Data integration and clustering of cardiomyocytes: Fetal, ND, DCM
Belinda Phipson & Neda R. Mehdiabadi
10/02/2021-27/11/2021
Last updated: 2022-04-07
Checks: 7 0
Knit directory:
Fetal-Gene-Program-snRNAseq/
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Load libraries and functions
library(edgeR)Loading required package: limmalibrary(RColorBrewer)
library(org.Hs.eg.db)Loading required package: AnnotationDbiLoading required package: stats4Loading required package: BiocGenerics
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intersect, setdiff, setequal, unionsource("code/normCounts.R")
source("code/findModes.R")
source("code/ggplotColors.R")targets <- read.delim("data/targets.txt",header=TRUE, stringsAsFactors = FALSE)
targets$FileName2 <- paste(targets$FileName,"/",sep="")
targets$Group_ID2 <- gsub("LV_","",targets$Group_ID)
group <- c("fetal_1","fetal_2","fetal_3",
"non-diseased_1","non-diseased_2","non-diseased_3",
"diseased_1","diseased_2",
"diseased_3","diseased_4")
m <- match(group, targets$Group_ID2)
targets <- targets[m,]fetal.integrated <- readRDS(file="/group/card2/Neda/MCRI_LAB/single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/output/RDataObjects/fetal-int.Rds")
load(file="/group/card2/Neda/MCRI_LAB/single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/output/RDataObjects/fetalObjs.Rdata")
##note: nd.integrated is also an integrated form of young heart samples. young.integrated has already been published in Sim et al., 2021
nd.integrated <- readRDS(file="/group/card2/Neda/MCRI_LAB/single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/output/RDataObjects/nd-int.Rds")
load(file="/group/card2/Neda/MCRI_LAB/single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/output/RDataObjects/ndObjs.Rdata")
dcm.integrated <- readRDS(file="/group/card2/Neda/MCRI_LAB/single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/output/RDataObjects/dcm-int.Rds")
load(file="/group/card2/Neda/MCRI_LAB/single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/output/RDataObjects/dcmObjs.Rdata")Set default clustering resolution
# Default 0.3
Idents(fetal.integrated) <- fetal.integrated$integrated_snn_res.0.3
DimPlot(fetal.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()
# Default 0.3
Idents(nd.integrated) <- nd.integrated$integrated_snn_res.0.3
DimPlot(nd.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()
# Default 0.3
Idents(dcm.integrated) <- dcm.integrated$integrated_snn_res.0.3
DimPlot(dcm.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()Merge all data together
# This data has already been generated and saved as the heart object.
#heart <- merge(fetal.integrated, y = c(nd.integrated, dcm.integrated), project = "heart")
heart <- readRDS("/group/card2/Neda/MCRI_LAB/must-do-projects/EnzoPorrelloLab/dilated-cardiomyopathy/data/heart-int-FND-filtered.Rds")
table(heart$orig.ident)
Fetal ND DCM
27760 16964 32712 Get cardiomyocytes only
Idents(heart) <- heart$Broad_celltype
cardio <- subset(heart,subset = Broad_celltype == "CM")Check for poor quality cells
Cardiomyocytes are fairly large cells and we wouldn’t expect them to only be expressing very few genes.
DefaultAssay(cardio) <- "RNA"
par(mar=c(4,4,2,1))
plot(density(cardio$nFeature_RNA),main="Number of genes detected")
abline(v=500,col=4, lty=3)
legend("topright",lty=2,col=4,legend="#genes = 500")
plot(density(cardio$nCount_RNA),main="Library size")
abline(v=2500,col=4, lty=3)
legend("topright",lty=2,col=4,legend="library size = 2500")
cardio <- subset(cardio, subset = nFeature_RNA > 500 & nCount_RNA > 2500)Run new integration with SCtransform normalisation
# For the sake of time, I ran the following code once and saved the object as cardio-int-FND.Rds
cardio.list <- SplitObject(cardio, split.by = "biorep")
min <- min(sapply(cardio.list, ncol))
for (i in 1:length(cardio.list)) {
cardio.list[[i]] <- SCTransform(cardio.list[[i]], verbose = FALSE)
}
cardio.anchors <- FindIntegrationAnchors(object.list = cardio.list, dims=1:30,anchor.features = 3000,k.filter=min)
cardio.integrated <- IntegrateData(anchorset = cardio.anchors,dims=1:30)
DefaultAssay(object = cardio.integrated) <- "integrated"
cardio.integrated <- ScaleData(cardio.integrated, verbose = FALSE)
cardio.integrated <- RunPCA(cardio.integrated, npcs = 50, verbose = FALSE)
ElbowPlot(cardio.integrated,ndims=50)
cardio.integrated <- FindNeighbors(cardio.integrated, dims = 1:20)
cardio.integrated <- FindClusters(cardio.integrated, resolution = 0.1)
table(Idents(cardio.integrated))
saveRDS(cardio.integrated, file = "/group/card2/Neda/MCRI_LAB/must-do-projects/EnzoPorrelloLab/dilated-cardiomyopathy/data/cardio-int-FND.Rds")cardio.integrated <- readRDS("/group/card2/Neda/MCRI_LAB/must-do-projects/EnzoPorrelloLab/dilated-cardiomyopathy/data/cardio-int-FND.Rds")
cardio.integrated$orig.ident <- factor(cardio.integrated$orig.ident,levels = c("Fetal","ND","DCM"))
cardio.integrated$biorep <- factor(cardio.integrated$biorep,levels = c("f1","f2","f3","nd1","nd2","nd3","d1","d2","d3","d4"))
table(cardio.integrated$orig.ident)
Fetal ND DCM
16220 5516 6982 table(cardio.integrated$biorep)
f1 f2 f3 nd1 nd2 nd3 d1 d2 d3 d4
4620 7056 4544 1049 1962 2505 1267 912 3798 1005 VizDimLoadings(cardio.integrated, dims = 1:4, reduction = "pca")
DimPlot(cardio.integrated, reduction = "pca",group.by="orig.ident")
DimPlot(cardio.integrated, reduction = "pca",group.by="biorep")
DimPlot(cardio.integrated, reduction = "pca",group.by="sex")
DimPlot(cardio.integrated, reduction = "pca",group.by="batch")
DimHeatmap(cardio.integrated, dims = 1:15, cells = 500, balanced = TRUE)
DimHeatmap(cardio.integrated, dims = 16:30, cells = 500, balanced = TRUE)
DimHeatmap(cardio.integrated, dims = 31:45, cells = 500, balanced = TRUE)
par(mar=c(5,4,2,2))
barplot(table(Idents(cardio.integrated)),ylab="Number of cells",xlab="Clusters")
title("Number of cells in each cluster")
Visualisation with UMAP
set.seed(10)
cardio.integrated <- RunUMAP(cardio.integrated, reduction = "pca", dims = 1:20)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session11:10:33 UMAP embedding parameters a = 0.9922 b = 1.11211:10:33 Read 28718 rows and found 20 numeric columns11:10:33 Using Annoy for neighbor search, n_neighbors = 3011:10:33 Building Annoy index with metric = cosine, n_trees = 500% 10 20 30 40 50 60 70 80 90 100%[----|----|----|----|----|----|----|----|----|----|**************************************************|
11:10:38 Writing NN index file to temp file /tmp/RtmpRaRHlD/file711060039953
11:10:38 Searching Annoy index using 1 thread, search_k = 3000
11:10:48 Annoy recall = 100%
11:10:49 Commencing smooth kNN distance calibration using 1 thread
11:10:52 Initializing from normalized Laplacian + noise
11:10:53 Commencing optimization for 200 epochs, with 1289270 positive edges
11:11:31 Optimization finishedDimPlot(cardio.integrated, reduction = "umap",label=TRUE,label.size = 6,pt.size = 0.5, split.by = "orig.ident")+NoLegend()
DimPlot(cardio.integrated, reduction = "umap", group.by = "biorep")
DimPlot(cardio.integrated, reduction = "umap", group.by = "sex")
DimPlot(cardio.integrated, reduction = "umap", group.by = "batch")
par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(cardio.integrated),cardio.integrated$biorep)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(10),legend=TRUE)
par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(cardio.integrated),cardio.integrated$orig.ident)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(3))
legend("topleft",legend=colnames(tab),fill=ggplotColors(3))
Find DEG per sub cluster
DefaultAssay(cardio.integrated) <- "RNA"
Idents(cardio.integrated) <- cardio.integrated$integrated_snn_res.0.1
# Load unfiltered counts matrix for every sample (object all)
load("/group/card2/Neda/MCRI_LAB/single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/output/RDataObjects/all-counts.Rdata")columns(org.Hs.eg.db) [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
[6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
[11] "GENETYPE" "GO" "GOALL" "IPI" "MAP"
[16] "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM"
[21] "PMID" "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG"
[26] "UNIPROT" ann <- AnnotationDbi:::select(org.Hs.eg.db,keys=rownames(all),columns=c("SYMBOL","ENTREZID","ENSEMBL","GENENAME","CHR"),keytype = "SYMBOL")Warning in .deprecatedColsMessage(): Accessing gene location information via 'CHR','CHRLOC','CHRLOCEND' is
deprecated. Please use a range based accessor like genes(), or select()
with columns values like TXCHROM and TXSTART on a TxDb or OrganismDb
object instead.'select()' returned 1:many mapping between keys and columnsm <- match(rownames(all),ann$SYMBOL)
ann <- ann[m,]
table(ann$SYMBOL==rownames(all))
TRUE
33939 mito <- grep("mitochondrial",ann$GENENAME)
length(mito)[1] 224ribo <- grep("ribosomal",ann$GENENAME)
length(ribo)[1] 197missingEZID <- which(is.na(ann$ENTREZID))
length(missingEZID)[1] 10976# Limma-trend for DE
m <- match(colnames(cardio.integrated),colnames(all))
all.counts <- all[,m]chuck <- unique(c(mito,ribo,missingEZID))
length(chuck)[1] 11318all.counts.keep <- all.counts[-chuck,]
ann.keep <- ann[-chuck,]
table(ann.keep$SYMBOL==rownames(all.counts.keep))
TRUE
22621 numzero.genes <- rowSums(all.counts.keep==0)
table(numzero.genes > (ncol(all.counts.keep)-20))
FALSE TRUE
18038 4583 keep.genes <- numzero.genes < (ncol(all.counts.keep)-20)
table(keep.genes)keep.genes
FALSE TRUE
4636 17985 all.keep <- all.counts.keep[keep.genes,]
dim(all.keep)[1] 17985 28718ann.keep <- ann.keep[keep.genes,]y.cardio <- DGEList(all.keep)
logcounts <- normCounts(y.cardio,log=TRUE,prior.count=0.5)
maxclust <- length(levels(Idents(cardio.integrated)))-1
grp <- paste("c",Idents(cardio.integrated),sep = "")
grp <- factor(grp,levels = paste("c",0:maxclust,sep=""))
design <- model.matrix(~0+grp+cardio.integrated$biorep)
colnames(design)[1:(maxclust+1)] <- levels(grp)
mycont <- matrix(0,ncol=length(levels(grp)),nrow=length(levels(grp)))
colnames(mycont)<-levels(grp)
diag(mycont)<-1
mycont[upper.tri(mycont)]<- -1/(length(levels(factor(grp)))-1)
mycont[lower.tri(mycont)]<- -1/(length(levels(factor(grp)))-1)
# Fill out remaining rows with 0s
zero.rows <- matrix(0,ncol=length(levels(grp)),nrow=(ncol(design)-length(levels(Idents(cardio.integrated)))))
test <- rbind(mycont,zero.rows)
fit <- lmFit(logcounts,design)
fit.cont <- contrasts.fit(fit,contrasts=test)
fit.cont <- eBayes(fit.cont,trend=TRUE,robust=TRUE)
fit.cont$genes <- ann.keep
summary(decideTests(fit.cont)) c0 c1 c2 c3 c4 c5
Down 6573 4641 3978 2255 1542 4588
NotSig 9658 10665 9798 12863 11479 12381
Up 1754 2679 4209 2867 4964 1016treat <- treat(fit.cont,lfc=0.5)
dt <- decideTests(treat)
summary(dt) c0 c1 c2 c3 c4 c5
Down 19 36 3 83 93 16
NotSig 17956 17862 17834 17767 17750 17906
Up 10 87 148 135 142 63par(mfrow=c(3,3))
for(i in 1:ncol(mycont)){
plotMD(treat,coef=i,status = dt[,i],hl.cex=0.5)
abline(h=0,col=colours()[c(226)])
lines(lowess(treat$Amean,treat$coefficients[,i]),lwd=1.5,col=4)
}
Write out marker genes for each cluster
contnames <- colnames(mycont)
for(i in 1:length(contnames)){
topsig <- topTreat(treat,coef=i,n=Inf)
write.csv(topsig,file=paste("./output/CM-Cluster-",contnames[i],".csv",sep=""))
}fdr <- apply(treat$p.value, 2, function(x) p.adjust(x, method="BH"))
output <- data.frame(treat$genes,LogFC=treat$coefficients,AveExp=treat$Amean,tstat=treat$t, pvalue=treat$p.value, fdr=fdr)
write.csv(output,file="./output/CM-MarkerAnalysis.csv")Perform gene set testing on C2 and GO sets
contnames <- colnames(mycont)
load("/group/card2/Neda/MCRI_LAB/single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/output/RDataObjects/human_c2_v5p2.rdata")
load("/group/card2/Neda/MCRI_LAB/single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/output/RDataObjects/human_c5_v5p2.rdata")
c2.id <- ids2indices(Hs.c2,treat$genes$ENTREZID)
c5.id <- ids2indices(Hs.c5,treat$genes$ENTREZID)
reactome.id <-c2.id[grep("REACTOME",names(c2.id))]
c2.c0 <- cameraPR(treat$t[,1],c2.id)
reactome.c0 <- cameraPR(treat$t[,1],reactome.id)
go.c0 <- cameraPR(treat$t[,1],c5.id)
for(i in 1:length(contnames)){
write.csv(cameraPR(treat$t[,i],c2.id),file=paste("./output/CM-GeneSetTests-c2-",contnames[i],".csv",sep=""))
write.csv(cameraPR(treat$t[,i],reactome.id),file=paste("./output/CM-GeneSetTests-reactome-",contnames[i],".csv",sep=""))
write.csv(cameraPR(treat$t[,i],c5.id),file=paste("./CM-GeneSetTests-go-",contnames[i],".csv",sep=""))
}Check quality of clusters
The quality of the clusters look good.
par(mfrow=c(1,1))
numgenes <- colSums(all.keep!=0)
boxplot(numgenes~grp)
Heatmap of marker genes
sam <- factor(cardio.integrated$biorep,levels=c("f1","f2","f3","nd1","nd2","nd3","d1","d2","d3","d4"))
newgrp <- paste(grp,sam,sep=".")
newgrp <- factor(newgrp,levels=paste(rep(levels(grp),each=10),levels(sam),sep="."))
o <-order(newgrp)
clust <- rep(levels(grp),each=10)
samps <- rep(levels(sam),length(levels(grp)))Summarise expression across cells
sumexpr <- matrix(NA,nrow=nrow(logcounts),ncol=length(levels(newgrp)))
rownames(sumexpr) <- rownames(logcounts)
colnames(sumexpr) <- levels(newgrp)
for(i in 1:nrow(sumexpr)){
sumexpr[i,] <- tapply(logcounts[i,],newgrp,mean)
}heatmap if DEGs for each subcluster
sig.genes <- gene.label <- vector("list", length(levels(grp)))
for(i in 1:length(sig.genes)){
top <- topTreat(treat,coef=i,n=Inf)
sig.genes[[i]] <- rownames(top)[top$logFC>0][1:10]
gene.label[[i]] <- paste(rownames(top)[top$logFC>0][1:10],levels(grp)[i],sep="-")
}
csig <- unlist(sig.genes)
genes <- unlist(gene.label)
myColors <- list(Clust=NA,Sample=NA)
myColors$Clust<-sample(ggplotColors(length(levels(grp))),length(levels(grp)))
names(myColors$Clust)<-levels(grp)
myColors$Sample <- sample(ggplotColors(length(levels(sam))),length(levels(sam)))
names(myColors$Sample) <- levels(sam)
#pdf(file="./output/Figures/cardio-heatmap-siggenes-summarised-FND-filtered.pdf",width=20,height=20,onefile = FALSE)
aheatmap(sumexpr[csig,],Rowv = NA,Colv = NA, labRow = genes,
annCol=list(Clust=clust,Sample=samps),
annColors=myColors,
fontsize=10,color="-RdYlBu",
scale="none")
#dev.off()
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /hpc/software/installed/R/4.1.2/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/4.1.2/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] dplyr_1.0.8 clustree_0.4.4
[3] ggraph_2.0.5 ggplot2_3.3.5
[5] NMF_0.23.0 bigmemory_4.5.36
[7] cluster_2.1.2 rngtools_1.5.2
[9] pkgmaker_0.32.2 registry_0.5-1
[11] scran_1.22.1 scuttle_1.4.0
[13] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[15] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[17] DelayedArray_0.20.0 MatrixGenerics_1.6.0
[19] matrixStats_0.61.0 Matrix_1.4-0
[21] cowplot_1.1.1 SeuratObject_4.0.4
[23] Seurat_4.1.0 org.Hs.eg.db_3.14.0
[25] AnnotationDbi_1.56.2 IRanges_2.28.0
[27] S4Vectors_0.32.3 Biobase_2.54.0
[29] BiocGenerics_0.40.0 RColorBrewer_1.1-2
[31] edgeR_3.36.0 limma_3.50.1
[33] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.24
[3] tidyselect_1.1.2 RSQLite_2.2.10
[5] htmlwidgets_1.5.4 grid_4.1.2
[7] BiocParallel_1.28.3 Rtsne_0.15
[9] munsell_0.5.0 ScaledMatrix_1.2.0
[11] codetools_0.2-18 ica_1.0-2
[13] statmod_1.4.36 future_1.24.0
[15] miniUI_0.1.1.1 withr_2.4.3
[17] spatstat.random_2.1-0 colorspace_2.0-3
[19] highr_0.9 knitr_1.37
[21] rstudioapi_0.13 ROCR_1.0-11
[23] tensor_1.5 listenv_0.8.0
[25] labeling_0.4.2 git2r_0.29.0
[27] GenomeInfoDbData_1.2.7 polyclip_1.10-0
[29] farver_2.1.0 bit64_4.0.5
[31] rprojroot_2.0.2 parallelly_1.30.0
[33] vctrs_0.3.8 generics_0.1.2
[35] xfun_0.29 doParallel_1.0.17
[37] R6_2.5.1 graphlayouts_0.8.0
[39] rsvd_1.0.5 locfit_1.5-9.4
[41] bitops_1.0-7 spatstat.utils_2.3-0
[43] cachem_1.0.6 assertthat_0.2.1
[45] promises_1.2.0.1 scales_1.1.1
[47] gtable_0.3.0 beachmat_2.10.0
[49] globals_0.14.0 processx_3.5.2
[51] goftest_1.2-3 tidygraph_1.2.0
[53] rlang_1.0.1 splines_4.1.2
[55] lazyeval_0.2.2 spatstat.geom_2.3-2
[57] yaml_2.3.5 reshape2_1.4.4
[59] abind_1.4-5 httpuv_1.6.5
[61] tools_4.1.2 gridBase_0.4-7
[63] ellipsis_0.3.2 spatstat.core_2.4-0
[65] jquerylib_0.1.4 ggridges_0.5.3
[67] Rcpp_1.0.8 plyr_1.8.6
[69] sparseMatrixStats_1.6.0 zlibbioc_1.40.0
[71] purrr_0.3.4 RCurl_1.98-1.6
[73] ps_1.6.0 rpart_4.1.16
[75] deldir_1.0-6 viridis_0.6.2
[77] pbapply_1.5-0 zoo_1.8-9
[79] ggrepel_0.9.1 fs_1.5.2
[81] magrittr_2.0.2 RSpectra_0.16-0
[83] data.table_1.14.2 scattermore_0.8
[85] lmtest_0.9-39 RANN_2.6.1
[87] whisker_0.4 fitdistrplus_1.1-6
[89] patchwork_1.1.1 mime_0.12
[91] evaluate_0.15 xtable_1.8-4
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[95] tibble_3.1.6 KernSmooth_2.23-20
[97] crayon_1.5.0 htmltools_0.5.2
[99] mgcv_1.8-39 later_1.3.0
[101] tidyr_1.2.0 DBI_1.1.2
[103] tweenr_1.0.2 MASS_7.3-55
[105] cli_3.2.0 parallel_4.1.2
[107] metapod_1.2.0 igraph_1.2.11
[109] bigmemory.sri_0.1.3 pkgconfig_2.0.3
[111] getPass_0.2-2 plotly_4.10.0
[113] spatstat.sparse_2.1-0 foreach_1.5.2
[115] bslib_0.3.1 dqrng_0.3.0
[117] XVector_0.34.0 stringr_1.4.0
[119] callr_3.7.0 digest_0.6.29
[121] sctransform_0.3.3 RcppAnnoy_0.0.19
[123] spatstat.data_2.1-2 Biostrings_2.62.0
[125] rmarkdown_2.12.1 leiden_0.3.9
[127] uwot_0.1.11 DelayedMatrixStats_1.16.0
[129] shiny_1.7.1 lifecycle_1.0.1
[131] nlme_3.1-155 jsonlite_1.8.0
[133] BiocNeighbors_1.12.0 viridisLite_0.4.0
[135] fansi_1.0.2 pillar_1.7.0
[137] lattice_0.20-45 KEGGREST_1.34.0
[139] fastmap_1.1.0 httr_1.4.2
[141] survival_3.3-0 glue_1.6.2
[143] iterators_1.0.14 png_0.1-7
[145] bluster_1.4.0 bit_4.0.4
[147] ggforce_0.3.3 stringi_1.7.6
[149] sass_0.4.0 blob_1.2.2
[151] BiocSingular_1.10.0 memoise_2.0.1
[153] irlba_2.3.5 future.apply_1.8.1