Recluster smooth muscle cells

workflowr

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Last updated: 2019-10-31

Checks: 6 0

Knit directory: Porello-heart-snRNAseq/

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Untracked files:
    Untracked:  analysis/07b-DECardioFYA.Rmd
    Untracked:  analysis/PBapproachMarker-cardio.Rmd
    Untracked:  analysis/doublet-detection.Rmd
    Untracked:  broad_props.csv
    Untracked:  code/ReadDataObjects.R
    Untracked:  code/getTransformedProps.R
    Untracked:  data/adult-clust.txt
    Untracked:  data/dcm-clust.txt
    Untracked:  data/fetal-clust.txt
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    Untracked:  data/gstlist-dcm-res03.Rdata
    Untracked:  data/gstlist-dcm.Rdata
    Untracked:  data/gstlist-fetal.Rdata
    Untracked:  data/gstlist-young.Rdata
    Untracked:  data/heart-markers-long.txt
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    Untracked:  data/targets_pools.txt
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    Untracked:  output/AllAdult-clustermarkers-v2.csv
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    Untracked:  output/Alldcm-clustermarkers.csv
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    Untracked:  output/cardio-numcells-clusters.csv
    Untracked:  output/cardio-numcells-clusters.txt
    Untracked:  output/fetal1-clustermarkers.csv
    Untracked:  output/fetal2-clustermarkers.csv
    Untracked:  output/fetal3-clustermarkers.csv
    Untracked:  output/heatmap-top10-adultmarkergenes.pdf
    Untracked:  output/young1-clustermarkers.csv

Unstaged changes:
    Modified:   analysis/01-QualityControl.Rmd
    Modified:   analysis/01a-DEpseudobulk.Rmd
    Modified:   analysis/02-ClusterFetal.Rmd
    Modified:   analysis/02c-ClusterFetal3.Rmd
    Modified:   analysis/03-ClusterYoung.Rmd
    Modified:   analysis/04-ClusterAdult.Rmd
    Modified:   analysis/07a-ClustCardioFYA.Rmd
    Modified:   analysis/index.Rmd

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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	286b88b	Belinda Phipson	2019-10-31	immune, endo, smc reclustering
html	603b51b	Belinda Phipson	2019-10-31	Build site.
Rmd	36f15a6	Belinda Phipson	2019-10-30	add endothelial reclustering, fixed smc clustering
html	878c13c	Belinda Phipson	2019-10-30	Build site.
html	604f664	Belinda Phipson	2019-10-29	Build site.
Rmd	4352989	Belinda Phipson	2019-10-29	add reclustering of smooth muscle cells

---

Load libraries and functions

library(edgeR)
library(RColorBrewer)
library(org.Hs.eg.db)
library(limma)
library(Seurat)
library(monocle)
library(cowplot)
library(DelayedArray)
library(scran)
library(NMF)
library(workflowr)
library(ggplot2)
library(clustree)
library(dplyr)

source("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/code/normCounts.R")
source("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/code/findModes.R")
source("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/code/ggplotColors.R")

targets <- read.delim("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/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",
           "Young_1","Young_2","Young_3",
           "Adult_1","Adult_2","Adult_3", 
           "Diseased_1","Diseased_2",
           "Diseased_3","Diseased_4")
m <- match(group, targets$Group_ID2)
targets <- targets[m,]

fetal.integrated <- readRDS(file="./output/RDataObjects/fetal-int.Rds")
load(file="./output/RDataObjects/fetalObjs.Rdata")

young.integrated <- readRDS(file="./output/RDataObjects/young-int.Rds")
load(file="./output/RDataObjects/youngObjs.Rdata")

adult.integrated <- readRDS(file="./output/RDataObjects/adult-int.Rds")
load(file="./output/RDataObjects/adultObjs.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()

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

# Default 0.3
DimPlot(young.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()

Past versions of unnamed-chunk-3-2.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

# Default 0.6
DimPlot(adult.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()

Past versions of unnamed-chunk-3-3.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

Merge all data together

heart <- merge(fetal.integrated, y = c(young.integrated, adult.integrated), project = "heart")
table(heart$orig.ident)

adult fetal young 
 9416 27760 16964 

DefaultAssay(object = heart) <- "RNA"

Get epicardial cells only

smc <- subset(heart,subset = Broad_celltype == "Smooth muscle cells")
dim(smc)

[1] 17926   430

Filter out crappy cells

Check for cells with very low number of uniquely detected genes.

par(mfrow=c(1,2))
plot(density(smc$nFeature_RNA),main="Number of genes detected")
abline(v=500,col=2)
plot(density(smc$nCount_RNA),main="Library size")
abline(v=2500,col=2)

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

#smc <- subset(smc, subset = nFeature_RNA > 500 & nCount_RNA > 2500)
dim(smc)

[1] 17926   430

table(smc$biorep)

 a1  a2  a3  f1  f2  f3  y1  y2  y3 
 22  49  13  54  20 136  59  28  49 

Run new integration with SCtransform normalisation

There are very few cells for each biological replicate, so I will normalise and integrate the data by group rather than biological replicate.

smc.list <- SplitObject(smc, split.by = "orig.ident")

for (i in 1:length(smc.list)) {
    smc.list[[i]] <- SCTransform(smc.list[[i]], verbose = FALSE)
}

kf <- min(sapply(smc.list, ncol))
smc.anchors <- FindIntegrationAnchors(object.list = smc.list, dims=1:30,anchor.features = 3000,k.filter=kf)
smc.integrated <- IntegrateData(anchorset = smc.anchors,dims=1:30)

Perform clustering

DefaultAssay(object = smc.integrated) <- "integrated"

Perform scaling and PCA

smc.integrated <- ScaleData(smc.integrated, verbose = FALSE)
smc.integrated <- RunPCA(smc.integrated, npcs = 50, verbose = FALSE)
ElbowPlot(smc.integrated,ndims=50)

Past versions of pca-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

VizDimLoadings(smc.integrated, dims = 1:4, reduction = "pca")

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

DimPlot(smc.integrated, reduction = "pca",group.by="orig.ident")

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

DimPlot(smc.integrated, reduction = "pca",group.by="biorep")

Past versions of unnamed-chunk-11-2.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

DimPlot(smc.integrated, reduction = "pca",group.by="sex")

Past versions of unnamed-chunk-11-3.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

DimPlot(smc.integrated, reduction = "pca",group.by="batch")

Past versions of unnamed-chunk-11-4.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

DimHeatmap(smc.integrated, dims = 1:15, cells = 500, balanced = TRUE)

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

#DimHeatmap(smc.integrated, dims = 16:30, cells = 500, balanced = TRUE)
#DimHeatmap(smc.integrated, dims = 31:45, cells = 500, balanced = TRUE)

Perform nearest neighbours clustering

smc.integrated <- FindNeighbors(smc.integrated, dims = 1:10)
smc.integrated <- FindClusters(smc.integrated, resolution = 0.1)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9233
Number of communities: 3
Elapsed time: 0 seconds

table(Idents(smc.integrated))

  0   1   2 
232 174  24 

par(mfrow=c(1,1))
par(mar=c(5,4,2,2))
barplot(table(Idents(smc.integrated)),ylab="Number of cells",xlab="Clusters")
title("Number of cells in each cluster")

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

Visualisation with TSNE

set.seed(10)
smc.integrated <- RunTSNE(smc.integrated, reduction = "pca", dims = 1:10)

DimPlot(smc.integrated, reduction = "tsne",label=TRUE,label.size = 6,pt.size = 0.5)+NoLegend()

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
603b51b	Belinda Phipson	2019-10-31
604f664	Belinda Phipson	2019-10-29

pdf(file="./output/Figures/tsne-smcALL-res01.pdf",width=10,height=8,onefile = FALSE)
DimPlot(smc.integrated, reduction = "tsne",label=TRUE,label.size = 6,pt.size = 0.5)+NoLegend()
dev.off()

png 
  2 

DimPlot(smc.integrated, reduction = "tsne", group.by = "orig.ident")

Past versions of unnamed-chunk-16-1.png

Version	Author	Date
603b51b	Belinda Phipson	2019-10-31
604f664	Belinda Phipson	2019-10-29

DimPlot(smc.integrated, reduction = "tsne", split.by = "orig.ident")

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
603b51b	Belinda Phipson	2019-10-31
604f664	Belinda Phipson	2019-10-29

DimPlot(smc.integrated, reduction = "tsne", group.by = "biorep")

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
603b51b	Belinda Phipson	2019-10-31
604f664	Belinda Phipson	2019-10-29

DimPlot(smc.integrated, reduction = "tsne", group.by = "sex")

Past versions of unnamed-chunk-19-1.png

Version	Author	Date
603b51b	Belinda Phipson	2019-10-31
604f664	Belinda Phipson	2019-10-29

DimPlot(smc.integrated, reduction = "tsne", split.by = "sex")

Past versions of unnamed-chunk-20-1.png

Version	Author	Date
603b51b	Belinda Phipson	2019-10-31
604f664	Belinda Phipson	2019-10-29

DimPlot(smc.integrated, reduction = "tsne", group.by = "batch")

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
603b51b	Belinda Phipson	2019-10-31
604f664	Belinda Phipson	2019-10-29

par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(smc.integrated),smc.integrated$biorep)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(9),legend=TRUE)

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(smc.integrated),smc.integrated$orig.ident)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(3))
legend("topleft",legend=colnames(tab),fill=ggplotColors(3))

Past versions of unnamed-chunk-23-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

Visualisation with clustree

clusres <- c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2)

for(i in 1:length(clusres)){
  smc.integrated <- FindClusters(smc.integrated, 
                                   resolution = clusres[i])
}

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9233
Number of communities: 3
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8775
Number of communities: 3
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8400
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8063
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7766
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7518
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7269
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7072
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6889
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6707
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6536
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 430
Number of edges: 13189

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6366
Number of communities: 8
Elapsed time: 0 seconds

pct.male <- function(x) {mean(x=="m")}
pct.female <- function(x) {mean(x=="f")}
pct.fetal <- function(x) {mean(x=="fetal")}
pct.young <- function(x) {mean(x=="young")}
pct.adult <- function(x) {mean(x=="adult")}

clustree(smc.integrated, prefix = "integrated_snn_res.")

Past versions of unnamed-chunk-26-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

clustree(smc.integrated, prefix = "integrated_snn_res.",
         node_colour = "sex", node_colour_aggr = "pct.female",assay="RNA")

Past versions of unnamed-chunk-27-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

clustree(smc.integrated, prefix = "integrated_snn_res.",
         node_colour = "sex", node_colour_aggr = "pct.male",assay="RNA")

Past versions of unnamed-chunk-28-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

clustree(smc.integrated, prefix = "integrated_snn_res.",
         node_colour = "orig.ident", node_colour_aggr = "pct.fetal",assay="RNA")

Past versions of unnamed-chunk-29-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

clustree(smc.integrated, prefix = "integrated_snn_res.",
         node_colour = "orig.ident", node_colour_aggr = "pct.young",assay="RNA")

Past versions of unnamed-chunk-30-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

clustree(smc.integrated, prefix = "integrated_snn_res.",
         node_colour = "orig.ident", node_colour_aggr = "pct.adult",assay="RNA")

Past versions of unnamed-chunk-31-1.png

Version	Author	Date
604f664	Belinda Phipson	2019-10-29

Save Seurat object

DefaultAssay(smc.integrated) <- "RNA"
Idents(smc.integrated) <- smc.integrated$integrated_snn_res.0.1

saveRDS(smc.integrated,file="./output/RDataObjects/smc-int-FYA-filtered.Rds")
#smc.integrated <- readRDS(file="./output/RDataObjects/smc-int-FYA.Rds")
# Load unfiltered counts matrix for every sample (object all)
load("./output/RDataObjects/all-counts.Rdata")

Session information

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.7 (Final)

Matrix products: default
BLAS:   /usr/local/installed/R/3.6.0/lib64/R/lib/libRblas.so
LAPACK: /usr/local/installed/R/3.6.0/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=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] splines   parallel  stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] dplyr_0.8.3                 clustree_0.4.0             
 [3] ggraph_1.0.2                workflowr_1.3.0            
 [5] NMF_0.21.0                  bigmemory_4.5.33           
 [7] cluster_2.1.0               rngtools_1.4               
 [9] pkgmaker_0.27               registry_0.5-1             
[11] scran_1.12.0                SingleCellExperiment_1.6.0 
[13] SummarizedExperiment_1.14.1 GenomicRanges_1.36.0       
[15] GenomeInfoDb_1.20.0         DelayedArray_0.10.0        
[17] BiocParallel_1.18.1         matrixStats_0.55.0         
[19] cowplot_1.0.0               monocle_2.12.0             
[21] DDRTree_0.1.5               irlba_2.3.3                
[23] VGAM_1.1-1                  ggplot2_3.2.1              
[25] Matrix_1.2-17               Seurat_3.0.3.9019          
[27] org.Hs.eg.db_3.8.2          AnnotationDbi_1.46.1       
[29] IRanges_2.18.1              S4Vectors_0.22.0           
[31] Biobase_2.44.0              BiocGenerics_0.30.0        
[33] RColorBrewer_1.1-2          edgeR_3.26.3               
[35] limma_3.40.2               

loaded via a namespace (and not attached):
  [1] reticulate_1.13          R.utils_2.9.0           
  [3] tidyselect_0.2.5         RSQLite_2.1.2           
  [5] htmlwidgets_1.5          grid_3.6.0              
  [7] combinat_0.0-8           docopt_0.6.1            
  [9] Rtsne_0.15               munsell_0.5.0           
 [11] codetools_0.2-16         ica_1.0-2               
 [13] statmod_1.4.30           future_1.14.0           
 [15] withr_2.1.2              colorspace_1.4-1        
 [17] fastICA_1.2-2            knitr_1.25              
 [19] ROCR_1.0-7               gbRd_0.4-11             
 [21] listenv_0.7.0            labeling_0.3            
 [23] Rdpack_0.11-0            git2r_0.26.1            
 [25] slam_0.1-45              GenomeInfoDbData_1.2.1  
 [27] polyclip_1.10-0          farver_1.1.0            
 [29] bit64_0.9-7              pheatmap_1.0.12         
 [31] rprojroot_1.3-2          vctrs_0.2.0             
 [33] xfun_0.10                R6_2.4.0                
 [35] doParallel_1.0.15        ggbeeswarm_0.6.0        
 [37] rsvd_1.0.2               locfit_1.5-9.1          
 [39] bitops_1.0-6             assertthat_0.2.1        
 [41] SDMTools_1.1-221.1       scales_1.0.0            
 [43] beeswarm_0.2.3           gtable_0.3.0            
 [45] npsurv_0.4-0             globals_0.12.4          
 [47] tidygraph_1.1.2          rlang_0.4.0             
 [49] zeallot_0.1.0            lazyeval_0.2.2          
 [51] checkmate_1.9.4          yaml_2.2.0              
 [53] reshape2_1.4.3           backports_1.1.5         
 [55] tools_3.6.0              gridBase_0.4-7          
 [57] gplots_3.0.1.1           dynamicTreeCut_1.63-1   
 [59] ggridges_0.5.1           Rcpp_1.0.2              
 [61] plyr_1.8.4               zlibbioc_1.30.0         
 [63] purrr_0.3.2              RCurl_1.95-4.12         
 [65] densityClust_0.3         pbapply_1.4-1           
 [67] viridis_0.5.1            zoo_1.8-6               
 [69] ggrepel_0.8.1            fs_1.3.1                
 [71] magrittr_1.5             data.table_1.12.4       
 [73] lmtest_0.9-37            RANN_2.6.1              
 [75] whisker_0.3-2            fitdistrplus_1.0-14     
 [77] lsei_1.2-0               evaluate_0.14           
 [79] xtable_1.8-4             sparsesvd_0.1-4         
 [81] gridExtra_2.3            HSMMSingleCell_1.4.0    
 [83] compiler_3.6.0           scater_1.12.2           
 [85] tibble_2.1.3             KernSmooth_2.23-15      
 [87] crayon_1.3.4             R.oo_1.22.0             
 [89] htmltools_0.4.0          tidyr_0.8.3             
 [91] DBI_1.0.0                tweenr_1.0.1            
 [93] MASS_7.3-51.4            R.methodsS3_1.7.1       
 [95] gdata_2.18.0             metap_1.1               
 [97] igraph_1.2.4.1           pkgconfig_2.0.3         
 [99] bigmemory.sri_0.1.3      plotly_4.9.0            
[101] foreach_1.4.7            vipor_0.4.5             
[103] dqrng_0.2.1              XVector_0.24.0          
[105] bibtex_0.4.2             stringr_1.4.0           
[107] digest_0.6.21            sctransform_0.2.0       
[109] RcppAnnoy_0.0.12         tsne_0.1-3              
[111] rmarkdown_1.14           DelayedMatrixStats_1.6.0
[113] gtools_3.8.1             nlme_3.1-141            
[115] jsonlite_1.6             BiocNeighbors_1.2.0     
[117] viridisLite_0.3.0        pillar_1.4.2            
[119] lattice_0.20-38          httr_1.4.1              
[121] survival_2.44-1.1        glue_1.3.1              
[123] qlcMatrix_0.9.7          FNN_1.1.3               
[125] png_0.1-7                iterators_1.0.12        
[127] bit_1.1-14               ggforce_0.3.0           
[129] stringi_1.4.3            blob_1.2.0              
[131] BiocSingular_1.0.0       caTools_1.17.1.2        
[133] memoise_1.1.0            future.apply_1.3.0      
[135] ape_5.3