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      Fall single cell RNA sequencing workshop @ UCSF

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Introduction and Lectures
Intro to the Workshop and Core
What is Bioinformatics/Genomics Perspective?
Experimental Design and Cost Estimation
Introduction to Command-Line and the Cluster
Logging in and Transferring Files
Intro to Command-Line
Advanced Command-Line (extra)
Running jobs on the Cluster and using modules
Intro to R and Rstudio
Getting Started
Intro to R
Prepare Data in R (extra)
Data in R (extra)
Data Reduction
Project setup
Generating Expression Matrix
scRNAseq Analysis
Prepare scRNAseq Analysis
scRNAseq Analysis - PART1
scRNAseq Analysis - PART2
scRNAseq Analysis - PART3
scRNAseq Analysis - PART4
scRNAseq Analysis - PART5
scRNAseq Analysis - PART6
Guest lecture by Dr. Gerald Quon
Prepare Single Cell Alignment
Single Cell Alignment (scAlign)
Support
Cheat Sheets
Software and Links
Scripts
ETC
Closing thoughts
Workshop Photos
Github
Biocore website

Load libraries

library(Seurat)
library(ggplot2)

Load the Seurat object

load(file="pca_sample_corrected.RData")
experiment.aggregate
An object of class Seurat 12811 features across 2681 samples within 1 assay Active assay: RNA (12811 features) 1 dimensional reduction calculated: pca

Identifying clusters

Seurat implements an graph-based clustering approach. Distances between the cells are calculated based on previously identified PCs. Seurat approach was heavily inspired by recent manuscripts which applied graph-based clustering approaches to scRNAseq data. Briefly, Seurat identify clusters of cells by a shared nearest neighbor (SNN) modularity optimization based clustering algorithm. First calculate k-nearest neighbors (KNN) and construct the SNN graph. Then optimize the modularity function to determine clusters. For a full description of the algorithms, see Waltman and van Eck (2013) The European Physical Journal B.

The FindClusters function implements the procedure, and contains a resolution parameter that sets the granularity of the downstream clustering, with increased values leading to a greater number of clusters. I tend to like to perform a series of resolutions, investigate and choose.

use.pcs = 1:29 

?FindNeighbors
experiment.aggregate <- FindNeighbors(experiment.aggregate, reduction="pca", dims = use.pcs)
Computing nearest neighbor graph
Computing SNN
?FindCluster
No documentation for 'FindCluster' in specified packages and libraries: you could try '??FindCluster'
experiment.aggregate <- FindClusters(
    object = experiment.aggregate, 
    resolution = seq(0.25,4,0.25), 
    verbose = FALSE
)

Lets first investigate how many clusters each resolution produces and set it to the smallest resolutions of 0.5 (fewest clusters).

sapply(grep("res",colnames(experiment.aggregate@meta.data),value = TRUE),
       function(x) length(unique(experiment.aggregate@meta.data[,x])))
RNA_snn_res.0.25 RNA_snn_res.0.5 RNA_snn_res.0.75 RNA_snn_res.1 10 14 15 16 RNA_snn_res.1.25 RNA_snn_res.1.5 RNA_snn_res.1.75 RNA_snn_res.2 18 21 24 24 RNA_snn_res.2.25 RNA_snn_res.2.5 RNA_snn_res.2.75 RNA_snn_res.3 25 26 26 27 RNA_snn_res.3.25 RNA_snn_res.3.5 RNA_snn_res.3.75 RNA_snn_res.4 28 27 28 29
Idents(experiment.aggregate) <- "RNA_snn_res.0.5"

Finally, lets produce a table of cluster to sample assignments.

table(Idents(experiment.aggregate),experiment.aggregate$orig.ident)
UCD_Adj_VitE UCD_Supp_VitE UCD_VitE_Def 0 157 174 161 1 118 141 106 2 77 115 155 3 94 82 94 4 55 77 86 5 51 79 62 6 60 66 65 7 50 52 45 8 23 45 39 9 34 33 37 10 20 30 20 11 31 19 18 12 18 20 19 13 20 14 19

tSNE dimensionality reduction plots are then used to visualise clustering results. As input to the tSNE, you should use the same PCs as input to the clustering analysis.

experiment.aggregate <- RunTSNE(
  object = experiment.aggregate,
  reduction.use = "pca",
  dims.use = use.pcs,
  do.fast = TRUE)

Plot TSNE coloring by the slot ‘ident’ (default).

DimPlot(object = experiment.aggregate, pt.size=0.5, reduction = "tsne", label = T)

Plot TSNE coloring by the clustering resolution 4

DimPlot(object = experiment.aggregate, group.by="RNA_snn_res.4", pt.size=0.5, do.label = TRUE, reduction = "tsne", label = T)

FeaturePlot can be used to color cells with a ‘feature’, non categorical data, like number of UMIs

FeaturePlot(experiment.aggregate, features = c('nCount_RNA'), pt.size=0.5)

and number of genes present

FeaturePlot(experiment.aggregate, features = c('nFeature_RNA'), pt.size=0.5)

percent mitochondrial

FeaturePlot(experiment.aggregate, features = c('percent.mito'), pt.size=0.5)

TSNE plot by cell cycle

DimPlot(object = experiment.aggregate, pt.size=0.5, group.by = "cell.cycle", reduction = "tsne" )

Building a tree relating the ‘average’ cell from each cluster. Tree is estimated based on a distance matrix constructed in either gene expression space or PCA space.

experiment.aggregate <- BuildClusterTree(
  experiment.aggregate, dims = use.pcs)

PlotClusterTree(experiment.aggregate)

DimPlot(object = experiment.aggregate, pt.size=0.5, label = TRUE, reduction = "tsne")

Merge Clustering results

experiment.merged = experiment.aggregate
# originally set clusters to resolutionm 0.5
Idents(experiment.merged) <- "RNA_snn_res.0.5"

table(Idents(experiment.merged))
0 1 2 3 4 5 6 7 8 9 10 11 12 13 492 365 347 270 218 192 191 147 107 104 70 68 57 53
# based on TSNE and Heirarchical tree
# merge clusters 6 and 7 into 0 and cluster 9 into 13
experiment.merged <- RenameIdents(
  object = experiment.merged,
  '6' = '0', '7' = '0', '9' = '13'
)

table(Idents(experiment.merged))
0 13 1 2 3 4 5 8 10 11 12 830 157 365 347 270 218 192 107 70 68 57
DimPlot(object = experiment.merged, pt.size=0.5, label = T, reduction = "tsne")

experiment.examples <- experiment.merged
# in order to reporder the clusters for plotting purposes
# take a look at the levels, which indicates the ordering
levels(experiment.examples@active.ident)
[1] "0" "13" "1" "2" "3" "4" "5" "8" "10" "11" "12"
# relevel setting 5 to the first factor
experiment.examples@active.ident <- relevel(experiment.examples@active.ident, "5")
levels(experiment.examples@active.ident)
[1] "5" "0" "13" "1" "2" "3" "4" "8" "10" "11" "12"
# now cluster 5 is the "first" factor

# relevel all the factors to the order I want
Idents(experiment.examples) <- factor(experiment.examples@active.ident, levels=c("5","13","1","2","3","0","4","8","11","12","10","14"))
levels(experiment.examples@active.ident)
[1] "5" "13" "1" "2" "3" "0" "4" "8" "11" "12" "10"
DimPlot(object = experiment.examples, pt.size=0.5, label = T, reduction = "tsne")

### Re-assign clustering result to resolution 4 for cells in cluster 0 (@ reslution 0.5) [adding a R prefix]
newIdent = as.character(Idents(experiment.examples))
newIdent[newIdent == '0'] = paste0("R",as.character(experiment.examples$RNA_snn_res.4[newIdent == '0']))

Idents(experiment.examples) <- as.factor(newIdent)

table(Idents(experiment.examples))
1 10 11 12 13 2 3 4 5 8 R1 R10 R12 R13 R15 R16 R21 R26 365 70 68 57 157 347 270 218 192 107 180 1 87 83 73 3 58 1 R27 R28 R5 R7 R8 R9 35 1 151 118 37 2
DimPlot(object = experiment.examples, pt.size=0.5, label = T, reduction = "tsne")

Plot TSNE coloring by the slot ‘orig.ident’ (sample names) with alpha colors turned on.

DimPlot(object = experiment.aggregate, group.by="orig.ident", pt.size=0.5, reduction = "tsne" )

## Pretty tsne using alpha
p <- DimPlot(object = experiment.aggregate, group.by="orig.ident", pt.size=0.5, reduction = "tsne", do.return = T)
alpha.use <- 2/5
p$layers[[1]]$mapping$alpha <- alpha.use
p + scale_alpha_continuous(range = alpha.use, guide = F)

Removing cells assigned to clusters from a plot, So here plot all clusters but clusters “3” and “5”

# create a new tmp object with those removed 
experiment.aggregate.tmp <- experiment.aggregate[,-which(Idents(experiment.aggregate) %in% c("3","5"))]

dim(experiment.aggregate)
[1] 12811 2681
dim(experiment.aggregate.tmp)
[1] 12811 2219
DimPlot(object = experiment.aggregate.tmp, group.by="orig.ident", pt.size=0.5, do.label = TRUE, reduction = "tsne", label = T)

Identifying Marker Genes

Seurat can help you find markers that define clusters via differential expression.

FindMarkers identifies markers for a cluster relative to all other clusters.

FindAllMarkers does so for all clusters

FindAllMarkersNode defines all markers that split a Node (Warning: need to validate)

?FindMarkers

markers = FindMarkers(experiment.merged, ident.1=c(10), genes.use = VariableFeatures(experiment.merged))

head(markers)
p_val avg_logFC pct.1 pct.2 p_val_adj Baiap2l1 7.352739e-235 1.1954540 0.714 0.014 9.419593e-231 Cadps2 4.448365e-198 2.3426788 0.971 0.051 5.698801e-194 Tbx3os2 1.753416e-182 0.6699683 0.443 0.005 2.246301e-178 Cbln2 1.849806e-152 1.1941476 0.786 0.038 2.369787e-148 Ntng1 1.302871e-150 1.0795688 0.686 0.028 1.669108e-146 Ntrk2 1.157467e-148 2.0506117 0.971 0.074 1.482831e-144
dim(markers)
[1] 1474 5
table(markers$avg_logFC > 0)
FALSE TRUE 688 786

pct.1 and pct.2 are the proportion of cells with expression above 0 in ident.1 and ident.2 respectively. p_val is the raw p_value associated with the differntial expression test with adjusted value in p_val_adj. avg_logFC is the average log fold change difference between the two groups.

avg_diff (lines 130, 193 and) appears to be the difference in log(x = mean(x = exp(x = x) - 1) + 1) between groups. It doesn’t seem like this should work out to be the signed ratio of pct.1 to pct.2 so I must be missing something. It doesn’t seem to be related at all to how the p-values are calculated so maybe it doesn’t matter so much, and the sign is probably going to be pretty robust to how expression is measured.

Can use a violin plot to visualize the expression pattern of some markers

VlnPlot(object = experiment.merged, features = rownames(markers)[1:2], pt.size = 0.05)

Or a feature plot

FeaturePlot(
    experiment.merged, 
    head(rownames(markers), n=6), 
    cols = c("lightgrey", "blue"), 
    ncol = 2
)

FeaturePlot(    
    experiment.merged, 
    "Fxyd1", 
    cols = c("lightgrey", "blue") 
)

FindAllMarkers can be used to automate the process across all genes. WARNING: TAKES A LONG TIME TO RUN

markers_all <- FindAllMarkers(
    object = experiment.merged, 
    only.pos = TRUE, 
    min.pct = 0.25, 
    thresh.use = 0.25
)
Calculating cluster 0
Calculating cluster 13
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Calculating cluster 4
Calculating cluster 5
Calculating cluster 8
Calculating cluster 10
Calculating cluster 11
Calculating cluster 12
dim(markers_all)
[1] 4140 7
head(markers_all)
p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene Pcp4 1.850077e-112 0.9957707 0.630 0.197 2.370134e-108 0 Pcp4 Tac1 3.148614e-60 0.6683619 0.737 0.458 4.033689e-56 0 Tac1 Marcks 3.243647e-59 0.7666353 0.681 0.397 4.155436e-55 0 Marcks Adcyap1 3.955847e-53 0.9475240 0.433 0.178 5.067836e-49 0 Adcyap1 Nrsn1 2.536293e-50 0.8374397 0.716 0.486 3.249245e-46 0 Nrsn1 Gal 1.310030e-48 0.8974830 0.324 0.100 1.678279e-44 0 Gal
table(table(markers_all$gene))
1 2 3 4 5 6 1511 689 274 87 15 1
markers_all_single <- markers_all[markers_all$gene %in% names(table(markers_all$gene))[table(markers_all$gene) == 1],]

dim(markers_all_single)
[1] 1511 7
table(table(markers_all_single$gene))
1 1511
table(markers_all_single$cluster)
0 13 1 2 3 4 5 8 10 11 12 19 112 98 232 148 150 224 55 338 20 115
head(markers_all_single)
p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene Nrsn1 2.536293e-50 0.8374397 0.716 0.486 3.249245e-46 0 Nrsn1 Gm13889 1.365278e-37 0.6624093 0.658 0.491 1.749057e-33 0 Gm13889 Ctnnd2 4.716368e-20 0.5710907 0.339 0.201 6.042138e-16 0 Ctnnd2 Cpeb2 3.676886e-11 0.4783811 0.500 0.444 4.710458e-07 0 Cpeb2 Nrip1 4.853869e-10 0.6027342 0.290 0.217 6.218291e-06 0 Nrip1 Gprasp1 7.501332e-10 0.4571512 0.408 0.328 9.609956e-06 0 Gprasp1

Plot a heatmap of genes by cluster for the top 5 marker genes per cluster

library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats': filter, lag
The following objects are masked from 'package:base': intersect, setdiff, setequal, union
top5 <- markers_all_single %>% group_by(cluster) %>% top_n(5, avg_logFC)
dim(top5)
[1] 55 7
DoHeatmap(
    object = experiment.merged, 
    features = top5$gene
) 
Warning in DoHeatmap(object = experiment.merged, features = top5$gene): The following features were omitted as they were not found in the scale.data slot for the RNA assay: Mest, Nwd2, Pik3r1, Nrip1, Cpeb2, Ctnnd2, Gm13889, Nrsn1

# Get expression of genes for cells in and out of each cluster
getGeneClusterMeans <- function(gene, cluster){
  x <- GetAssayData(experiment.merged)[gene,]
  m <- tapply(x, ifelse(Idents(experiment.merged) == cluster, 1, 0), mean)
  mean.in.cluster <- m[2]
  mean.out.of.cluster <- m[1]
  return(list(mean.in.cluster = mean.in.cluster, mean.out.of.cluster = mean.out.of.cluster))
}

## for sake of time only using first six (head)
means <- mapply(getGeneClusterMeans, head(markers_all[,"gene"]), head(markers_all[,"cluster"]))
means <- matrix(unlist(means), ncol = 2, byrow = T)

colnames(means) <- c("mean.in.cluster", "mean.out.of.cluster")
rownames(means) <- head(markers_all[,"gene"])
markers_all2 <- cbind(head(markers_all), means)
head(markers_all2)
p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene Pcp4 1.850077e-112 0.9957707 0.630 0.197 2.370134e-108 0 Pcp4 Tac1 3.148614e-60 0.6683619 0.737 0.458 4.033689e-56 0 Tac1 Marcks 3.243647e-59 0.7666353 0.681 0.397 4.155436e-55 0 Marcks Adcyap1 3.955847e-53 0.9475240 0.433 0.178 5.067836e-49 0 Adcyap1 Nrsn1 2.536293e-50 0.8374397 0.716 0.486 3.249245e-46 0 Nrsn1 Gal 1.310030e-48 0.8974830 0.324 0.100 1.678279e-44 0 Gal mean.in.cluster mean.out.of.cluster Pcp4 1.6811115 0.4801027 Tac1 2.0261848 1.0531066 Marcks 1.5559093 0.7591471 Adcyap1 1.0159140 0.3406419 Nrsn1 1.7072675 0.9505800 Gal 0.7620386 0.2084508

Finishing up clusters.

At this point in time you should use the tree, markers, domain knowledge, and goals to finalize your clusters. This may mean adjusting PCA to use, mergers clusters together, choosing a new resolutions, etc. When finished you can further name it cluster by something more informative. Ex.

experiment.clusters <- experiment.aggregate
experiment.clusters <- RenameIdents(
  object = experiment.clusters,
  '0' = 'cell_type_A',
  '1' = 'cell_type_B',
  '2' = 'cell_type_C'
)
# and so on

DimPlot(object = experiment.clusters, pt.size=0.5, label = T, reduction = "tsne")

experiment.merged$finalcluster <- Idents(experiment.merged)

Subsetting samples

If you want to look at the representation of just one sample, or sets of samples

experiment.sample2 <- subset(experiment.merged, orig.ident == "UCD_Supp_VitE")

DimPlot(object = experiment.sample2, group.by = "RNA_snn_res.0.5", pt.size=0.5, label = TRUE, reduction = "tsne")

FeaturePlot(experiment.sample2, features =c('Calca'), pt.size=0.5)

FeaturePlot(experiment.sample2, features =c('Adcyap1'), pt.size=0.5)

experiment.batch1 <- subset(experiment.merged, batchid == "Batch1")

DimPlot(object = experiment.batch1, group.by = "RNA_snn_res.0.5", pt.size=0.5, label = TRUE, reduction = "tsne")

Adding in a new metadata column representing samples within clusters

experiment.merged$samplecluster = paste(experiment.merged$orig.ident,experiment.merged$finalcluster,sep = '-')

# set the identity to the new variable 
Idents(experiment.merged) <- "samplecluster"

markers.comp <- FindMarkers(experiment.merged, ident.1 = "UCD_Adj_VitE-0", ident.2= c("UCD_Supp_VitE-0","UCD_VitE_Def-0"))

markers.comp
p_val avg_logFC pct.1 pct.2 p_val_adj Actb 1.183758e-10 -0.5181553 0.993 0.972 1.516513e-06 Ndufa3 1.726235e-07 0.2771138 0.633 0.419 2.211479e-03 Pcp4 3.185979e-07 0.3935550 0.745 0.575 4.081557e-03 Tmsb10 4.479867e-07 0.2762000 0.948 0.897 5.739157e-03 Rpl21 6.119361e-07 0.3487513 0.861 0.716 7.839513e-03 Atp5e 1.245447e-05 0.2579366 0.727 0.570 1.595543e-01 Erp29 5.928481e-05 0.2908362 0.360 0.224 7.594977e-01 Dbpht2 1.636614e-04 0.2793749 0.303 0.181 1.000000e+00 Arhgap15 2.074370e-04 0.2892275 0.180 0.089 1.000000e+00 Gpx3 2.216773e-04 0.2730416 0.382 0.245 1.000000e+00 Wdfy1 1.831103e-03 -0.2644521 0.056 0.126 1.000000e+00 Itga6 3.983884e-03 0.2519751 0.112 0.055 1.000000e+00 Cbx3 4.043714e-03 -0.4100692 0.345 0.407 1.000000e+00 Cartpt 1.248596e-02 0.2926574 0.172 0.108 1.000000e+00 Mt2 1.512022e-02 0.2709658 0.187 0.123 1.000000e+00 Aff3 2.886276e-02 -0.2680419 0.105 0.156 1.000000e+00 Abhd2 3.420656e-02 -0.4601006 0.341 0.387 1.000000e+00 Birc6 3.474076e-02 -0.3123984 0.139 0.190 1.000000e+00 Plp1 4.093181e-02 -0.2754103 0.127 0.181 1.000000e+00 Hdlbp 4.918260e-02 -0.3744042 0.146 0.188 1.000000e+00 Cadm3 8.110966e-02 -0.2948876 0.210 0.247 1.000000e+00 Clasp2 8.530859e-02 -0.3256511 0.195 0.234 1.000000e+00 Lasp1 9.134266e-02 -0.2663812 0.165 0.204 1.000000e+00 Usp22 9.683177e-02 -0.2661925 0.288 0.325 1.000000e+00 Akap9 9.685074e-02 -0.2512918 0.161 0.201 1.000000e+00 Necab1 1.067273e-01 -0.3338435 0.131 0.167 1.000000e+00 Zhx1 1.369138e-01 -0.2584958 0.101 0.131 1.000000e+00 Fam168b 1.679998e-01 -0.2650448 0.176 0.202 1.000000e+00 Bhlhe41 1.791974e-01 -0.2964435 0.348 0.377 1.000000e+00 Aqp1 1.877121e-01 -0.2666116 0.356 0.380 1.000000e+00 Jup 1.933382e-01 -0.2724179 0.315 0.332 1.000000e+00 Ddhd1 2.063209e-01 -0.2885703 0.109 0.133 1.000000e+00 Gpsm3 2.149255e-01 -0.2692685 0.097 0.121 1.000000e+00 Mt1 2.226626e-01 0.2690128 0.464 0.407 1.000000e+00 Armc8 2.539710e-01 -0.2711362 0.172 0.195 1.000000e+00 Rtcb 2.887037e-01 -0.2647632 0.154 0.171 1.000000e+00 Pam 3.206657e-01 -0.3743205 0.509 0.490 1.000000e+00 Ythdf2 3.773664e-01 -0.2782778 0.311 0.307 1.000000e+00 Wtap 4.772890e-01 -0.2980818 0.348 0.343 1.000000e+00 Spry2 5.154505e-01 -0.2640849 0.213 0.217 1.000000e+00 Acpp 5.297953e-01 -0.2583925 0.195 0.197 1.000000e+00 Nsg2 6.207279e-01 -0.2622005 0.213 0.211 1.000000e+00 Setd3 8.494933e-01 -0.2720362 0.285 0.263 1.000000e+00
experiment.subset <- subset(experiment.merged, samplecluster %in%  c( "UCD_Adj_VitE-0", "UCD_Supp_VitE-0" ))
DoHeatmap(experiment.subset, features = rownames(markers.comp))
Warning in DoHeatmap(experiment.subset, features = rownames(markers.comp)): The following features were omitted as they were not found in the scale.data slot for the RNA assay: Setd3, Nsg2, Wtap, Ythdf2, Rtcb, Armc8, Ddhd1, Jup, Aqp1, Fam168b, Zhx1, Necab1, Akap9, Usp22, Lasp1, Clasp2, Hdlbp, Birc6, Aff3, Itga6, Wdfy1, Arhgap15, Erp29, Atp5e, Rpl21, Tmsb10, Ndufa3

Idents(experiment.merged) <- "finalcluster"

And last lets save all the objects in our session.

save(list=ls(), file="clusters_seurat_object.RData")

Get the next Rmd file

download.file("https://raw.githubusercontent.com/ucdavis-bioinformatics-training/2019-single-cell-RNA-sequencing-Workshop-UCD_UCSF/master/scrnaseq_analysis/scRNA_Workshop-PART6.Rmd", "scRNA_Workshop-PART6.Rmd")

Session Information

sessionInfo()
R version 3.6.0 (2019-04-26) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS Mojave 10.14.5 Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] dplyr_0.8.1 ggplot2_3.2.0 Seurat_3.0.2 loaded via a namespace (and not attached): [1] httr_1.4.0 tidyr_0.8.3 viridisLite_0.3.0 [4] jsonlite_1.6 splines_3.6.0 lsei_1.2-0 [7] R.utils_2.9.0 gtools_3.8.1 Rdpack_0.11-0 [10] assertthat_0.2.1 yaml_2.2.0 ggrepel_0.8.1 [13] globals_0.12.4 pillar_1.4.1 lattice_0.20-38 [16] reticulate_1.12 glue_1.3.1 digest_0.6.19 [19] RColorBrewer_1.1-2 SDMTools_1.1-221.1 colorspace_1.4-1 [22] cowplot_0.9.4 htmltools_0.3.6 Matrix_1.2-17 [25] R.oo_1.22.0 plyr_1.8.4 pkgconfig_2.0.2 [28] bibtex_0.4.2 tsne_0.1-3 listenv_0.7.0 [31] purrr_0.3.2 scales_1.0.0 RANN_2.6.1 [34] gdata_2.18.0 Rtsne_0.15 tibble_2.1.3 [37] withr_2.1.2 ROCR_1.0-7 pbapply_1.4-0 [40] lazyeval_0.2.2 survival_2.44-1.1 magrittr_1.5 [43] crayon_1.3.4 evaluate_0.14 R.methodsS3_1.7.1 [46] future_1.13.0 nlme_3.1-140 MASS_7.3-51.4 [49] gplots_3.0.1.1 ica_1.0-2 tools_3.6.0 [52] fitdistrplus_1.0-14 data.table_1.12.2 gbRd_0.4-11 [55] stringr_1.4.0 plotly_4.9.0 munsell_0.5.0 [58] cluster_2.1.0 irlba_2.3.3 compiler_3.6.0 [61] rsvd_1.0.1 caTools_1.17.1.2 rlang_0.3.4 [64] grid_3.6.0 ggridges_0.5.1 htmlwidgets_1.3 [67] igraph_1.2.4.1 labeling_0.3 bitops_1.0-6 [70] rmarkdown_1.13 npsurv_0.4-0 gtable_0.3.0 [73] codetools_0.2-16 reshape2_1.4.3 R6_2.4.0 [76] gridExtra_2.3 zoo_1.8-6 knitr_1.23 [79] future.apply_1.3.0 KernSmooth_2.23-15 metap_1.1 [82] ape_5.3 stringi_1.4.3 parallel_3.6.0 [85] Rcpp_1.0.1 sctransform_0.2.0 png_0.1-7 [88] tidyselect_0.2.5 xfun_0.7 lmtest_0.9-37