.1
#>  [55] pkgconfig_2.0.3               XML_3.99-0.10
#>  [57] nnet_7.3-17                   Gviz_1.41.1
#>  [59] sass_0.4.2                    deldir_1.0-6
#>  [61] utf8_1.2.2                    tidyselect_1.1.2
#>  [63] rlang_1.0.4                   reshape2_1.4.4
#>  [65] later_1.3.0                   munsell_0.5.0
#>  [67] BiocVersion_3.16.0            tools_4.2.0
#>  [69] cachem_1.0.6                  cli_3.3.0
#>  [71] generics_0.1.3                RSQLite_2.2.15
#>  [73] evaluate_0.15                 stringr_1.4.0
#>  [75] fastmap_1.1.0                 yaml_2.3.5
#>  [77] ragg_1.2.2                    knitr_1.39
#>  [79] bit64_4.0.5                   fs_1.5.2
#>  [81] purrr_0.3.4                   KEGGREST_1.37.3
#>  [83] mime_0.12                     xml2_1.3.3
#>  [85] biomaRt_2.53.2                rstudioapi_0.13
#>  [87] compiler_4.2.0                filelock_1.0.2
#>  [89] curl_4.3.2                    png_0.1-7
#>  [91] interactiveDisplayBase_1.35.0 tibble_3.1.8
#>  [93] bslib_0.4.0                   stringi_1.7.8
#>  [95] highr_0.9                     desc_1.4.1
#>  [97] lattice_0.20-45               ProtGenerics_1.29.0
#>  [99] Matrix_1.4-1                  vctrs_0.4.1
#> [101] pillar_1.8.0                  lifecycle_1.0.1
#> [103] BiocManager_1.30.18           jquerylib_0.1.4
#> [105] data.table_1.14.2             bitops_1.0-7
#> [107] httpuv_1.6.5                  rtracklayer_1.57.0
#> [109] qvalue_2.29.0                 R6_2.5.1
#> [111] BiocIO_1.7.1                  latticeExtra_0.6-30
#> [113] promises_1.2.0.1              gridExtra_2.3
#> [115] codetools_0.2-18              dichromat_2.0-0.1
#> [117] gtools_3.9.3                  assertthat_0.2.1
#> [119] rprojroot_2.0.3               rjson_0.2.21
#> [121] GenomicAlignments_1.33.1      Rsamtools_2.13.3
#> [123] GenomeInfoDbData_1.2.8        parallel_4.2.0
#> [125] hms_1.1.1                     rpart_4.1.16
#> [127] grid_4.2.0                    rmarkdown_2.14
#> [129] biovizBase_1.45.0             shiny_1.7.2
#> [131] base64enc_0.1-3               interp_1.1-3
#> [133] restfulr_0.0.15