1 Overview

Droplet-based scRNA-seq protocols capture cells in droplets for massively multiplexed library prepation (Klein et al. 2015; Macosko et al. 2015). This greatly increases the throughput of scRNA-seq studies, allowing tens of thousands of individual cells to be profiled in a routine experiment. However, it (again) involves some differences from the previous workflows to reflect some unique aspects of droplet-based data.

Here, we describe a brief analysis of the peripheral blood mononuclear cell (PBMC) dataset from 10X Genomics (Zheng et al. 2017). The data are publicly available from the 10X Genomics website, from which we download the raw gene/barcode count matrices, i.e., before cell calling from the CellRanger pipeline.

library(BiocFileCache)
bfc <- BiocFileCache("raw_data", ask = FALSE)
raw.path <- bfcrpath(bfc, file.path("http://cf.10xgenomics.com/samples",
    "cell-exp/2.1.0/pbmc4k/pbmc4k_raw_gene_bc_matrices.tar.gz"))
untar(raw.path, exdir=file.path(tempdir(), "pbmc4k"))

2 Setting up the data

library(DropletUtils)
fname <- file.path(tempdir(), "pbmc4k/raw_gene_bc_matrices/GRCh38")
sce <- read10xCounts(fname, col.names=TRUE)
sce

## class: SingleCellExperiment 
## dim: 33694 737280 
## metadata(0):
## assays(1): counts
## rownames(33694): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
##   ENSG00000268674
## rowData names(2): ID Symbol
## colnames(737280): AAACCTGAGAAACCAT-1 AAACCTGAGAAACCGC-1 ...
##   TTTGTCATCTTTAGTC-1 TTTGTCATCTTTCCTC-1
## colData names(2): Sample Barcode
## reducedDimNames(0):
## spikeNames(0):

class(counts(sce))

## [1] "dgCMatrix"
## attr(,"package")
## [1] "Matrix"

library(scater)
rownames(sce) <- uniquifyFeatureNames(rowData(sce)$ID, rowData(sce)$Symbol)
head(rownames(sce))

## [1] "RP11-34P13.3"  "FAM138A"       "OR4F5"         "RP11-34P13.7" 
## [5] "RP11-34P13.8"  "RP11-34P13.14"

library(EnsDb.Hsapiens.v86)
location <- mapIds(EnsDb.Hsapiens.v86, keys=rowData(sce)$ID, 
    column="SEQNAME", keytype="GENEID")
rowData(sce)$CHR <- location
summary(location=="MT")

##    Mode   FALSE    TRUE    NA's 
## logical   33537      13     144

3 Calling cells from empty droplets

3.1 Testing for deviations from ambient expression

An interesting aspect of droplet-based data is that we have no prior knowledge about which droplets (i.e., cell barcodes) actually contain cells, and which are empty. Thus, we need to call cells from empty droplets based on the observed expression profiles. This is not entirely straightforward as empty droplets can contain ambient (i.e., extracellular) RNA that can be captured and sequenced. The distribution of total counts exhibits a sharp transition between barcodes with large and small total counts (Figure 1), probably corresponding to cell-containing and empty droplets respectively.

bcrank <- barcodeRanks(counts(sce))

# Only showing unique points for plotting speed.
uniq <- !duplicated(bcrank$rank)
plot(bcrank$rank[uniq], bcrank$total[uniq], log="xy",
    xlab="Rank", ylab="Total UMI count", cex.lab=1.2)

abline(h=metadata(bcrank)$inflection, col="darkgreen", lty=2)
abline(h=metadata(bcrank)$knee, col="dodgerblue", lty=2)

legend("bottomleft", legend=c("Inflection", "Knee"), 
    col=c("darkgreen", "dodgerblue"), lty=2, cex=1.2)

Figure 1: Total UMI count for each barcode in the PBMC dataset, plotted against its rank (in decreasing order of total counts). The inferred locations of the inflection and knee points are also shown.

We use the emptyDrops() function to test whether the expression profile for each cell barcode is significantly different from the ambient RNA pool (Lun et al. 2018). Any significant deviation indicates that the barcode corresponds to a cell-containing droplet. We call cells at a false discovery rate (FDR) of 0.1%, meaning that no more than 0.1% of our called barcodes should be empty droplets on average.

set.seed(100)
e.out <- emptyDrops(counts(sce))
sum(e.out$FDR <= 0.001, na.rm=TRUE)

## [1] 4237

We then subset our SingleCellExperiment object to retain only the detected cells.

# using which() to automatically remove NAs.
sce <- sce[,which(e.out$FDR <= 0.001)]

Comments from Aaron:

• emptyDrops() computes Monte Carlo \(p\)-values based on a Dirichlet-multinomial model of sampling molecules into droplets. These \(p\)-values are stochastic so it is important to set the random seed to obtain reproducible results.
• The stability of the Monte Carlo \(p\)-values depends on the number of iterations used to compute them. This is controlled using the niters= argument in emptyDrops(), set to a default of 10000 for speed. Larger values improve stability at the cost of time only, so users should set niters= to the largest value they are willing to tolerate.
• The function assumes that cell barcodes with total UMI counts below a certain threshold (lower=100 by default) correspond to empty droplets, and uses them to estimate the ambient expression profile. By definition, these barcodes cannot be cell-containing droplets and are excluded from the hypothesis testing, hence the NAs in the output.
• Users wanting to use the cell calling algorithm from the CellRanger pipeline can call defaultDrops() instead. This tends to be quite conservative as it often discards genuine cells with low RNA content (and thus low total counts). It also requires an estimate of the expected number of cells in the experiment.

3.2 Examining cell-calling diagnostics

The number of Monte Carlo iterations (specified by the niters argument in emptyDrops()) determines the lower bound for the _p_values (Phipson and Smyth 2010). The Limited field in the output indicates whether or not the computed p-value for a particular barcode is bounded by the number of iterations. If any non-significant barcodes are TRUE for Limited, we may need to increase the number of iterations. A larger number of iterations will often result in a lower p-value for these barcodes, which may allow them to be detected after correcting for multiple testing.

table(Sig=e.out$FDR <= 0.01, Limited=e.out$Limited)

##        Limited
## Sig     FALSE TRUE
##   FALSE   929    0
##   TRUE   1722 2638

As mentioned above, emptyDrops() assumes that barcodes with low total UMI counts are empty droplets. Thus, the null hypothesis should be true for all of these barcodes. We can check whether the hypothesis test holds its size by examining the distribution of \(p\)-values for low-total barcodes. Ideally, the distribution should be close to uniform.

full.data <- read10xCounts(fname, col.names=TRUE)
set.seed(100)
limit <- 100   
all.out <- emptyDrops(counts(full.data), lower=limit, test.ambient=TRUE)
hist(all.out$PValue[all.out$Total <= limit & all.out$Total > 0],
    xlab="P-value", main="", col="grey80") 

Figure 2: Distribution of \(p\)-values for the assumed empty droplets.

Large peaks near zero indicate that barcodes with total counts below lower are not all ambient in origin. This can be resolved by decreasing lower further to exclude barcodes corresponding to droplets with very small cells.

4 Quality control on the cells

The previous step only distinguishes cells from empty droplets, but makes no statement about the quality of the cells. It is entirely possible for droplets to contain damaged or dying cells, which need to be removed prior to downstream analysis. We compute some QC metrics using calculateQCMetrics() (McCarthy et al. 2017) and examine their distributions in Figure 3.

sce <- calculateQCMetrics(sce, feature_controls=list(Mito=which(location=="MT")))
par(mfrow=c(1,3))
hist(sce$log10_total_counts, breaks=20, col="grey80",
    xlab="Log-total UMI count")
hist(sce$log10_total_features_by_counts, breaks=20, col="grey80",
    xlab="Log-total number of expressed features")
hist(sce$pct_counts_Mito, breaks=20, col="grey80",
    xlab="Proportion of reads in mitochondrial genes")

Figure 3: Histograms of QC metric distributions in the PBMC dataset.

Ideally, we would remove cells with low library sizes or total number of expressed features as described previously. However, this would likely remove cell types with low RNA content, especially in a heterogeneous PBMC population with many different cell types. Thus, we use a more relaxed strategy and only remove cells with large mitochondrial proportions, using it as a proxy for cell damage. (Keep in mind that droplet-based datasets usually do not have spike-in RNA.)

high.mito <- isOutlier(sce$pct_counts_Mito, nmads=3, type="higher")
sce <- sce[,!high.mito]
summary(high.mito)

##    Mode   FALSE    TRUE 
## logical    3925     312

Comments from Aaron:

• The above justification for using a more relaxed filter is largely retrospective. In practice, we may not know a priori the degree of population heterogeneity and whether it manifests in the QC metrics. We recommend performing an initial analysis with some QC, and then relaxing the filter (or making it more stringent) based on further diagnostics. See here for an example of a potential diagnostic approach.
• Removal of cells with high mitochondrial content assumes that the damage to the cell membrane is modest enough that the mitochondria are retained. It is possible for the cells to be so damaged that all cytoplasmic content is lost and only the stripped nucleus remains for sequencing. This manifests as mitochondrial proportions of zero, usually accompanied by low library sizes/numbers of expressed genes and possibly low ribosomal protein gene expression. If a cluster of such cells is observed in the data, they can be removed by using isOutlier() on the mitochondrial proportions with type="lower" and log=TRUE (to improve resolution around zero).

5 Examining gene expression

The average expression of each gene is much lower here compared to the previous datasets (Figure 4). This is due to the reduced coverage per cell when thousands of cells are multiplexed together for sequencing.

ave <- calcAverage(sce)
rowData(sce)$AveCount <- ave
hist(log10(ave), col="grey80")

Figure 4: Histogram of the log₁₀-average counts for each gene in the PBMC dataset.

The set of most highly expressed genes is dominated by ribosomal protein and mitochondrial genes (Figure 5), as expected.

plotHighestExprs(sce)

Figure 5: Percentage of total counts assigned to the top 50 most highly-abundant features in the PBMC dataset. For each feature, each bar represents the percentage assigned to that feature for a single cell, while the circle represents the average across all cells. Bars are coloured by the total number of expressed features in each cell.

6 Normalizing for cell-specific biases

We perform some pre-clustering as described previously. Recall that we need to set the seed when using BSPARAM=IrlbaParam() due to the randomness of irlba.

library(scran)
library(BiocSingular)
set.seed(1000)
clusters <- quickCluster(sce, use.ranks=FALSE, BSPARAM=IrlbaParam())
table(clusters)

## clusters
##   1   2   3   4   5   6   7   8   9  10  11  12 
## 188 161 391 272 383 944 347 242 347 331 152 167

We apply the deconvolution method to compute size factors for all cells (Lun, Bach, and Marioni 2016). Again, we use min.mean=0.1 to account for the fact that UMI counts are lower. The specification of cluster= also ensures that we do not pool cells that are very different.

sce <- computeSumFactors(sce, min.mean=0.1, cluster=clusters)
summary(sizeFactors(sce))

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000511  0.707189  0.865822  1.000000  1.087737 13.869128

The size factors are well correlated against the library sizes (Figure 6), indicating that capture efficiency and sequencing depth are the major biases.

plot(sce$total_counts, sizeFactors(sce), log="xy")

Figure 6: Size factors for all cells in the PBMC dataset, plotted against the library size.

Finally, we compute normalized log-expression values. There is no need to call computeSpikeFactors() here, as there are no spike-in transcripts available.

sce <- normalize(sce)

Comments from Aaron:

• Larger droplet-based datasets will often be generated in separate batches or runs. In such cases, we can set block= in quickCluster() to cluster cells within each batch or run. This reduces computational work considerably without compromising performance, provided that there are enough cells within each batch.
• Even in the absence of any known batch structure, we can improve speed by setting an arbitrary factor, e.g., using block=cut(seq_len(ncol(sce)), 10) to split the cells into ten “batches” of roughly equal size. Recall that we are not interpreting the clusters themselves, so it is not a problem to have multiple redundant cluster labels. Again, this assumes that each cluster is large enough to support deconvolution.
• On a similar note, both quickCluster() and computeSumFactors() can process blocks or clusters in parallel. This is achieved using the BiocParallel framework, which provides support for a range of parallelization strategies.

7 Modelling the mean-variance trend

The lack of spike-in transcripts complicates the modelling of the technical noise. One option is to assume that most genes do not exhibit strong biological variation, and to fit a trend to the variances of endogenous genes (see here for details). However, this assumption is generally unreasonable for a heterogeneous population. Instead, we assume that the technical noise is Poisson and create a fitted trend on that basis using the makeTechTrend() function.

new.trend <- makeTechTrend(x=sce)

We estimate the variances for all genes and compare the trend fits in Figure 7. The Poisson-based trend serves as a lower bound for the variances of the endogenous genes. This results in non-zero biological components for most genes, which is consistent with other UMI-based data sets (see the corresponding analysis of the Zeisel et al. (2015) data set).

fit <- trendVar(sce, use.spikes=FALSE, loess.args=list(span=0.05))
plot(fit$mean, fit$var, pch=16)
curve(fit$trend(x), col="dodgerblue", add=TRUE)
curve(new.trend(x), col="red", add=TRUE)

Figure 7: Variance of normalized log-expression values for each gene in the PBMC dataset, plotted against the mean log-expression. The blue line represents the mean-dependent trend fitted to the variances, while the red line represents the Poisson noise.

We decompose the variance for each gene using the Poisson-based trend, and examine the genes with the highest biological components.

fit$trend <- new.trend # overwrite trend.
dec <- decomposeVar(fit=fit) # use per-gene variance estimates in 'fit'.
top.dec <- dec[order(dec$bio, decreasing=TRUE),] 
head(top.dec)

## DataFrame with 6 rows and 6 columns
##                     mean            total              bio              tech
##                <numeric>        <numeric>        <numeric>         <numeric>
## LYZ     1.98875005638816 5.14341260170461 4.51176985000412 0.631642751700493
## S100A9  1.96174422905567 4.65172391412982  4.0159132208848 0.635810693245017
## S100A8  1.73152962115248  4.5347219053919  3.8698340831234 0.664887822268501
## HLA-DRA 2.10323878785893 3.73688775561837 3.12425058096729 0.612637174651078
## CD74    2.90706397948024 3.31874474040374 2.86026077094717 0.458483969456575
## CST3    1.50277896589381 3.00840701247919 2.32991598910274  0.67849102337645
##           p.value       FDR
##         <numeric> <numeric>
## LYZ             0         0
## S100A9          0         0
## S100A8          0         0
## HLA-DRA         0         0
## CD74            0         0
## CST3            0         0

We can plot the genes with the largest biological components, to verify that they are indeed highly variable (Figure 8).

plotExpression(sce, features=rownames(top.dec)[1:10])

Figure 8: Distributions of normalized log-expression values for the top 10 genes with the largest biological components in the PBMC dataset. Each point represents the log-expression value in a single cell.

Comments from Aaron:

• The Poisson-based trend from makeTechTrend() tends to yield large biological components for highly-expressed genes for which Poisson noise is low (in the log-expression space). This often includes so-called “house-keeping” genes coding for essential cellular components such as ribosomal proteins. These genes are often considered uninteresting for characterizing cellular heterogeneity, though this is debatable as they are often differentially expressed in a variety of conditions (Glare et al. 2002; Nazari, Parham, and Maleki 2015; Guimaraes and Zavolan 2016). Indeed, the fact that they have large biological components indicates that there is strong variation in their expression across cells, which warrants some further investigation. Nonetheless, if they are deemed to be uninteresting, their impact can be reduced by fitting the mean-variance trend to the endogenous genes.

8 Dimensionality reduction

We use the denoisePCA() function with the assumed Poisson technical trend to choose the number of dimensions to retain after PCA. Recall that this involves a random initialization when BSPARAM=IrlbaParam(), which motivates the call to set.seed() to obtain reproducible results.

set.seed(1000)
sce <- denoisePCA(sce, technical=new.trend, BSPARAM=IrlbaParam())
ncol(reducedDim(sce, "PCA"))

## [1] 16

plot(attr(reducedDim(sce), "percentVar"), xlab="PC",
    ylab="Proportion of variance explained")
abline(v=ncol(reducedDim(sce, "PCA")), lty=2, col="red")

Figure 9: Variance explained by each principal component in the PBMC dataset. The red line represents the chosen number of PCs.

Examination of the first few PCs already reveals some strong substructure in the data (Figure 10).

plotPCA(sce, ncomponents=3, colour_by="log10_total_features_by_counts")

Figure 10: Pairwise PCA plots of the first three PCs in the PBMC dataset, constructed from normalized log-expression values of genes with positive biological components. Each point represents a cell, coloured by the log-number of expressed features.

This is recapitulated with a t-SNE plot (Figure 11). Again, note that we set use_dimred= to perform t-SNE on the denoised expression matrix.

set.seed(1000)
sce <- runTSNE(sce, use_dimred="PCA", perplexity=30)
plotTSNE(sce, colour_by="log10_total_features_by_counts")

Figure 11: t-SNE plots constructed from the denoised PCs of the PBMC dataset. Each point represents a cell and is coloured according to the log-number of expressed features.

9 Clustering with graph-based methods

We build a shared nearest neighbour graph (Xu and Su 2015) and use the Walktrap algorithm to identify clusters.

snn.gr <- buildSNNGraph(sce, use.dimred="PCA")
clusters <- igraph::cluster_walktrap(snn.gr)
sce$Cluster <- factor(clusters$membership)
table(sce$Cluster)

## 
##   1   2   3   4   5   6   7   8   9  10  11  12  13 
## 818 536 124 553 515 173 836  41  45 149  81  17  37

We look at the ratio of the observed and expected edge weights to confirm that the clusters are modular. (We don’t look at the modularity score itself, as that varies by orders of magnitudes across clusters and is difficult to interpret.) Figure 12 indicates that most of the clusters are well seperated, with few strong off-diagonal entries.

cluster.mod <- clusterModularity(snn.gr, sce$Cluster, get.values=TRUE)
log.ratio <- log2(cluster.mod$observed/cluster.mod$expected + 1)

library(pheatmap)
pheatmap(log.ratio, cluster_rows=FALSE, cluster_cols=FALSE, 
    color=colorRampPalette(c("white", "blue"))(100))

Figure 12: Heatmap of the log₁₀-ratio of the total weight between nodes in the same cluster or in different clusters, relative to the total weight expected under a null model of random links.

We examine the cluster identities on a t-SNE plot (Figure 13) to confirm that different clusters are indeed separated.

plotTSNE(sce, colour_by="Cluster")

Figure 13: t-SNE plots constructed from the denoised PCs of the PBMC dataset. Each point represents a cell and is coloured according to its cluster identity.

10 Marker gene detection

We detect marker genes for each cluster using findMarkers(). Again, we only look at upregulated genes in each cluster, as these are more useful for positive identification of cell types in a heterogeneous population.

markers <- findMarkers(sce, clusters=sce$Cluster, direction="up")

We examine the markers for cluster 9 in more detail. The upregulation of genes such as PF4 and PPBP suggests that this cluster contains platelets or their precursors.

marker.set <- markers[["9"]]
head(marker.set[,1:8], 10) # only first 8 columns, for brevity

## DataFrame with 10 rows and 8 columns
##              Top              p.value                  FDR          logFC.1
##        <integer>            <numeric>            <numeric>        <numeric>
## PF4            1 3.40689700480522e-32 1.14791987679907e-27 7.66630703836341
## TAGLN2         1 6.47484186766419e-24 7.27211072963593e-20 5.64734054340417
## TMSB4X         2 4.24317841881877e-30 7.14848268218396e-26  3.5479835313661
## SDPR           2 1.91721010484285e-22 1.61496193181438e-18 6.29292565811117
## NRGN           3 7.06171472326395e-21 3.39910594122364e-17 5.61079339116364
## ACTB           4 6.04817751092793e-21 3.39910594122364e-17 3.54952764038114
## B2M            4 6.39837805023113e-21 3.39910594122364e-17 2.59554547928819
## GPX1           4 1.80370694415441e-19 6.75267797514879e-16 3.62078799193754
## PPBP           5 7.35916305435611e-20 3.09949549941844e-16 7.14683150493454
## CCL5           7 3.31577325558259e-18 9.31013867279998e-15 6.08860014839329
##                 logFC.2          logFC.3          logFC.4          logFC.5
##               <numeric>        <numeric>        <numeric>        <numeric>
## PF4    7.72240822840049  7.7060871361676 7.72722555160816 7.72853946460267
## TAGLN2 5.38577335836165 4.90872302060875 5.62509032303173 5.31989255604341
## TMSB4X  3.9528741990056 3.61045775580167 3.97201059861384 4.54626695104843
## SDPR   6.34204370413446 6.32085253878529 6.35078759414508 6.34422138799743
## NRGN   5.84742786465627 5.59314832903448 5.84931609367271 5.84852115240837
## ACTB   4.37285917900389 3.26329512458978 4.17056364378725 4.72082927537447
## B2M    2.10307119852859 3.26219276368274 1.91615428748404 2.79416179348316
## GPX1   5.88278944108498 4.12620545864619 6.04814489379613  5.6105291399479
## PPBP    7.2393768164112 7.24410907609996 7.24593724855077 7.23959814614347
## CCL5   5.46437940268433 6.07470706998285 2.54480219458938  6.0551232843156

This is confirmed in Figure 14, where the transcriptional profile of cluster 9 is clearly distinct from the others.

chosen <- rownames(marker.set)[marker.set$Top <= 10]
plotHeatmap(sce, features=chosen, exprs_values="logcounts", 
    zlim=5, center=TRUE, symmetric=TRUE, cluster_cols=FALSE,
    colour_columns_by="Cluster", columns=order(sce$Cluster),
    show_colnames=FALSE)

Figure 14: Heatmap of mean-centred and normalized log-expression values for the top set of markers for cluster 9 in the PBMC dataset. Column colours represent the cluster to which each cell is assigned, as indicated by the legend.

11 Concluding remarks

Having completed the basic analysis, we save the SingleCellExperiment object with its associated data to file. This avoids having to repeat all of the pre-processing steps described above prior to further analyses.

saveRDS(sce, file="pbmc_data.rds")

All software packages used in this workflow are publicly available from the Comprehensive R Archive Network (https://cran.r-project.org) or the Bioconductor project (http://bioconductor.org). The specific version numbers of the packages used are shown below, along with the version of the R installation.

sessionInfo()

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] pheatmap_1.0.12             BiocSingular_1.0.0         
##  [3] scran_1.12.0                EnsDb.Hsapiens.v86_2.99.0  
##  [5] ensembldb_2.8.0             AnnotationFilter_1.8.0     
##  [7] GenomicFeatures_1.36.0      AnnotationDbi_1.46.0       
##  [9] scater_1.12.0               ggplot2_3.1.1              
## [11] DropletUtils_1.4.0          SingleCellExperiment_1.6.0 
## [13] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
## [15] BiocParallel_1.18.0         matrixStats_0.54.0         
## [17] Biobase_2.44.0              GenomicRanges_1.36.0       
## [19] GenomeInfoDb_1.20.0         IRanges_2.18.0             
## [21] S4Vectors_0.22.0            BiocGenerics_0.30.0        
## [23] BiocFileCache_1.8.0         dbplyr_1.4.0               
## [25] knitr_1.22                  BiocStyle_2.12.0           
## 
## loaded via a namespace (and not attached):
##  [1] Rtsne_0.15               ggbeeswarm_0.6.0        
##  [3] colorspace_1.4-1         dynamicTreeCut_1.63-1   
##  [5] XVector_0.24.0           BiocNeighbors_1.2.0     
##  [7] bit64_0.9-7              codetools_0.2-16        
##  [9] R.methodsS3_1.7.1        Rsamtools_2.0.0         
## [11] R.oo_1.22.0              HDF5Array_1.12.0        
## [13] BiocManager_1.30.4       compiler_3.6.0          
## [15] httr_1.4.0               dqrng_0.2.0             
## [17] assertthat_0.2.1         Matrix_1.2-17           
## [19] lazyeval_0.2.2           limma_3.40.0            
## [21] htmltools_0.3.6          prettyunits_1.0.2       
## [23] tools_3.6.0              rsvd_1.0.0              
## [25] igraph_1.2.4.1           gtable_0.3.0            
## [27] glue_1.3.1               GenomeInfoDbData_1.2.1  
## [29] reshape2_1.4.3           dplyr_0.8.0.1           
## [31] rappdirs_0.3.1           Rcpp_1.0.1              
## [33] Biostrings_2.52.0        rtracklayer_1.44.0      
## [35] DelayedMatrixStats_1.6.0 xfun_0.6                
## [37] stringr_1.4.0            ps_1.3.0                
## [39] irlba_2.3.3              statmod_1.4.30          
## [41] XML_3.98-1.19            edgeR_3.26.0            
## [43] zlibbioc_1.30.0          scales_1.0.0            
## [45] hms_0.4.2                ProtGenerics_1.16.0     
## [47] rhdf5_2.28.0             RColorBrewer_1.1-2      
## [49] yaml_2.2.0               curl_3.3                
## [51] memoise_1.1.0            gridExtra_2.3           
## [53] biomaRt_2.40.0           stringi_1.4.3           
## [55] RSQLite_2.1.1            highr_0.8               
## [57] simpleSingleCell_1.8.0   rlang_0.3.4             
## [59] pkgconfig_2.0.2          bitops_1.0-6            
## [61] evaluate_0.13            lattice_0.20-38         
## [63] purrr_0.3.2              Rhdf5lib_1.6.0          
## [65] GenomicAlignments_1.20.0 labeling_0.3            
## [67] cowplot_0.9.4            bit_1.1-14              
## [69] tidyselect_0.2.5         processx_3.3.0          
## [71] plyr_1.8.4               magrittr_1.5            
## [73] bookdown_0.9             R6_2.4.0                
## [75] DBI_1.0.0                pillar_1.3.1            
## [77] withr_2.1.2              RCurl_1.95-4.12         
## [79] tibble_2.1.1             crayon_1.3.4            
## [81] rmarkdown_1.12           viridis_0.5.1           
## [83] progress_1.2.0           locfit_1.5-9.1          
## [85] grid_3.6.0               blob_1.1.1              
## [87] callr_3.2.0              digest_0.6.18           
## [89] R.utils_2.8.0            munsell_0.5.0           
## [91] beeswarm_0.2.3           viridisLite_0.3.0       
## [93] vipor_0.4.5