Single cell transcriptomics of human iPSC differentiation dynamics reveal a core network of Parkinson’s disease
Authors
Gabriela Novak, Dimitrios Kyriakis, Kamil Grzyb, Michela Bernini, Sophie Rodius, Gunnar Dittmar, Steven Finkbeiner, and Alexander Skupin
Abstract
Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder, characterized by the loss of dopaminergic neurons (mDA) in the midbrain. The complex underlying mechanisms are only partly understood and there is no treatment able to reverse PD progression. Here, we aim to understand the disease mechanism using mDA neurons obtained through differentiation of human induced pluripotent stem cells (hiPSCs) carrying the ILE368ASN mutation within the PINK1 gene. Single-cell RNA sequencing (RNAseq) and gene expression analysis of a PINK1 and a control cell line identified genes consistently differentially expressed during mDA neuron differentiation. Network analysis revealed that these genes form a core network, members of which interact with all known 19 protein-coding Parkinson’s disease-associated genes and encompasses key PD-associated pathways, including ubiquitination, mitochondrial, protein processing, RNA metabolism, and vesicular transport. Subsequent proteomics analysis showed a consistent alteration in proteins of dopamine metabolism validating the manifestation of the affected neuronal phenotype. Our findings suggest the existence of a network onto which pathways associated with PD pathology converge and offers new interpretation of the phenotypic heterogeneity of PD.
Figure 1:
Experimental design. a. Fibroblasts were used to generate human induced
pluripotent stem cells (iPSCs), which were then used to generate mDA neurons.
Differentiation was initiated concurrently in a PINK1 and a control cell line, at three different
times to obtain cells at different stages of differentiation that could all be collected for SCRNAseq at the same time, thus avoiding batch effects. “P+1” indicates that the iPSCs were
passaged before new differentiation was initiated. Since D10 was not used in pairwise
analysis, we indicated “P+2” between D15 and D6 differentiation initiation. B. Heatmap
illustrating the transitions in gene expression from iPSC markers (MYC, POU5F1-OCT3/4), to
genes associated with mDA differentiation (PTCH1, FZDZ, HES1, OTX2, SLIT1, LMX1A), and
finally to early expression of mature mDA markers (DCX, DDC). This is discussed in more
detail in the text and in Fig. 3 & 4. The gene expression matrix used here consists of 4495
cells (see Methods section).
Figure 2: Classification of iPSC status. a. Immunocytochemistry (ICC). Staining for the iPSC markers OCT3/4 and TRA-1-80 of iPSC colonies, prior to differentiation. DAPI was used to
stain cell nuclei as a reference. b. Expression of genes known to indicate iPSC status (MYC, POU5F1-OCT3/4) and of genes identified by a differential expression analysis between iPSCs and differentiating cells (also see Supplement Fig. 12). TDGF-1 is expressed in iPS cells of high stemness60; L1TD1, USP44, POLR3G, and TERF1 are essential for the maintenance of pluripotency in human stem cells 61–64; IFITM1, DPPA4, and PRDX1 are associated with
stemness 65–67. c. Results of Scorecard analysis of iPSCs and embryonic bodies (EBs). iPSCs
are expected to show high expression of self-renewal genes and low expression of
mesoderm, ectoderm and endoderm markers. EBs are cells at an early stage of spontaneous
differentiation. Scorecard analysis of EBs determines the iPSC line’s potential to
differentiate into the three germ layers, hence, EBs are expected to express few or no selfrenewal genes and to show expression of some mesoderm, ectoderm and endoderm
markers: Ecto +/-, Meso +/-, Endo +/-.
Figure 3. In vitro differentiation of iPSC-derived mDA neurons recapitulates the in vivo
process. a. To illustrate the maturation of neuronal morphology and mDA status,
differentiated neurons were stained at D25 and D35 for a neuronal marker MAP2 (red) and
mDA markers (green): TH, PITX3, LMX1A and DAT. b. Quantitation of mDA markers TH,
ALDH1A1 and LMX1A. using absolute quantitation via qPCR and standardization to a
housekeeping gene. Each timepoint represents three biological replicates, amplified in
duplicate. Standard error (SE) bars are the SE of biological replicates. The expression levels are standardized to total RNA and to the expression of the housekeeping gene GAPDH.
c. Heatmap showing the expression of genes known from literature to be involved and
necessary for mDA neuron differentiation (Supplement Table 3a,b). d. The mDA
differentiation gene expression profile recently published by Ásgrímsdóttir and Arenas
(2020)68 was used to show the progression during differentiation, from iPSCs, to radial glia
(Rgl), to progenitors (Prog) and neuroprogenitors (NProg), to early mDA neurons (DA) For
genes used to determine expression modules see Supplement Table 3b).
Figure 4: Classification of mDA status. a. TH positive neurons co-express mDA markers
PITX3, LMX1A and DAT in control (top) and PINK1 cell line (bottom), at D35. b. Table on the left shows that, based on our SC-RNAseq data, cell lines cluster according to differentiation stage, indicating that gene expression is very homogenous in the control and the PINK1 cell lines, which allows for the detection of even subtle alteration by the presence of the PINK1 mutation. Tables on the right show trajectory of expression of TH and KCNJ6 (GIRK2), two mDA markers. At D21 neurons begin to show TH expression, together with expression of mDA markers, this indicates that they are becoming early postmitotic mDA neurons. Similar observations can also be made from an expression heatmap shown in Supplement Figure
Figure 5: Differentialy expressed genes (DEGs) in a cell line homozygous for a mutation in the PINK1 gene, compared to a control cell line, at three timepoints during the
differentiation of mDA neurons (D6, D15 and D21). a. Heatmap of the top DEGs. Each
column corresponds to a timepoint for either control (left 3) or PINK1 cells (right 3); each
row shows the expression of one gene in individual cells. b. Top DEGs. The minimum fold
change was increased to highlight the top differentially expressed genes. We identified top
56 genes as our group A (Table 2), here we show the top five upregulated genes (left Venn
diagram) and the top three downregulated genes (right Venn diagram). c. Enrichment
analysis performed using the STRING69 database. The top KEGG pathway associated with
this dataset is Parkinson’s disease. Other three KEGG pathways identified were Spliceosome,
Huntington's disease and Thermogenesis, in order of decreasing significance. Details are
listed in Supplement Table 12.
Figure 6. Network analysis. a. Protein-protein interaction network based on known interactions available through the STRING69 and GeneMANIA70 databases. Only strong interactions were retained, predicted interactions or text associations were omitted (see Methods). Betweenness centrality was used to illustrate the relative importance of each node within the network through the level of its connectedness to other proteins. In the larger the circle, the more partners the node is connected to. The colours represent the DEG sets, with the top 56 DEGs (group A) in light blue, group B in purple, group C in dark green and group D in light green. CHCHD2 (pink, part of group B) is a DEG which has recently also been identified as a PARK gene. b. DEGs which play a role in ubiquitination Additional functional pathways are listed in Supplement Fig. 7 and Supplement Table 9. Specific connection to ubiquitination are shown in Supplement Fig.8. c. Based on literature, 68% of the DEGs of this network are already known to be associated with PD (for references see Supplement Table 10). Supplement Fig.9 shows which genes/proteins of the network directly interact with PARK genes through known protein-protein interactions. The topology of all three networks is the same, the different appearance is a result of a separate analysis run, but the connections and size of the nodes remain the same.
Figure 7. Comparative proteomics analysis between CTRL and Pink1 cell line at D25 and
D40 validates the manifestation of the transcriptional phenotype. Results of proteomic
analysis at D25 and D40 of the differentiation protocol. a. Protein levels of protein that were detected as both top differentially abundant at the protein level by the proteomics analysis, and by SC-RNAseq, as differentially expressed at the mRNA level. The data shows results at two timepoints, D25 and D40, in two biological replicates per timepoint. b. This figure shows a network of proteins differentially expressed between a control and a PINK1 mutationcarrying cell line, at D25 and D40. Proteins which are differentially expressed at both D25 and D40 are highlighted in green and point to a dysfunction of the dopaminergic system. D25 differentially abundant proteins are in purple, D40 in blue, proteins also identified as by SC-RNAseq differentially expressed at the mRNA level are in pink. For a table of proteins see
Supplement Table 13.
scRNAseq Analysis
Libraries
Code
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)
library( 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)
Setting Up
Code
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$WORKDIR)
color_cond <- c( "magenta4", "#007A87",brewer.pal(6,"Dark2")[-1],"#FF5A5F","black")
color_clust <- c(brewer.pal(12,"Paired")[-11],"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6],brewer.pal(8,"Dark2"))
color_cells <- c(brewer.pal(9,"Set1")[-6],"goldenrod4","darkblue","seagreen4")
color_list <- list(condition=color_cond,Cluster=color_clust,Cell_Type=color_cells,State=color_clust)
imputation = FALSE
remove_mt=FALSE
remove_ribsomal=FALSE
n_cores=4
elbow = TRUE
SCT=TRUE
criteria_pass=3
min.cells <- 10
min.features <- 200
Preprocessing
The identification of the low quality cells was done separately in each data set. In order to select only the highest quality data, we sorted the cells by the cumulative gene expression. A subset of cells with the highest cumulative expression was considered for the analysis [1].
Additional to this filtering, we defined cells as low-quality, based on three criteria for each cell. The number of the genes that expressed is more than 200 and 2 median-absolute- deviations (MADs) above the median, the total number of counts is 2 MADs above or below the median and the percentage of counts to mitochondrial genes is 1.5 median-absolute- deviations (MADs) above the median. Cells failing at least one criteria were considered as low quality cells and filtered out from further analysis. Similar to the cell filtering, we filtered out the low quality genes that been expressed in less than 10 cells in the data.
Code
r
NewDir <- paste0(Sys.Date(),"_",tool,"_elbow_",elbow,"_Mito-",remove_mt,"_Ribo-",remove_ribsomal,"_SCT-",SCT,"_criteria_pass-",criteria_pass)
dir.create(NewDir)
setwd(NewDir)
dir.create("QC")
setwd("QC")
Return_fun <- ICSWrapper::create_cds2(list_of_files=list_of_files,
condition_names=condition_names,
min.features =min.features,min.cells=min.cells,
remove_mt=remove_mt,data_10x=data_10x,
elbow = elbow,tool=tool,n_cores=1,SCT=SCT,
criteria_pass = criteria_pass,vars.to.regress=c("nCount_RNA"))
Combined <- Return_fun$Combined
Data_List <- Return_fun$Data_List
setwd("../")
Supl.Figure1: Quality control Plots of control sample Day 06. a) Cumulative Total number of counts. The red vertical lines represent the down and upper bound of the expected elbow. The blue dot represent the transitional point calculated using ecp r package. b) Histograms of the three criteria that used for low quality cell filtering. c,d) Violin plots of the three criteria. c) Cell score before filtering. Red dots are the cells that filtered after the quality control. d) The overview of the three criteria after filtering step.
Data Intergration
The integration of the filtered matrices of the different datasets was performed using scTransform [2] on a Seurat object [3] based on the treatment. The final gene expression matrix which used for the downstream analysis, consist of 4495 cells and 39194 genes. Principal component analysis (PCA) was computed using the 5000 most variable genes on the integrated data.
Code
r
dir.create("Aligned_Cond_RegPhase")
setwd("Aligned_Cond_RegPhase")
DefaultAssay(Combined) <- "RNA"
Combined$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$Treatment)] <- "PINK"
pink.list <-SplitObject(Combined,split.by = "Treatment")
for (i in 1:length(pink.list)) {
pink.list[[i]] <- SCTransform(pink.list[[i]], verbose = FALSE,vars.to.regress=c("G2M.Score","S.Score"))
}
int.features <- SelectIntegrationFeatures(object.list = pink.list, nfeatures = 3000)
pink.list <- PrepSCTIntegration(object.list = pink.list, anchor.features = int.features,
verbose = FALSE)
int.anchors <- FindIntegrationAnchors(object.list = pink.list, normalization.method = "SCT",
anchor.features = int.features, verbose = FALSE)
Seurat.combined <- IntegrateData(anchorset = int.anchors, normalization.method = "SCT",
verbose = FALSE)
DefaultAssay(object = Seurat.combined) <- "integrated"
Combined <- Seurat.combined
setwd("../")
Clustering
The clustering of data was performed using Louvain clustering. The resolution of the clustering was selected based on the best silhouette score of the different resolutions [4].
Code
r
dir.create("Clusters")
setwd("Clusters")
Combined <- ICSWrapper::reduce_dim(Combined,project=project,assay = "SCT")$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")
Pairwise Differential Expression
Code
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)
Intersection of DF genes
Code
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("../")
# ----------------------------------------------------------------------------------------------
Correlation Network
Code
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()
Session Info
Code
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