# PINK1 shows LRRK2, Parkin, and SNCA as part of the Parkinson’s network.

# Authors
Gabriela Novak, Dimitrios Kyriakis, Kamil Grzyb, Michela Bernini, Alexander Skupin

# Google Docs
https://drive.google.com/drive/folders/1xoEtZF-J95cBWNCMLy2bk0pyJo4HLyX_?usp=sharing

# Abstract

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder, yet there is no treatment that can prevent or slow its progression. The mechanisms leading to PD pathology are not well understood, but we can gain insight by studying mutations known to cause PD. We used iPSCs carrying a homozygous mutation (ILE368ASN) within the PINK1 (PARK6) gene to generate midbrain dopaminergic (mDA) neurons, the primary targets of PD. Pairwise comparison between three independent pairs of a PINK1 and a control cell line, using single cell RNA sequencing, identified 151 genes consistently dysregulated at three different timepoints of dopaminergic differentiation. Upon examination, many of these genes formed a network which not only includes genes directly interacting with PINK1-related pathways like Parkin, but also genes that link to several additional PD-related pathways, including LRRK2, DJ-1 and α-synuclein. This suggests that pathology resulting from other PD mutations converges on a common PD network.

<!-- ![Figure1](Figures/Figure1.jpg) -->
<img src="Figures/Figure1.jpg" width=50%>

**Figure 1:** Experimental design.

![Figure2](Figures/Figure2.jpg)
**Figure 2:** Generation and classification of iPS cell lines.
**a)**. Immunocytochemistry. Staining for the iPSC markers Oct3/4 and TRA-180. DAPI was used to stain cell nuclei as a reference. 
**b)**. Results of Scorecard analysis of iPSCs and embryonic bodies (EBs). 
iPSCs are expected to show high expression of self-renewal genes (Self-renew +) and low mesoderm, ectoderm and endoderm marker expression (Ecto -, Meso -, Endo -).
EBs are cells at an early stage of spontaneous differentiation. Scorecard analysis of EBs determines the iPSC cell line’s potential to differentiate into the three germ layers: ectoderm, mesoderm, and endoderm. EBs are expected to express few or no self-renewal genes (Self-renew -) and to show expression of some mesoderm, ectoderm and endoderm markers: Ecto +/-, Meso +/-, Endo +/-.


![Figure3](Figures/Figure3.jpg)
**Figure 3:** iPSC status, differentiation and classification of mDA neurons.
**a)**. Heatmap of the top 15 differential expressed genes per time-point (adjusted p-value<0.01 and fold change >0.1) across the different time points of the control data that examined (IPSCs,Day06, Day15 and Day21).  **b)**. Expression of stemness markers: SOX2, MYC (c-Myc), POU5F1 (Oct4), and NANOG, and mDA-specific differentiation pathways in differentiating neurons (SC and qPCR): Otx2, EN1, Lmx1b, Lmx1a, and Foxa2. SOX2 directs the differentiation of iPSCs into neural progenitors and for maintains the properties of neural progenitor stem cells.


![Figure4](Figures/Figure4.jpg)
**Figure 4:** iPSC status, differentiation and classification of mDA neurons. **a)**. Expression of mDA-specific differentiation pathways in differentiating neurons (SC and qPCR): Otx2, EN1, Lmx1b, Lmx1a, and Foxa2 (also see Table 2). **b)**. Staining for DA marker TH, neuronal marker MAP2.


![Figure5](Figures/Figure5.jpg)
**Figure 5:** **a)** Heatmap of the common differential expressed genes (adjusted p-value<0.01 and fold change >0.1) across the different time points that examined (Day06, Day15 and Day21). Each column is a single cell, and each row is a single gene. The bar on the top shows the experimental origin of cells. **b)** Venn diagrams of the differential expressed genes across time points. **c)** Volcano plot for the pairwise differential expression analysis. For illustration purposes we used 0.6 fold change as threshold to annotate the genes with greater fold change and significant adjusted p-value (adjusted p-value<0.01).


# scRNAseq Analysis

## Libraries 
<details><summary>Code</summary> 
<p>

```r
library(reticulate)
use_python("C:/Users/dimitrios.kyriakis/AppData/Local/Continuum/anaconda3/envs/iscwrapper/python.exe", required = TRUE)
options(future.globals.maxSize= 2122317824)
library(sctransform)
library(Seurat)
ibrary( RColorBrewer)
library(tictoc)
library(crayon)
library(stringr)
library(Routliers)
library(jcolors)
library(cluster)
library(garnett)
library(NMF)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(RColorBrewer)
set.seed(123)
```
</p>
</details>


## Setting Up
<details><summary>Code</summary> 
<p>

```r
tool="seurat"
project ="Michi_Data"
dataset <- project
Data_select <- ICSWrapper::data_selection(project)
WORKDIR <- Data_select$WORKDIR
list_of_files <- Data_select$list_of_files
condition_names <- Data_select$condition_names
condition_names <- condition_names[c(1,2,3,4,5,6,8,28,29)]
list_of_files <- list_of_files[c(1,2,3,4,5,6,8,28,29)]
organism<- Data_select$organism
file<- Data_select$file
data_10x<- Data_select$data_10x
setwd(Data_select$WORKDIR)
color_cond <- c( "magenta4", "#007A87",brewer.pal(6,"Dark2")[-1],"#FF5A5F","black")
color_clust <- c(brewer.pal(12,"Paired")[-11],"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6],brewer.pal(8,"Dark2"))
color_cells <- c(brewer.pal(9,"Set1")[-6],"goldenrod4","darkblue","seagreen4")
color_list <- list(condition=color_cond,Cluster=color_clust,Cell_Type=color_cells,State=color_clust)
imputation = FALSE
remove_mt=FALSE
remove_ribsomal=FALSE
n_cores=4
elbow = TRUE
SCT=TRUE
criteria_pass=3
min.cells <- 10
min.features <- 200
```
</p>
</details>


## Preprocessing

The identification of the low quality cells was done separately in each data set. In order to select only the highest quality data, we sorted the cells by the cumulative gene expression. A subset of cells with the highest cumulative expression was considered for the analysis [1]. 
Additional to this filtering, we defined cells as low-quality, based on three criteria for each cell. The number of the genes that expressed is more than 200 and 2 median-absolute- deviations (MADs) above the median, the total number of counts is 2 MADs above or below the median and the percentage of counts to mitochondrial genes is 1.5 median-absolute- deviations (MADs) above the median. Cells failing at least one criteria were considered as low quality cells and filtered out from further analysis. Similar to the cell filtering, we filtered out the low quality genes that been expressed in less than 10 cells in the data. 


<details><summary>Code</summary> 
<p>

```r
NewDir <- paste0(Sys.Date(),"_",tool,"_elbow_",elbow,"_Mito-",remove_mt,"_Ribo-",remove_ribsomal,"_SCT-",SCT,"_criteria_pass-",criteria_pass)
dir.create(NewDir)
setwd(NewDir)
dir.create("QC")
setwd("QC")
Return_fun <- ICSWrapper::create_cds2(list_of_files=list_of_files,
                                    condition_names=condition_names,
                                    min.features =min.features,min.cells=min.cells,
                                    remove_mt=remove_mt,data_10x=data_10x,
                                    elbow = elbow,tool=tool,n_cores=1,SCT=SCT,
                                    criteria_pass = criteria_pass,vars.to.regress=c("nCount_RNA"))
Combined  <- Return_fun$Combined
Data_List <- Return_fun$Data_List
setwd("../")
```

</p>
</details>


![SupFig1](Figures/SupFig1.jpg)
**Supl.Figure1:** Quality control Plots of control sample Day 06. a) Cumulative Total number of counts. The red vertical lines represent the down and upper bound of the expected elbow. The blue dot represent the transitional point calculated using ecp r package. b)  Histograms of the three criteria that used for low quality cell filtering. c,d) Violin plots of the three criteria. c) Cell score before filtering. Red dots are the cells that filtered after the quality control. d) The overview of the three criteria after filtering step.  



## Data Intergration

The integration of the filtered matrices of the different datasets was performed using scTransform [2] on a Seurat object [3] based on the treatment. The final gene expression matrix which used for the downstream analysis, consist of 4495 cells and 39194 genes. Principal component analysis (PCA) was computed using the 5000 most variable genes on the integrated data. 


<details><summary>Code</summary> 
<p>

```r
dir.create("Aligned_Cond_RegPhase")
setwd("Aligned_Cond_RegPhase")
DefaultAssay(Combined) <- "RNA"
Combined$condition <- factor(as.factor(Combined$condition), levels = c("Control_IPSCs", "Control_D06"  ,"Control_D10",   "Control_D15",   "Control_D21",
"PINK1_IPSCs","PINK1_D06",     "PINK1_D15",     "PINK1_D21"))
Combined$Treatment <-as.vector(Combined$condition)
Combined$Treatment[grep("Control",Combined$Treatment)] <- "Control"
Combined$Treatment[grep("PINK",Combined$Treatment)] <- "PINK"
pink.list <-SplitObject(Combined,split.by = "Treatment")
for (i in 1:length(pink.list)) {
    pink.list[[i]] <- SCTransform(pink.list[[i]], verbose = FALSE,vars.to.regress=c("G2M.Score","S.Score"))
}
int.features <- SelectIntegrationFeatures(object.list = pink.list, nfeatures = 3000)
pink.list <- PrepSCTIntegration(object.list = pink.list, anchor.features = int.features,
                                    verbose = FALSE)
int.anchors <- FindIntegrationAnchors(object.list = pink.list, normalization.method = "SCT",
                                            anchor.features = int.features, verbose = FALSE)
Seurat.combined <- IntegrateData(anchorset = int.anchors, normalization.method = "SCT",
                                    verbose = FALSE)
DefaultAssay(object = Seurat.combined) <- "integrated"
Combined <- Seurat.combined
setwd("../")
```

</p>
</details>



## Clustering

The clustering of data was performed using Louvain clustering. The resolution of the clustering was selected based on the best silhouette score of the different resolutions [4].



<details><summary>Code</summary> 
<p>

```r
dir.create("Clusters")
setwd("Clusters")
Combined <- ICSWrapper::reduce_dim(Combined,project=project,assay = "SCT")$Combined#,resolution=c(0.1))$Combined
# ====== Reorder Conditions
Combined$condition <- factor(as.factor(Combined$condition), levels = c("Control_IPSCs", "Control_D06"  ,"Control_D10",   "Control_D15",   "Control_D21",
"PINK1_IPSCs","PINK1_D06",     "PINK1_D15",     "PINK1_D21"))
# ====== PLot
pdf(paste(Sys.Date(),project,"tsne","projection.pdf",sep="_"))
ICSWrapper::plot_cells(Combined,target="condition",leg_pos="right",save=FALSE,ncol=1,color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Cluster",leg_pos="right",save=FALSE,ncol=1,color_list = color_list)
dev.off()
# Quality Plots
ICSWrapper::plot_nFeatures(Combined,title="",save=TRUE,tiff=FALSE,reduce="t-SNE",p3D=FALSE)
ICSWrapper::plot_tot_mRNA(Combined,title="",save=TRUE,tiff=FALSE,reduce="t-SNE",p3D=FALSE)
if(tolower(tool)=="seurat" & elbow){
    p3 <- DimPlot(object = Combined, reduction = "umap", group.by = "condition",cols = color_cond)
    p4 <- DimPlot(object = Combined, reduction = "umap", label = TRUE,cols = color_clust)
    pdf(paste(Sys.Date(),project,"umap","Seurat.pdf",sep="_"))
    print(p3)
    print(p4)
    dev.off()
}
setwd("../")
saveRDS(Combined,paste0("Clustered_",NewDir,".rds"))
# Sum up Plots
pdf(paste(Sys.Date(),project,"_projection_Aligned_Treatment.pdf",sep="_"))
ICSWrapper::plot_cells(Combined,target="condition",leg_pos="right",save=FALSE,ncol=1,reduction="umap",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Cluster",leg_pos="right",save=FALSE,ncol=1,reduction="umap",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Phase",leg_pos="right",save=FALSE,ncol=1,reduction="umap",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="condition",leg_pos="right",save=FALSE,ncol=1,reduction="tsne",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Cluster",leg_pos="right",save=FALSE,ncol=1,reduction="tsne",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Phase",leg_pos="right",save=FALSE,ncol=1,reduction="tsne",color_list = color_list)
dev.off()
# ---------------------------------------------------------------------------------------
res<-ICSWrapper::scatter_gene(Combined,features = c("nCount_RNA","nFeature_RNA","percent.mito","percent.rb"),size=0.9)
pdf("Combined_QC.pdf")
print(res)
# Free Space
dev.off()
Return_fun <- NULL
Seurat.combined <- NULL
pink.list <- NULL
#save.image("IPSCs_PINK.RData")
```
</p>
</details>


  
## Pairwise Differential Expression

<details><summary>Code</summary> 
<p>

```r
# =============================== PAIRWISE DF ===============================================
dir.create("DF_Pairwise_PAPER")
setwd("DF_Pairwise_PAPER")
library(EnhancedVolcano)
Combined$condition <- as.factor(Combined$condition)
Idents(Combined) <- as.factor(Combined$condition)
cl_combinations <- combn(levels(Combined$condition),2)
cl_combinations <- cl_combinations[,c(5,13,25,30)]
DefaultAssay(Combined) <- "RNA"
Combined <- NormalizeData(Combined)
Combined <- ScaleData(Combined,rownames(Combined@assays$RNA@counts))
library(parallel)
pairwise_df <- function (comb,object,cl_combinations){
    DefaultAssay(object) <- "RNA"
    title <- paste(cl_combinations[,comb],collapse = "_")
    dir.create(title)
    setwd(title)
    target <- "condition"
    idents <- as.vector(cl_combinations[,comb])
    ident.1 <- idents[1]
    print(ident.1)
    ident.2 <- idents[2]
    pbmc.markers <- FindMarkers(object = object,
                                    ident.1 = ident.1,
                                    ident.2 =ident.2,
                                assay ="RNA",min.pct =0.1,
                                logfc.threshold=0.0,
                                only.pos = FALSE,
                                test.use = "MAST",latent.vars = c("nCount_RNA"))
    pbmc.markers$gene <- rownames(pbmc.markers)
    qvalue <- p.adjust(pbmc.markers$p_val, method = "BH",n=dim(object@assays$RNA@counts)[1])
    pbmc.markers$qvalue <- qvalue
    top <- pbmc.markers[pbmc.markers$p_val_adj<0.05,]
    to_fc <- top[order(abs(top$avg_logFC),decreasing = TRUE),]
    to_fc_gene <- rownames(to_fc)[1:50]
    #top10 <- top %>% top_n(n = 50, wt = abs(avg_logFC))
    #top10_genes<- rownames(top10)
    temp <- object[,object$condition%in%c(ident.1,ident.2)]
    temp$condition <- as.factor(as.vector(temp$condition))
    # debugonce(annotated_heat)
    pdf("Volcano.pdf")
    plot(EnhancedVolcano(pbmc.markers,
                    lab = pbmc.markers$gene,
                    x = 'avg_logFC',
                    y = 'p_val_adj',subtitle = paste(ident.1,"vs",ident.2,"(FCcutoff=0.6)"),
                    xlim = c(-2, 2),FCcutoff = 0.6))
    dev.off()
    ICSWrapper::annotated_heat(object=temp,
                row_annotation=c(1),
                gene_list=to_fc_gene,
                Rowv=TRUE,
                gene_list_name="DF_genes",
                title=title,
                ordering="condition",One_annot = TRUE)
    DefaultAssay(temp) <- "integrated"
    write.table(pbmc.markers, file = paste0(Sys.Date(),"_TO_EXP_each_",target,"_",title,".tsv"),row.names=FALSE, na="", sep="\t")
    setwd("../")
}
# Apply DF
mclapply(c(1:dim(cl_combinations)[2]),FUN=pairwise_df,object=Combined,cl_combinations=cl_combinations,mc.cores=1)
```
</p>
</details>



## Intersection of DF genes

<details><summary>Code</summary> 
<p>

```r
library("viridis")
library("ggpubr")
library("gridExtra")
library("VennDiagram")
library("RColorBrewer")
myCol <- brewer.pal(3, "Pastel2")

dirs_pairs <- list.dirs("C:/Users/dimitrios.kyriakis/Desktop/PhD/Projects/Michi_Data/Paper_fig_test/2020-05-27_seurat_elbow_TRUE_Mito-FALSE_Ribo-FALSE_SCT-TRUE_criteria_pass-3/DF_Pairwise_PAPER",full.names = TRUE )[-1]
dirs_pairs <- grep('IPSC|D06.*D06|D15.*D15|D21.*D21',dirs_pairs,value = TRUE)
dirs_pairs <- dirs_pairs[-4]
df_return_nt_cntrl <- list()
df_return_nt_pink <- list()
df_return_nt_all <- list()
# ===== Iterate through different DF files
for (iter in 1:length(dirs_pairs)){
    dirs_iter <- dirs_pairs[iter]
    file <- paste0(dirs_iter ,"/", dir(dirs_iter, "*.tsv"))
    l1 <- read.table(file,header=TRUE)
    l1$cluster <- l1$avg_logFC
    l1$cluster[ l1$avg_logFC<0] <- "PINK"
    l1$cluster[ l1$avg_logFC>0] <- "Control"
    ctrl_l1 <- l1[grep("Control",l1$cluster),]
    pink_l1 <- l1[grep("PINK",l1$cluster),]
    all_l1 <-  l1
    df_return_nt_cntrl[[iter]] <- as.vector(ctrl_l1[ctrl_l1$p_val_adj<0.01 & abs(ctrl_l1$avg_logFC) >0.4,"gene"])
    df_return_nt_pink[[iter]] <- as.vector(pink_l1[pink_l1$p_val_adj<0.01 & abs(pink_l1$avg_logFC) >0.4,"gene"])
    print(length(df_return_nt_cntrl[[iter]]))
    print(length(df_return_nt_pink[[iter]]))
    df_return_nt_all[[iter]] <- c(df_return_nt_cntrl[[iter]] ,df_return_nt_pink[[iter]])
}
# # ============= Intersect Common Genes
cntrl_intesect <- Reduce(intersect, df_return_nt_cntrl)
print(cntrl_intesect)
pink_intesect <- Reduce(intersect, df_return_nt_pink)
print(pink_intesect)
length(cntrl_intesect)
length(pink_intesect)
# ==== PLOT VENN
pdf("Control_venn_diagramm.pdf")
day06 <- c(df_return_nt_cntrl[[1]])
day15 <- c(df_return_nt_cntrl[[2]])
day21 <- c(df_return_nt_cntrl[[3]])
# Generate plot
v <- venn.diagram(list(Day06=day06, Day15=day15,Day21=day21),
                fill = myCol,
                alpha = c(0.5, 0.5, 0.5), cat.cex = 1.5, cex=1.5,
                filename=NULL)
# have a look at the default plot
grid.newpage()
grid.draw(v)
# have a look at the names in the plot object v
lapply(v,  names)
# We are interested in the labels
lapply(v, function(i) i$label)
v[[11]]$label <- paste(intersect(intersect(day06, day15),day21), collapse="\n")  
# plot  
grid.newpage()
grid.draw(v)
dev.off()
# ======= PINK VENN
pdf("PINK_venn_diagramm.pdf")
day06 <- c(df_return_nt_pink[[1]])
day15 <- c(df_return_nt_pink[[2]])
day21 <- c(df_return_nt_pink[[3]])
# Generate plot
v <- venn.diagram(list(Day06=day06, Day15=day15,Day21=day21),
                fill = myCol,
                alpha = c(0.5, 0.5, 0.5), cat.cex = 1.5, cex=1.5,
                filename=NULL)
# have a look at the default plot
grid.newpage()
grid.draw(v)
# have a look at the names in the plot object v
lapply(v,  names)
# We are interested in the labels
lapply(v, function(i) i$label)
v[[11]]$label <- paste(intersect(intersect(day06, day15),day21), collapse="\n")  
# plot  
grid.newpage()
grid.draw(v)
dev.off()
setwd("../")
# ----------------------------------------------------------------------------------------------
```
</p>
</details>



## Correlation Network

<details><summary>Code</summary> 
<p>

```r

Control<-subset(Combined,subset = Treatment=="Control")
PINK1<-subset(Combined,Treatment=="PINK") 

dataset <- as.data.frame(Combined@assays$SCT@data)

graph_annotation <- read.csv("NODES run 13.csv")
first_graph <- read.csv("g3.csv")
f_g_genes <- unique(c(as.vector(first_graph$Source),as.vector(first_graph$Target))) 
f_g_genes <- toupper(f_g_genes)
f_g_genes <- gsub("-", ".", f_g_genes, fixed = TRUE)
# f_g_genes<-c(f_g_genes,"PRKN","ADGRG7")
r_f_g_genes <- f_g_genes[f_g_genes%in% rownames(dataset)]
cat(paste("Genes not in dataset:",
length(f_g_genes)-length(r_f_g_genes)))
setdiff(f_g_genes, r_f_g_genes)
r_first_graph <- first_graph[first_graph$Source %in% r_f_g_genes,]
r2_first_graph <- r_first_graph[r_first_graph$Target %in% r_f_g_genes,]
dim(first_graph)
dim(r2_first_graph)
dataset_r <-dataset[r_f_g_genes,]
retur_graph <- ics_net(dataset = dataset_r , method = "correlation",threshold = 0.3)

g3 <- graph_from_data_frame(r2_first_graph,directed = FALSE)
common <- retur_graph$graph %s% g3
common_S <-  graph_from_data_frame(as_edgelist(igraph::simplify(common), names = TRUE),directed = FALSE)

pdf("f3_Common_net.pdf")
set.seed(123)
plot(common_S)
dev.off()
```
</p>
</details>


![Network_DE_only](Figures/Figure6.jpg)
**Figure ?:** **a)** 291 differentialy expressed (DE) genes. connections from STRING and GENEMANIA, output limited to interactions among DE genes. **b)** All 291 genes. **c)** Correlation anlysis. The correlation network of the differentially expressed genes constructed based on the normalized counts. Two genes were linked if the correlation of the gene expressions was greater than 0.1 and the p-values less than 0.05. **d)** Then we overlaid the correlation network with the network construced by Genemania-String. We illustrate the common edges-interactions of these two networks.    



##
Diffence from the common f
PINK and COntrold and all
Ubitiquination
Mittocondrial


## Session Info

<details><summary>Code</summary> 
<p>

```r


R version 3.6.2 (2019-12-12)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
 [1] grid      stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gridBase_0.4-7              VennDiagram_1.6.20          futile.logger_1.4.3        
 [4] gridExtra_2.3               viridis_0.5.1               viridisLite_0.3.0          
 [7] forcats_0.5.0               dplyr_0.8.5                 purrr_0.3.3                
[10] readr_1.3.1                 tidyr_1.0.2                 tibble_3.0.0               
[13] tidyverse_1.3.0             cowplot_1.0.0               ggpubr_0.2.5               
[16] magrittr_1.5                ggplot2_3.3.0               NMF_0.22.0                 
[19] rngtools_1.5                pkgmaker_0.31.1             registry_0.5-1             
[22] garnett_0.2.9               monocle3_0.2.1.5            SingleCellExperiment_1.8.0 
[25] SummarizedExperiment_1.16.1 DelayedArray_0.12.3         BiocParallel_1.20.1        
[28] matrixStats_0.56.0          GenomicRanges_1.38.0        GenomeInfoDb_1.22.1        
[31] IRanges_2.20.2              S4Vectors_0.24.4            Biobase_2.46.0             
[34] BiocGenerics_0.32.0         cluster_2.1.0               jcolors_0.0.4              
[37] Routliers_0.0.0.3           stringr_1.4.0               crayon_1.3.4               
[40] tictoc_1.0                  RColorBrewer_1.1-2          Seurat_3.1.5               
[43] sctransform_0.2.1           reticulate_1.15            

loaded via a namespace (and not attached):
  [1] readxl_1.3.1           backports_1.1.6        plyr_1.8.6             igraph_1.2.5          
  [5] lazyeval_0.2.2         splines_3.6.2          listenv_0.8.0          digest_0.6.23         
  [9] foreach_1.5.0          htmltools_0.4.0        gdata_2.18.0           fansi_0.4.1           
 [13] doParallel_1.0.15      ROCR_1.0-7             globals_0.12.5         modelr_0.1.6          
 [17] prettyunits_1.1.1      colorspace_1.4-1       rvest_0.3.5            ggrepel_0.8.2         
 [21] haven_2.2.0            RCurl_1.98-1.1         jsonlite_1.6           graph_1.64.0          
 [25] survival_3.1-11        zoo_1.8-7              iterators_1.0.12       ape_5.3               
 [29] glue_1.4.0             gtable_0.3.0           zlibbioc_1.32.0        XVector_0.26.0        
 [33] leiden_0.3.3           future.apply_1.4.0     abind_1.4-5            scales_1.1.0          
 [37] futile.options_1.0.1   DBI_1.1.0              bibtex_0.4.2.2         Rcpp_1.0.4.6          
 [41] xtable_1.8-4           progress_1.2.2         rsvd_1.0.3             tsne_0.1-3            
 [45] htmlwidgets_1.5.1      httr_1.4.1             gplots_3.0.3           ellipsis_0.3.0        
 [49] ica_1.0-2              farver_2.0.3           pkgconfig_2.0.3        uwot_0.1.8            
 [53] dbplyr_1.4.2           labeling_0.3           tidyselect_1.0.0       rlang_0.4.5           
 [57] reshape2_1.4.3         cellranger_1.1.0       munsell_0.5.0          tools_3.6.2           
 [61] cli_2.0.2              generics_0.0.2         broom_0.5.5            ggridges_0.5.2        
 [65] npsurv_0.4-0           fs_1.4.1               fitdistrplus_1.0-14    caTools_1.18.0        
 [69] RANN_2.6.1             pbapply_1.4-2          future_1.16.0          nlme_3.1-145          
 [73] formatR_1.7            xml2_1.3.1             compiler_3.6.2         rstudioapi_0.11       
 [77] plotly_4.9.2.1         png_0.1-7              lsei_1.2-0             ggsignif_0.6.0        
 [81] reprex_0.3.0           stringi_1.4.6          lattice_0.20-41        Matrix_1.2-18         
 [85] vctrs_0.2.4            pillar_1.4.3           lifecycle_0.2.0        lmtest_0.9-37         
 [89] RcppAnnoy_0.0.16       data.table_1.12.8      bitops_1.0-6           irlba_2.3.3           
 [93] patchwork_1.0.0.9000   R6_2.4.1               KernSmooth_2.23-16     codetools_0.2-16      
 [97] lambda.r_1.2.4         MASS_7.3-51.5          gtools_3.8.2           assertthat_0.2.1      
[101] MAST_1.12.0            withr_2.1.2            GenomeInfoDbData_1.2.2 hms_0.5.3             
[105] ICSWrapper_0.2.2       Rtsne_0.15             lubridate_1.7.8    

```
</p>
</details>