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Last updated: 2020-03-23
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Knit directory: 10x-adipocyte-analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 9c28b10 | Pytrik Folkertsma | 2020-03-23 | Removed unnecessary libraries |
| html | 6e008da | Pytrik Folkertsma | 2020-03-23 | Build site. |
| Rmd | 98f120f | Pytrik Folkertsma | 2020-03-23 | Removed integration from notebook |
| Rmd | 1ad23ea | Pytrik Folkertsma | 2020-03-19 | Wolfrum 180831 integration with Harmony and Liger |
| Rmd | d306f3f | Pytrik Folkertsma | 2020-03-19 | Liger and Harmony integration |
| Rmd | adae7b7 | Pytrik Folkertsma | 2020-01-23 | vijay analysis branch markers |
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| Rmd | a3dbe85 | Pytrik Folkertsma | 2019-12-02 | wflow_publish(c(“analysis/wolfrum_analysis.Rmd”)) |
| html | d28b5ef | Pytrik Folkertsma | 2019-11-15 | Build site. |
| Rmd | 55cb46e | Pytrik Folkertsma | 2019-11-15 | wolfrum analysis |
| Rmd | 5cffa21 | Pytrik Folkertsma | 2019-11-13 | wolfrum analysis |
| Rmd | 9ae091e | Pytrik Folkertsma | 2019-11-12 | wolfrum branch markers |
library(Seurat)
library(monocle)
library(cowplot)
library(dplyr)
library(tidyr)
library(knitr)
library(kableExtra)
library(DT)wolfrum <- readRDS('/projects/timshel/sc-scheele_lab_adipose_fluidigm_c1/data-wolfrum/wolfrum.compute.seurat_obj.rds')
data_180831 <- readRDS('/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/output/seurat_objects/180831/10x-180831-S3')Which clusters are adipocytes/preadipocytes?
plot_grid(
UMAPPlot(wolfrum, group.by='orig.ident', label=T),
UMAPPlot(wolfrum, group.by='seurat_clusters', 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.
markers <- read.table('output/markergenes/wolfrum/markers_wolfrum.compute.seurat_obj.rds_seurat_clusters_negbinom', sep='\t', header=T)Top 10 positive markers per cluster
pos_markers_top10 <- markers %>%
group_by(cluster) %>%
top_n(n=10, wt=avg_logFC)
neg_markers_top20 <- markers %>%
group_by(cluster) %>%
top_n(n=6, wt=desc(avg_logFC))
pos_markers_top10# A tibble: 270 x 7
# Groups: cluster [27]
cluster p_val avg_logFC pct.1 pct.2 p_val_adj gene
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
1 0 0 -0.250 0.04 0.213 0 NHLRC2
2 0 0 -0.250 0.046 0.242 0 KANSL3
3 0 0 -0.250 0.041 0.238 0 PSMA5
4 0 0 -0.250 0.077 0.248 0 ZNF106
5 0 0 -0.250 0.045 0.201 0 C9orf85
6 0 0 -0.250 0.042 0.247 0 ILKAP
7 0 0 -0.250 0.037 0.24 0 PHF11
8 0 0 -0.250 0.05 0.242 0 SEL1L
9 0 0 -0.251 0.044 0.255 0 KLC1
10 0 0 -0.251 0.034 0.192 0 DZIP3
# … with 260 more rowsWhich clusters are the preadipocytes? Check by plotting some of the DE genes between T1T2T3 and T4T5 in the 180831 dataset.
#DE genes between T1T2T3 and T4T5 in the 10x-180831 data.
markers_T1T2T3_T4T5 <- read.table('output/markergenes/180831/markers_10x-180831_time_combined_negbinom', header=T)
markers_T1T2T3_T4T5 <- markers_T1T2T3_T4T5[order(-markers_T1T2T3_T4T5$avg_logFC),]
markers_T1T2T3 <- markers_T1T2T3_T4T5[which(markers_T1T2T3_T4T5$cluster == 1),]
markers_T4T5 <- markers_T1T2T3_T4T5[which(markers_T1T2T3_T4T5$cluster == 2),]How do these genes look in the 180831 data?
plots <- FeaturePlot(data_180831, features=c(as.vector(markers_T1T2T3$gene)[1:10], as.vector(markers_T4T5$gene)[1:10]), pt.size=1, combine=F)
plot_grid(plotlist=plots, ncol=4)
How are they expressed in the Wolfrum data?
plots <- FeaturePlot(wolfrum, features=c(as.vector(markers_T1T2T3$gene)[1:10], as.vector(markers_T4T5$gene)[1:10]), pt.size=1, combine=F)
plot_grid(plotlist=plots, ncol=2)
Not all genes are expressed clearly in specific clusters. It looks like cluster 22, 21, 5, 14, 23, 11 and 10 are the preadipocytes.
UMAPPlot(wolfrum, group.by='seurat_clusters', label=T)
Also plot the top 10 ECM and Metabolic markers.
#DE genes between T1T2T3 and T4T5 in the 10x-180831 data.
markers_u_l <- read.table('output/markergenes/180831/markers_10x-180831_upperbranch_lowerbranch_negbinom', sep='\t', header=T)
markers_u <- markers_u_l[order(-markers_u_l$avg_logFC),]
markers_l <- markers_u_l[order(markers_u_l$avg_logFC),]How do these genes look in the 180831 data?
plots <- FeaturePlot(data_180831, features=c(as.vector(markers_u$gene)[1:10], as.vector(markers_l$gene)[1:10]), pt.size=1, combine=F)
plot_grid(plotlist=plots, ncol=4)
And how are they expressed in the Wolfrum data?
plots <- FeaturePlot(wolfrum, features=c(as.vector(markers_u$gene)[1:10], as.vector(markers_l$gene)[1:10]), pt.size=1, combine=F)Warning in FetchData(object = object, vars = c(dims, "ident", features), : The
following requested variables were not found: RP11-572C15.6plot_grid(plotlist=plots, ncol=2)
The U and L branch markers are clearly expressed in the two clusters in the top right of the UMAP plot.
Check how many of the marker genes are found in each cluster.
get_gene_overlap_per_cluster <- function(min_logFC, n_genes){
pos_markers <- markers[markers$avg_logFC > min_logFC,]
genes_P <- c(as.vector(markers_T1T2T3$gene)[1:n_genes])
#genes_P <- c(as.vector(markers_T1T2T3$gene)[1:n_genes], as.vector(markers_T4T5$gene)[1:n_genes])
genes_U <- as.vector(markers_u$gene)[1:n_genes]
genes_L <- as.vector(markers_l$gene)[1:n_genes]
all_genes <- unique(c(genes_P, genes_U, genes_L))
df <- as.data.frame(matrix(ncol=4, nrow=length(unique(pos_markers$cluster))))
colnames(df) <- c('total_overlap', 'overlap_P', 'overlap_L', 'overlap_U')
rownames(df) <- unique(pos_markers$cluster)
for (i in 1:length(unique(pos_markers$cluster))){
genes_cluster <- as.vector(pos_markers[pos_markers$cluster == i,]$gene)
df[i, 'overlap_P'] <- round(length(intersect(genes_cluster, genes_P)) / length(genes_P), 2)
df[i, 'overlap_L'] <- round(length(intersect(genes_cluster, genes_L)) / length(genes_L), 2)
df[i, 'overlap_U'] <- round(length(intersect(genes_cluster, genes_U)) / length(genes_U), 2)
df[i, 'total_overlap'] <- round(length(intersect(genes_cluster, all_genes)) / length(all_genes), 2)
}
return(df)
}
print_top_clusters <- function(df){
print(paste('P: ', toString(rownames(df[order(-df$overlap_P),][1:3,])), sep=''))
print(paste('L: ', toString(rownames(df[order(-df$overlap_L),][1:3,])), sep=''))
print(paste('U: ', toString(rownames(df[order(-df$overlap_U),][1:3,])), sep=''))
}Table shows the percentage of genes with avgLogFC > 0.7 that was found in the cluster. Sort on columns to get the top clusters.
logfc_0.5_genes_100 <- get_gene_overlap_per_cluster(0.5, 100)
datatable(logfc_0.5_genes_100)Check the top 5 clusters per branch for different n_genes and min logFC
logfc <- c(0.25, 0.5, 0.7)
n_genes <- c(100, 100, 100)
for (i in 1:length(logfc)){
print(paste('min_logFC: ', logfc[i], ' | n_genes: ', n_genes[i], sep=''))
df <- get_gene_overlap_per_cluster(logfc[i], n_genes[i])
print_top_clusters(df)
}[1] "min_logFC: 0.25 | n_genes: 100"
[1] "P: 24, 11, 22"
[1] "L: 11, 10, 23"
[1] "U: 14, 24, 23"
[1] "min_logFC: 0.5 | n_genes: 100"
[1] "P: 24, 11, 22"
[1] "L: 11, 10, 23"
[1] "U: 14, 23, 24"
[1] "min_logFC: 0.7 | n_genes: 100"
[1] "P: 24, 11, 19"
[1] "L: 11, 10, 23"
[1] "U: 14, 23, 22"There is some overlap between branches which is good. Cluster 11 is shared between P and L, cluster 14 is shared between P and U and cluster 23 is shared between L and U. This would indicate that cluster 24 contains most immature preadipocytes, cluster 10 contains most mature L branch cells and cluster 22 contains most mature U branch cells.\
Based on the results:\ P = 24\ L = 11\ U = 14\
Hypothesis: cluster 23 represents preadipocytes at the start of differentation (the cell states between T3 and T4 in 180831 data that we missed). Cluster 5 represents even more mature metabolic cells and cluster 11 represents more mature ECM cells.\
Clusters 22 shares most genes with the P branch. Cluster 23 most with the U branch. (see datatable above). These could also represent the preadipocytes at start of differentiation or the cells that transfer back to progenitor cells. \
UMAPPlot(wolfrum, group.by='seurat_clusters', label=T)
#Preadipocyte subset
#Idents(wolfrum) <- wolfrum@meta.data$seurat_clusters
#preadipocyte_subset <- subset(wolfrum, idents=c(5, 14, 23, 11, 10, 21, 22, 24))
#preadipocyte_subset <- FindVariableFeatures(preadipocyte_subset)
#preadipocyte_subset <- ScaleData(preadipocyte_subset)
#preadipocyte_subset <- RunPCA(object=preadipocyte_subset, npcs=30)
#ElbowPlot(preadipocyte_subset, ndims=30)
#preadipocyte_subset <- FindNeighbors(object = preadipocyte_subset, dims=1:13)
#preadipocyte_subset <- FindClusters(object = preadipocyte_subset, resolution=0.8)
#preadipocyte_subset <- RunTSNE(object = preadipocyte_subset, dims=1:13)
#preadipocyte_subset <- RunUMAP(object = preadipocyte_subset, dims=1:13)
#saveRDS(preadipocyte_subset, '/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/output/seurat_objects/wolfrum/wolfrum.preadipocyte_subset.rds')
preadipocyte_subset <- readRDS('output/seurat_objects/wolfrum/wolfrum.preadipocyte_subset.rds')
plot_grid(
UMAPPlot(preadipocyte_subset, group.by='all_data_seurat_clusters', label=T),
TSNEPlot(preadipocyte_subset, group.by='all_data_seurat_clusters', label=T)
)
Figures
adipoq <- FeaturePlot(wolfrum, features='ADIPOQ') + NoLegend() + NoAxes() + theme(plot.title = element_text(size=20)) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar', limits=c(0,5))Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.lipe <- FeaturePlot(wolfrum, features='LIPE') + NoLegend() + NoAxes() + theme(plot.title = element_text(size=20)) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar', limits=c(0,5))Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.apod <- FeaturePlot(wolfrum, features='APOD') + NoLegend() + NoAxes() + theme(plot.title = element_text(size=20)) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar', limits=c(0,5))Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.dcn <- FeaturePlot(wolfrum, features='DCN') + NoAxes() + theme(plot.title = element_text(size=20), legend.text=element_text(size=20), legend.key.height = unit(1.3, 'cm')) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar', limits=c(0,5)) Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.g <- plot_grid(
adipoq, lipe, apod, dcn, ncol=4, rel_widths=c(1, 1, 1, 1.3)
)
g
save_plot("figures/figures_paper/main_figures/Figure_wolfrum/featureplots.pdf", g, base_width=16, base_height=4)EBF2 and LEP
ebf2 <- FeaturePlot(wolfrum, features='EBF2') + NoAxes() + theme(plot.title = element_text(size=20), legend.text=element_text(size=20), legend.key.height = unit(1.3, 'cm')) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar') Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.pparg <- FeaturePlot(wolfrum, features='PPARG') + NoAxes() + theme(plot.title = element_text(size=20), legend.text=element_text(size=20), legend.key.height = unit(1.3, 'cm')) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar') Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.lep <- FeaturePlot(wolfrum, features='LEP') + NoAxes() + theme(plot.title = element_text(size=20), legend.text=element_text(size=20), legend.key.height = unit(1.3, 'cm')) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar') Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.ucp1 <- FeaturePlot(wolfrum, features='UCP1') + NoAxes() + theme(plot.title = element_text(size=20), legend.text=element_text(size=20), legend.key.height = unit(1.3, 'cm')) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar') Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.ebf2vln <- VlnPlot(wolfrum, features='EBF2', group.by='RNA_snn_res.0.8', pt.size=-1) + NoLegend()
lepvln <- VlnPlot(wolfrum, features='LEP', group.by='RNA_snn_res.0.8', pt.size=0.1) + NoLegend()
ucp1vln <- VlnPlot(wolfrum, features='UCP1', group.by='RNA_snn_res.0.8', pt.size=0.1) + NoLegend()
ppargvln <- VlnPlot(wolfrum, features='PPARG', group.by='RNA_snn_res.0.8', pt.size=0.1) + NoLegend()
plot_grid(ebf2vln, lepvln, ucp1vln, ppargvln, ncol=2)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/ebf2.pdf", ebf2, base_width=5, base_height=4)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/ebf2vln.pdf", ebf2vln, base_width=6, base_height=2)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/pparg.pdf", pparg, base_width=5, base_height=4)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/ppargvln.pdf", ppargvln, base_width=6, base_height=2)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/ucp1.pdf", ucp1, base_width=5, base_height=4)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/ucp1vln.pdf", ucp1vln, base_width=6, base_height=2)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/lep.pdf", lep, base_width=5, base_height=4)
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/lepvln.pdf", lepvln, base_width=5, base_height=2)UMAPPlot(wolfrum, label=T, group.by='RNA_snn_res.0.8') 
VlnPlot(wolfrum, features=c('EBF2', 'PPARG'), group.by='RNA_snn_res.0.8', pt.size=-1, ncol=1)
| Version | Author | Date |
|---|---|---|
| 6e008da | Pytrik Folkertsma | 2020-03-23 |
#EBF2 and PPARG
#PPARG: 5, 14, 23
#EBF2: 10, 11, 21, 23
new_labels <- unlist(lapply(wolfrum@meta.data$RNA_snn_res.0.8, function(x){
if (x %in% c(5, 14, 23, 10, 11, 21, 23)){
return(x)
} else {
return(NA)
}
}))
wolfrum@meta.data['labels_clusters_preadipocytes'] <- new_labels
p <- UMAPPlot(wolfrum, group.by='labels_clusters_preadipocytes', label=T) + theme(axis.text = element_blank(), axis.ticks = element_blank(), axis.title = element_blank(), plot.margin=grid::unit(c(0,0,0,0), "mm")) + NoLegend()
p
save_plot("figures/figures_paper/main_figures/Figure_wolfrum/UMAP_adipocyte_clusters2.pdf", p, base_width=6, base_height=5)ebf2 <- FeaturePlot(wolfrum, features='EBF2', pt.size=0.5) + NoLegend() + NoAxes() + theme(plot.title = element_text(size=20)) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar', limits=c(0,5))Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.pparg <- FeaturePlot(wolfrum, features='PPARG', pt.size=0.5) + NoAxes() + theme(plot.title = element_text(size=20), legend.text=element_text(size=15), legend.key.height = unit(1.3, 'cm')) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar', limits=c(0,5)) Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.g <- plot_grid(
ebf2, pparg, ncol=2, rel_widths = c(1, 1.3)
)
g
save_plot("figures/figures_paper/main_figures/Figure_wolfrum/EBF2_PPARG_ptsize0.5.pdf", g, base_width=8.5, base_height=4)pparg <- FeaturePlot(data_180831, features='PPARG', pt.size=0.5) + NoLegend() + NoAxes() + theme(plot.title = element_text(size=20)) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar', limits=c(0,2.5))Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.ebf2 <- FeaturePlot(data_180831, features='EBF2', pt.size=0.5) + NoAxes() + theme(plot.title = element_text(size=20), legend.text=element_text(size=15), legend.key.height = unit(1.3, 'cm')) + scale_color_gradient(name='Expression', low='gray', high='blue', guide='colorbar', limits=c(0,2.5)) Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.g <- plot_grid(
pparg, ebf2, ncol=2, rel_widths = c(1, 1.4)
)
g
| Version | Author | Date |
|---|---|---|
| 6e008da | Pytrik Folkertsma | 2020-03-23 |
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/EBF2_PPARG_no-axes.pdf", g, base_width=8.5, base_height=4)data.180831.brown <- subset(data_180831, cells=rownames(data_180831@meta.data)[data_180831@meta.data$type == 'brown'])colors.states.labels <- c(
P='#ecdd83',
L='#93c8bc',
U='#e27268')
ebf2_vln_brown <- VlnPlot(data.180831.brown, features='EBF2', group.by='State.labels', pt.size=-1, cols=colors.states.labels) + NoLegend() + theme(axis.title = element_blank(), axis.text.x = element_text(angle = 0, size=15), plot.title = element_text(size=20), axis.text.y = element_text(size=15), plot.margin = unit(c(1.5,1.5,1.5,1.5), "lines")) + scale_y_continuous(breaks=c(0, 1, 2))Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.pparg_vln_brown <- VlnPlot(data.180831.brown, features='PPARG', group.by='State.labels', pt.size=-1, cols=colors.states.labels) + NoLegend() + theme(axis.title = element_blank(), axis.text.x = element_text(angle = 0, size=15), plot.title = element_text(size=20), axis.text.y = element_text(size=15), plot.margin = unit(c(1.5,1.5,1.5,1.5), "lines")) + scale_y_continuous(breaks=c(0, 1, 2))Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.g <- plot_grid(
pparg_vln_brown, ebf2_vln_brown, ncol=2
)
g
#save_plot("../figures/figures_paper/main_figures/Figure_wolfrum/EBF2_PPARG_brown_violinplots.pdf", g, base_width=8.5, base_height=3)plots <- VlnPlot(wolfrum, features=c('ADIPOQ', 'LIPE', 'PLIN4', 'FABP4', 'ADIRF', 'APOD', 'MGP', 'DCN', 'CCDC80', 'PLAC9'), group.by='RNA_snn_res.0.8', pt.size=-1, combine=F)
for (i in 1:length(plots)){
if (i == length(plots)){
plots[[i]] <- plots[[i]] + NoLegend() +
theme(plot.title=element_blank(),
axis.title.y=element_blank(),
axis.line.x=element_blank(),
axis.text.x=element_text(angle=0, size=12),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
labs(x='Cluster')
} else {
plots[[i]] <- plots[[i]] + NoLegend() +
theme(plot.title=element_blank(),
axis.title.y=element_blank(),
axis.line.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.x=element_blank(),
axis.title.x=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm"))
}
}
vlnplts <- plot_grid(plotlist=plots, ncol=1, rel_heights=c(1,1,1,1,1,1,1,1,1,1.6))
vlnplts
| Version | Author | Date |
|---|---|---|
| 6e008da | Pytrik Folkertsma | 2020-03-23 |
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Storage
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.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] splines stats4 parallel stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] DT_0.12 kableExtra_1.1.0 knitr_1.28
[4] tidyr_1.0.2 dplyr_0.8.5 cowplot_1.0.0
[7] monocle_2.10.1 DDRTree_0.1.5 irlba_2.3.3
[10] VGAM_1.1-2 ggplot2_3.3.0 Biobase_2.42.0
[13] BiocGenerics_0.28.0 Matrix_1.2-18 Seurat_3.1.4
[16] workflowr_1.6.0
loaded via a namespace (and not attached):
[1] backports_1.1.5 sn_1.5-5 plyr_1.8.6
[4] igraph_1.2.4.2 lazyeval_0.2.2 crosstalk_1.0.0
[7] densityClust_0.3 listenv_0.8.0 fastICA_1.2-2
[10] TH.data_1.0-10 digest_0.6.25 htmltools_0.4.0
[13] viridis_0.5.1 fansi_0.4.1 gdata_2.18.0
[16] magrittr_1.5 cluster_2.1.0 ROCR_1.0-7
[19] limma_3.38.3 readr_1.3.1 globals_0.12.5
[22] RcppParallel_4.4.4 matrixStats_0.55.0 docopt_0.6.1
[25] sandwich_2.5-1 colorspace_1.4-1 rvest_0.3.5
[28] rappdirs_0.3.1 ggrepel_0.8.2 xfun_0.12
[31] sparsesvd_0.2 crayon_1.3.4 jsonlite_1.6.1
[34] survival_3.1-11 zoo_1.8-7 ape_5.3
[37] glue_1.3.1 gtable_0.3.0 webshot_0.5.2
[40] leiden_0.3.3 future.apply_1.4.0 scales_1.1.0
[43] pheatmap_1.0.12 mvtnorm_1.1-0 bibtex_0.4.2.2
[46] Rcpp_1.0.3 metap_1.3 plotrix_3.7-7
[49] xtable_1.8-4 viridisLite_0.3.0 reticulate_1.14
[52] rsvd_1.0.3 tsne_0.1-3 htmlwidgets_1.5.1
[55] httr_1.4.1 FNN_1.1.3 gplots_3.0.3
[58] RColorBrewer_1.1-2 TFisher_0.2.0 ica_1.0-2
[61] farver_2.0.3 pkgconfig_2.0.3 uwot_0.1.5
[64] utf8_1.1.4 labeling_0.3 tidyselect_1.0.0
[67] rlang_0.4.5 reshape2_1.4.3 later_1.0.0
[70] munsell_0.5.0 tools_3.5.3 cli_2.0.2
[73] ggridges_0.5.2 fastmap_1.0.1 evaluate_0.14
[76] stringr_1.4.0 yaml_2.2.1 npsurv_0.4-0
[79] fs_1.3.2 fitdistrplus_1.0-14 caTools_1.17.1.2
[82] purrr_0.3.3 RANN_2.6.1 pbapply_1.4-2
[85] future_1.16.0 nlme_3.1-140 mime_0.9
[88] whisker_0.4 slam_0.1-47 xml2_1.2.2
[91] rstudioapi_0.11 compiler_3.5.3 plotly_4.9.2
[94] png_0.1-7 lsei_1.2-0 tibble_2.1.3
[97] stringi_1.4.6 highr_0.8 lattice_0.20-38
[100] HSMMSingleCell_1.2.0 multtest_2.38.0 vctrs_0.2.4
[103] mutoss_0.1-12 pillar_1.4.3 lifecycle_0.2.0
[106] combinat_0.0-8 Rdpack_0.11-1 lmtest_0.9-37
[109] RcppAnnoy_0.0.15 data.table_1.12.8 bitops_1.0-6
[112] gbRd_0.4-11 httpuv_1.5.2 patchwork_1.0.0.9000
[115] R6_2.4.1 promises_1.1.0 KernSmooth_2.23-15
[118] gridExtra_2.3 codetools_0.2-16 MASS_7.3-51.4
[121] gtools_3.8.1 assertthat_0.2.1 rprojroot_1.3-2
[124] withr_2.1.2 qlcMatrix_0.9.7 sctransform_0.2.1
[127] mnormt_1.5-6 multcomp_1.4-12 hms_0.5.3
[130] grid_3.5.3 rmarkdown_2.1 Rtsne_0.15
[133] git2r_0.26.1 shiny_1.4.0 numDeriv_2016.8-1.1