The doseR package aims to provide a consistent, efficient, and generalizable analytical framework for assessing sex chromosome dosage compensation using RNA-seq based read-count data. The study of sex chromosome dosage compensation has garnered increasing interest in the last 15 years or so. This growth has been driven in part by the rise of RNA-seq as a method for assaying genome-wide patterns of gene expression, along with the discovery of a surprising diversity of dosage compensation patterns across species. Yet despite the growing interest in the topic of dosage compensation, with many similarities among the analyses performed in various studies, each subsequent study tends to employ its own bespoke scripting to analyze and visualize the data. There has yet to emerge a common computational framework specifically developed to streamline and increase reproducibility among studies of dosage compensation. Our hope is for doseR to help fill that void.
Functions provided in doseR offer support for several analytical steps commonly done in studies of dosage compensation, including:
Many of these elements may be of interest for studies of gene expression not specifically concerning dosage compensation, and the doseR package is not meant to be limited only to studies contrasting sex chromosomes versus autosomes or males versus females. doseR supports any number of comparisons between biological treatments and groupings of loci.
The standard goal of most sex chromosome dosage compensation studies is to assess whether the sex chromosome (i.e. the X or Z chromosome, depending on male or female heterogamety) shows a globally distinct pattern of gene expression between the sexes. On average, autosomal expression is expected to be comparable between the sexes because autosomes are diploid in both sexes. In contrast, differences in gene dose between the sexes due to heterogamety may influence average expression on the X or Z, depending on what compensating mechanisms have evolved. Thus most contemporary studies of sex chromosome dosage compensation proceed by using sex-specific RNA-seq data to compare distributions of normalized gene expression across chromosomes.
We assume users are starting with read count data from an RNA-seq experiment, as is typically generated after employing one of many commonly used alignment pipelines (e.g. RSEM, Salmon, etc). The doseR workflow starts with read count data, and not RPKM (or, comparably, FPKM), because RPKM values are calculated during the analysis after selecting the desired library-size normalization factor. RNA-seq data will typically come from distinct treatments (e.g. male, female) and may have one or more replicates per treatment. Users also provide lengths (in basepairs) for each transcript assayed. Finally, annotations indicating the groupings of loci (e.g. chromosome) are also needed.
Accounting for differences in library size (i.e. depth of sequencing) between samples is an important step in most statistical analyses involving RNA-seq data. doseR provides a variety of methods for defining the library size for each sample. After defining library sizes, read counts are scaled both by library size and transcript length to give RPKM values that can be meaningfully compared between loci and between samples.
One notable concern and potential artifact in dosage compensation analysis is the filtering of “unexpressed” loci. In theory, it is ideal to remove loci prior to analysis since we should be primarily concerned only with actively expressed genes. In practice, however, it is far from clear how best to do this or what criteria should be employed. When “unexpressed” loci are disproportionately represented among chromosomes (or other gene groups), failing to effectively remove them can cause spurious results. At the other extreme, overly aggressive removal of low-abundance transcripts can artificially compress distributions of gene expression, masking true differences in the data. A variety of approaches have been implement in doseR for filtering out loci considered to be “unexpressed”.
One informative way to assess dosage compensation is to consider the distribution of expression levels within sex between groups of genes, typically for genes on the X or Z versus the autosomes. This is done separately in each sex (or other treatments), providing a direct inference of absolute differences in gene expression among chromosomes (or other gene groupings). However, this approach is limited in that it does not easily support a direct comparison between sexes (or other treatments). doseR includes functions for visualization and statistical tests of differences in absolute gene expression.
Examining the distribution of expression ratios (e.g. Male:Female) between treatments across chromosomes (or other gene groupings) is an alternative approach to assess whether global expression biases exist for particular sets of genes, such as might be expected for uncompensated X or Z chromosomes. While this approach can provide nuanced assessment of dosage effects, it does not indicate what absolute changes in gene expression may be occurring that underlie shifts in expression ratio. doseR provides functions for visualizing and statistically testing variation in expression ratios across chromosomes (or other gene groupings), complementing functions for examining absolute differences in expression among groupings within treatment.
To demonstrate a typical workflow using doseR, we will use a subset
of data from a study in Heliconius butterflies. H. melpomene
has 20 autosomes with chromosome 21 being the “Z” sex chromosome.
Like all Lepidoptera, this is a female-heterogametic species, so males are diploid for the Z while females have a single Z and W chromosomes. The relevant data are included in the doseR package, which must be installed.
doseR can be installed from Bioconductor directly using the
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("doseR")
Once installed, you must load the package.
library(doseR) library(SummarizedExperiment) #> Loading required package: MatrixGenerics #> Loading required package: matrixStats #> #> Attaching package: 'MatrixGenerics' #> The following objects are masked from 'package:matrixStats': #> #> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, #> colCounts, colCummaxs, colCummins, colCumprods, colCumsums, #> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, #> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, #> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, #> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, #> colWeightedMeans, colWeightedMedians, colWeightedSds, #> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, #> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, #> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, #> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, #> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, #> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, #> rowWeightedMads, rowWeightedMeans, rowWeightedMedians, #> rowWeightedSds, rowWeightedVars #> Loading required package: GenomicRanges #> Loading required package: stats4 #> Loading required package: BiocGenerics #> Loading required package: parallel #> #> Attaching package: 'BiocGenerics' #> The following objects are masked from 'package:parallel': #> #> clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, #> clusterExport, clusterMap, parApply, parCapply, parLapply, #> parLapplyLB, parRapply, parSapply, parSapplyLB #> The following objects are masked from 'package:stats': #> #> IQR, mad, sd, var, xtabs #> The following objects are masked from 'package:base': #> #> Filter, Find, Map, Position, Reduce, anyDuplicated, append, #> as.data.frame, basename, cbind, colnames, dirname, do.call, #> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, #> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, #> pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table, #> tapply, union, unique, unsplit, which.max, which.min #> Loading required package: S4Vectors #> #> Attaching package: 'S4Vectors' #> The following objects are masked from 'package:base': #> #> I, expand.grid, unname #> Loading required package: IRanges #> Loading required package: GenomeInfoDb #> Loading required package: Biobase #> Welcome to Bioconductor #> #> Vignettes contain introductory material; view with #> 'browseVignettes()'. To cite Bioconductor, see #> 'citation("Biobase")', and for packages 'citation("pkgname")'. #> #> Attaching package: 'Biobase' #> The following object is masked from 'package:MatrixGenerics': #> #> rowMedians #> The following objects are masked from 'package:matrixStats': #> #> anyMissing, rowMedians
The primary data used by doseR are read counts from RNA-seq data, in this case generated from legs of H. melpomene, with three biological replicates each for male and female. Read count data should be formatted as a matrix, with replicates in columns and transcripts in rows. We also need a vector of corresponding transcript lengths. Finally, data indicating gene groupings are needed. Here we have a vector giving chromosomal linkage for each transcript (note that these data are limited to only one representative transcript per gene).
These three data elements are used to populate a new
which is the core data structure for the doseR package. This object
contains and organizes the read counts, RPKM values, segment lengths,
and annotations of gene groupings. However, there are some further data
manipulations required before we can create a new object.
First, we need to create a vector indicating to the replicate structure in the data, in this case three male and three female replicates, as organized in the counts matrix.
reps <- c("Male", "Male", "Male", "Female", "Female", "Female")
Second, we must create a dataframe to hold the annotation of gene groupings. In doing this, we will also convert the numeric annotation vector into a factor, which facilitates proper ordering of gene groups in later plotting. Additionally, we will recode the chromosomal linkage information as being Z-linked or autosomal.
annotxn <- data.frame("Chromosome" = factor(hmel.dat$chromosome, levels = 1:21)) annotxn$ZA <- factor(ifelse(hmel.dat$chromosome == 21, "Z", "A"), levels = c("A", "Z"))
Now we are ready to populate a new
counts <- hmel.dat$readcounts colData <- S4Vectors::DataFrame(Treatment=as.factor(reps), row.names=colnames(hmel.dat$readcounts)) rowData <- S4Vectors::DataFrame(annotation = annotxn, seglens = hmel.dat$trxLength, row.names=rownames(hmel.dat$readcounts) ) se2 <- SummarizedExperiment(assays=list(counts=counts), colData=colData, rowData=rowData)
Next we select a method for calculating the library size (sometimes called scaling factors) so that data are normalized for depth of sequencing. Various methods for calculating a library size are provided, but we will use the most straightforward method of summing total read counts.
SummarizedExperiment::colData(se2)$Libsizes <- getLibsizes3(se2, estimationType = "total")
With library sizes now stored in the
SummExperiment object, we can calculate
RPKM values for each locus in each sample and store them in the RPKM slot.
SummarizedExperiment::assays(se2)$rpkm <- make_RPKM(se2) # se2 now equals se... data(hmel.se) # loads hmel.dat list # MD5 checksum equivalence: #library(digest) digest::digest(se) == digest::digest(se2) #>  FALSE
It is always wise when first working with comparisons between genome-wide expression data sets like this to check that there are not clear biases or other artifacts showing up in the data, and that the normalization seems reasonable. Making MA plots is one important visualization to guard agains such problems.
plotMA.se(se, samplesA ="Male", samplesB = "Female", cex = .2 , pch = 19, col = rgb(0,0,0, .2), xlab = "Log2(Average RPKM)", ylab = "Log2(Male:Female)")
It is generally good practice to remove loci considered to be “unexpressed”. In this case we will apply a filter so that included loci must have a mean RPKM value across samples greather than 0.01. Many other approaches to filtering are provided in doseR, and it is generally wise to explore how filtering choices affect assessments of dosage compensation.
f_se <- simpleFilter(se, mean_cutoff = 0.01, counts = FALSE) #> Filtering removed 4035 (29.63%).
Having generated filtered RPKM values, we can proceed to comparing
distributions of expression among different groupings of genes, as
indicated in the annotation slot of the
SummExperiment object. Biological
replicates are averaged to give a single representative RPKM value per gene.
In this case it is clear that the gene expression is reduced on the Z
chromosome relative to autosomes in both sexes.
plotExpr(f_se, groupings = "annotation.ZA", clusterby_grouping = FALSE, col=c("grey80","red","grey80","red"), notch=TRUE, ylab = "Log2(RPKM)")
Beyond visualizing with boxplots, we will also want statistical
summaries and tests applied to these data, which can be done with
generateStats() function. Here we will want to
SummarizedExperiment object so that tests are applied
using only data from a single treatment.
The function returns a list of three items:
Here we demonstrate this only for male samples, but a full analysis would also require the same be done for the female samples.
se.male <- f_se[, colData(f_se)$Treatment == "Male"] male_ZvA <- generateStats(se.male , groupings = "annotation.ZA", LOG2 = FALSE) #> View output: outlist$kruskal, outlist$summary male_ZvA$summary # distributional summary statistics #> A Z #> Min. 0.000000 0.000000 #> 1st Qu. 1.837499 1.093565 #> Median 8.715750 5.795021 #> Mean 54.737996 37.759261 #> 3rd Qu. 23.573567 16.577152 #> Max. 20065.669794 4351.420842 male_ZvA$kruskal # htest class output from kruskal.test() #> #> Kruskal-Wallis rank sum test #> #> data: tmp #> Kruskal-Wallis chi-squared = 19.006, df = 1, p-value = 1.303e-05 lapply(male_ZvA$data, head) # a record of values used for statistics. #> $A #> HMEL000002 HMEL000003 HMEL000004 HMEL000006 HMEL000007 HMEL000008 #> 10.71571713 0.09353976 41.72790716 21.12032979 0.00000000 4.87423582 #> #> $Z #> HMEL002056 HMEL002096 HMEL002142 HMEL002143 HMEL002145 HMEL002156 #> 4.9311162 0.4912925 27.8965294 6.1170531 1.8668670 99.4424234
It may be of interest to examine other groupings of genes. For instance,
how variable are the distributions of expression across chromosomes?
Again, we’ll limit this only to males for the sake of demonstration.
plotExpr(f_se, groupings = "annotation.Chromosome", col=c(rep("grey80", 20), "red"), notch=TRUE, ylab = "Log2(RPKM)", las = 2, treatment = "Male", clusterby_grouping = TRUE )