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, 2000 variable 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 20 24 24
RNA_snn_res.2.25 RNA_snn_res.2.5 RNA_snn_res.2.75 RNA_snn_res.3
25 26 27 28
RNA_snn_res.3.25 RNA_snn_res.3.5 RNA_snn_res.3.75 RNA_snn_res.4
28 28 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
0 157 171 156
1 120 142 107
2 78 118 159
3 93 81 94
4 55 77 86
5 60 67 65
6 51 79 62
7 49 51 45
8 23 45 39
9 34 33 38
10 20 30 20
11 30 19 17
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)
Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.
Plot TSNE coloring by the clustering resolution 4
DimPlot(object = experiment.aggregate, group.by="RNA_snn_res.4", pt.size=0.5, reduction = "tsne", label = T)
uMAP dimensionality reduction plot.
experiment.aggregate <- RunUMAP(
object = experiment.aggregate,
dims = use.pcs)
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 session
07:41:54 UMAP embedding parameters a = 0.9922 b = 1.112
07:41:54 Read 2681 rows and found 29 numeric columns
07:41:54 Using Annoy for neighbor search, n_neighbors = 30
07:41:54 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
07:41:55 Writing NN index file to temp file /var/folders/74/h45z17f14l9g34tmffgq9nkw0000gn/T//RtmpvxH7UJ/file9d173360a454
07:41:55 Searching Annoy index using 1 thread, search_k = 3000
07:41:55 Annoy recall = 100%
07:41:55 Commencing smooth kNN distance calibration using 1 thread
07:41:56 Initializing from normalized Laplacian + noise
07:41:56 Commencing optimization for 500 epochs, with 107536 positive edges
07:41:59 Optimization finished
Plot uMap coloring by the slot ‘ident’ (default).
DimPlot(object = experiment.aggregate, pt.size=0.5, reduction = "umap", 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
484 369 355 268 218 192 192 145 107 105 70 66 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
821 158 369 355 268 218 192 107 70 66 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 R11 R12 R13 R14 R16 R20 R21 R26
369 70 66 57 158 355 268 218 192 107 172 2 1 92 83 1 5 59 58 42
R27 R28 R6 R8 R9
32 1 145 105 23
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")
alpha.use <- 2/5
p$layers[[1]]$mapping$alpha <- alpha.use
p + scale_alpha_continuous(range = alpha.use, guide = F)
Removing cells assigned to clusters from a plot, So here plot all clusters but clusters “3” and “5”
# create a new tmp object with those removed
experiment.aggregate.tmp <- experiment.aggregate[,-which(Idents(experiment.aggregate) %in% c("3","5"))]
dim(experiment.aggregate)
[1] 12811 2681
dim(experiment.aggregate.tmp)
[1] 12811 2221
DimPlot(object = experiment.aggregate.tmp, group.by="orig.ident", pt.size=0.5, reduction = "tsne", label = T)
Identifying Marker Genes
Seurat can help you find markers that define clusters via differential expression.
FindMarkers identifies markers for a cluster relative to all other clusters.
FindAllMarkers does so for all clusters
FindAllMarkersNode defines all markers that split a Node (Warning: need to validate)
?FindMarkers
markers = FindMarkers(experiment.merged, ident.1=c(10), genes.use = VariableFeatures(experiment.merged))
head(markers)
p_val avg_logFC pct.1 pct.2 p_val_adj
Baiap2l1 7.352739e-235 1.1954540 0.714 0.014 9.419593e-231
Cadps2 4.448365e-198 2.3426788 0.971 0.051 5.698801e-194
Tbx3os2 1.753416e-182 0.6699683 0.443 0.005 2.246301e-178
Cbln2 1.849806e-152 1.1941476 0.786 0.038 2.369787e-148
Ntng1 1.302871e-150 1.0795688 0.686 0.028 1.669108e-146
Ntrk2 1.157467e-148 2.0506117 0.971 0.074 1.482831e-144
dim(markers)
[1] 1474 5
table(markers$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] 4558 7
head(markers_all)
p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
Fxyd7 1.080507e-195 1.8236300 0.622 0.095 1.384238e-191 0 Fxyd7
Epb41l3 8.105698e-137 0.9447680 0.886 0.646 1.038421e-132 0 Epb41l3
Pcp4 8.134008e-117 1.1867777 0.622 0.202 1.042048e-112 0 Pcp4
Syt2 4.075036e-95 0.7285700 0.317 0.034 5.220529e-91 0 Syt2
Ppp3ca 7.953394e-94 0.7186081 0.932 0.827 1.018909e-89 0 Ppp3ca
Map1b 7.038770e-93 0.7138478 0.906 0.834 9.017368e-89 0 Map1b
table(table(markers_all$gene))
1 2 3 4 5 6
1460 735 355 107 21 5
markers_all_single <- markers_all[markers_all$gene %in% names(table(markers_all$gene))[table(markers_all$gene) == 1],]
dim(markers_all_single)
[1] 1460 7
table(table(markers_all_single$gene))
1
1460
table(markers_all_single$cluster)
0 13 1 2 3 4 5 8 10 11 12
98 102 93 113 101 143 259 51 372 16 112
head(markers_all_single)
p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
Syt2 4.075036e-95 0.7285700 0.317 0.034 5.220529e-91 0 Syt2
Map1b 7.038770e-93 0.7138478 0.906 0.834 9.017368e-89 0 Map1b
Pou4f3 3.681679e-70 0.7213944 0.253 0.032 4.716599e-66 0 Pou4f3
Faim2 1.249016e-64 0.6612418 0.262 0.041 1.600115e-60 0 Faim2
Clec2l 1.678623e-56 0.9394917 0.252 0.048 2.150484e-52 0 Clec2l
Bcl11b 4.229328e-55 0.6431914 0.263 0.053 5.418192e-51 0 Bcl11b
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, Chd5, Nwd2, Pik3r1, Nrsn1, Nrip1
# 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
Fxyd7 1.080507e-195 1.8236300 0.622 0.095 1.384238e-191 0 Fxyd7
Epb41l3 8.105698e-137 0.9447680 0.886 0.646 1.038421e-132 0 Epb41l3
Pcp4 8.134008e-117 1.1867777 0.622 0.202 1.042048e-112 0 Pcp4
Syt2 4.075036e-95 0.7285700 0.317 0.034 5.220529e-91 0 Syt2
Ppp3ca 7.953394e-94 0.7186081 0.932 0.827 1.018909e-89 0 Ppp3ca
Map1b 7.038770e-93 0.7138478 0.906 0.834 9.017368e-89 0 Map1b
mean.in.cluster mean.out.of.cluster
Fxyd7 1.6798649 0.19407543
Epb41l3 2.3404669 1.23177418
Pcp4 1.7363581 0.46152834
Syt2 0.4960596 0.05270559
Ppp3ca 2.8546865 1.98236564
Map1b 2.8048819 2.04237303
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 8.861881e-10 -0.5673947 0.992 0.977 1.135296e-05
Rpl21 7.774848e-08 0.3881549 0.864 0.706 9.960357e-04
Pcp4 4.741533e-07 0.4131801 0.735 0.571 6.074377e-03
Rpl23a 2.021054e-06 0.2894962 0.895 0.766 2.589172e-02
Rpl17 2.608916e-06 0.2684048 0.860 0.722 3.342283e-02
Eif3f 1.326337e-05 0.2665792 0.716 0.546 1.699171e-01
Gpx3 1.765717e-05 0.3159426 0.405 0.243 2.262061e-01
Wdfy1 2.520164e-05 -0.2918401 0.058 0.167 3.228583e-01
Arhgap15 3.719021e-05 0.2894767 0.257 0.137 4.764438e-01
S100a11 4.095142e-05 0.2800170 0.296 0.167 5.246287e-01
Dbpht2 1.257523e-04 0.2664507 0.381 0.245 1.000000e+00
Zfp467 2.451262e-04 0.2537192 0.152 0.071 1.000000e+00
Arf3 9.047709e-04 -0.2881535 0.599 0.649 1.000000e+00
Abhd2 1.222027e-03 -0.6137435 0.409 0.493 1.000000e+00
Cartpt 1.472543e-03 0.3209726 0.202 0.115 1.000000e+00
Polr2f 2.437619e-03 0.2545627 0.253 0.167 1.000000e+00
Vdac1 3.202511e-03 -0.2773691 0.518 0.574 1.000000e+00
Mt2 3.830778e-03 0.2982468 0.222 0.140 1.000000e+00
Ndn 6.667147e-03 0.2547047 0.393 0.303 1.000000e+00
Cbx3 8.925948e-03 -0.3627569 0.354 0.418 1.000000e+00
Tagln2 9.775257e-03 -0.2702790 0.066 0.126 1.000000e+00
Cadm3 1.275191e-02 -0.2684285 0.253 0.326 1.000000e+00
Pspc1 1.379649e-02 -0.2637400 0.070 0.124 1.000000e+00
Eno2 1.992050e-02 -0.2610868 0.490 0.543 1.000000e+00
Necab1 2.091664e-02 -0.3307004 0.148 0.209 1.000000e+00
Mt1 2.212451e-02 0.2748391 0.580 0.482 1.000000e+00
Fam168b 2.295118e-02 -0.2826729 0.226 0.285 1.000000e+00
Lix1 2.510876e-02 -0.3550222 0.237 0.291 1.000000e+00
S100a16 2.539679e-02 -0.3021223 0.105 0.161 1.000000e+00
Ptgir 3.874847e-02 -0.2614559 0.070 0.113 1.000000e+00
Ddhd1 4.619008e-02 -0.2977759 0.187 0.239 1.000000e+00
Srrm2 5.308238e-02 -0.2572114 0.342 0.388 1.000000e+00
Rere 5.664667e-02 -0.2638236 0.179 0.230 1.000000e+00
Clasp2 5.884710e-02 -0.3059209 0.214 0.264 1.000000e+00
Etv5 6.361715e-02 -0.2762539 0.179 0.225 1.000000e+00
Zbtb4 6.483122e-02 -0.2652181 0.284 0.328 1.000000e+00
Birc6 7.223208e-02 -0.2824634 0.163 0.207 1.000000e+00
Hdlbp 7.359119e-02 -0.3340025 0.222 0.261 1.000000e+00
Nat8l 8.014531e-02 -0.2993328 0.346 0.381 1.000000e+00
Ythdf2 1.112498e-01 -0.2643991 0.276 0.312 1.000000e+00
Nfia 1.960180e-01 -0.2508155 0.323 0.348 1.000000e+00
Bhlhe41 2.841893e-01 -0.2688964 0.389 0.404 1.000000e+00
Pam 3.461588e-01 -0.3386852 0.467 0.468 1.000000e+00
Wtap 3.912595e-01 -0.2518234 0.366 0.372 1.000000e+00
Setd3 5.982182e-01 -0.2640513 0.323 0.312 1.000000e+00
Plp1 6.189997e-01 -0.2627386 0.144 0.151 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, Wtap, Ythdf2, Hdlbp, Birc6, Zbtb4, Clasp2, Rere,
Srrm2, Ddhd1, Fam168b, Necab1, Pspc1, Vdac1, Polr2f, Arf3, Zfp467, Arhgap15,
Wdfy1, Eif3f, Rpl17, Rpl23a, Rpl21
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/2020-Intro_Single_Cell_RNA_Seq/master/data_analysis/scRNA_Workshop-PART6.Rmd", "scRNA_Workshop-PART6.Rmd")
Session Information
sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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 datasets utils methods base
other attached packages:
[1] dplyr_0.8.5 ggplot2_3.3.0 Seurat_3.1.5
loaded via a namespace (and not attached):
[1] httr_1.4.1 tidyr_1.0.3 jsonlite_1.6.1
[4] viridisLite_0.3.0 splines_4.0.0 leiden_0.3.3
[7] assertthat_0.2.1 BiocManager_1.30.10 renv_0.10.0
[10] yaml_2.2.1 ggrepel_0.8.2 globals_0.12.5
[13] pillar_1.4.4 lattice_0.20-41 limma_3.44.1
[16] glue_1.4.1 reticulate_1.15 digest_0.6.25
[19] RColorBrewer_1.1-2 colorspace_1.4-1 cowplot_1.0.0
[22] htmltools_0.4.0 Matrix_1.2-18 plyr_1.8.6
[25] pkgconfig_2.0.3 tsne_0.1-3 listenv_0.8.0
[28] purrr_0.3.4 patchwork_1.0.0 scales_1.1.1
[31] RANN_2.6.1 RSpectra_0.16-0 Rtsne_0.15
[34] tibble_3.0.1 farver_2.0.3 ellipsis_0.3.1
[37] withr_2.2.0 ROCR_1.0-11 pbapply_1.4-2
[40] lazyeval_0.2.2 survival_3.1-12 magrittr_1.5
[43] crayon_1.3.4 evaluate_0.14 future_1.17.0
[46] nlme_3.1-147 MASS_7.3-51.5 ica_1.0-2
[49] tools_4.0.0 fitdistrplus_1.1-1 data.table_1.12.8
[52] lifecycle_0.2.0 stringr_1.4.0 plotly_4.9.2.1
[55] munsell_0.5.0 cluster_2.1.0 irlba_2.3.3
[58] compiler_4.0.0 rsvd_1.0.3 rlang_0.4.6
[61] grid_4.0.0 ggridges_0.5.2 RcppAnnoy_0.0.16
[64] htmlwidgets_1.5.1 igraph_1.2.5 labeling_0.3
[67] rmarkdown_2.1 gtable_0.3.0 codetools_0.2-16
[70] reshape2_1.4.4 R6_2.4.1 gridExtra_2.3
[73] zoo_1.8-8 knitr_1.28 uwot_0.1.8
[76] future.apply_1.5.0 KernSmooth_2.23-16 ape_5.3
[79] stringi_1.4.6 parallel_4.0.0 Rcpp_1.0.4.6
[82] vctrs_0.3.0 sctransform_0.2.1 png_0.1-7
[85] tidyselect_1.1.0 xfun_0.13 lmtest_0.9-37