README.md 21.7 KB
Newer Older
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
1
# PINK1 shows LRRK2, Parkin, and SNCA as part of the Parkinson’s network.
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
2

Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
3
4
5
6
7

# 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.

Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
8
9
<!-- ![Figure1](Figures/Figure1.jpg) -->
<img src="Figures/Figure1.jpg" width=50%>
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
10

Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
11
**Figure 1:** Experimental design.
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
12
13

![Figure2](Figures/Figure2.jpg)
Dimitrios Kyriakis's avatar
Figure1    
Dimitrios Kyriakis committed
14
**Figure 2:** Generation and classification of iPS cell lines.
Dimitrios Kyriakis's avatar
Bold    
Dimitrios Kyriakis committed
15
16
**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). 
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
17
18
19
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 +/-.

Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
20

Dimitrios Kyriakis's avatar
figure3    
Dimitrios Kyriakis committed
21
![Figure3](Figures/Figure3.jpg)
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
22
**Figure 3:** iPSC status, differentiation and classification of mDA neurons.
Dimitrios Kyriakis's avatar
Bold    
Dimitrios Kyriakis committed
23
**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.
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
24
25
26


![Figure4](Figures/Figure4.jpg)
Dimitrios Kyriakis's avatar
Bold    
Dimitrios Kyriakis committed
27
**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.
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
28
29
30


![Figure5](Figures/Figure5.jpg)
Dimitrios Kyriakis's avatar
Bold    
Dimitrios Kyriakis committed
31
**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).
Dimitrios Kyriakis's avatar
Figure3    
Dimitrios Kyriakis committed
32
33
34
35
36


# scRNAseq Analysis

## Libraries 
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
<details>
  <summary>Code</summary>
    ```{r libraries, include=FALSE}
    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)
    set.seed(123)
    ```
</details>

Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
61

Dimitrios Kyriakis's avatar
Figure3    
Dimitrios Kyriakis committed
62
## Setting Up
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
63
64
<details>
  <summary>Code</summary>  
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
65
    ```{r setup}
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
    # ================================ SETTING UP ======================================== #
    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)
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
84
    # ========= Parameters
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
85
86
87
88
89
90
91
92
93
    imputation = FALSE
    remove_mt=FALSE
    remove_ribsomal=FALSE
    n_cores=4
    elbow = TRUE
    SCT=TRUE
    criteria_pass=3
    min.cells <- 10
    min.features <- 200
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
94
    ```
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
95
</details>
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
96

Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
97

Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
98
99
100
101
102
## 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. 

Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
103

Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
<details>
  <summary>Code</summary>  
    ```{r readfiles}
    # ======== Perform an integrated analysis ====
    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("../")
    ```
</details>
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
124

Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
125

Dimitrios Kyriakis's avatar
supfig    
Dimitrios Kyriakis committed
126
127
128
![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.  

Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
129
130
131
132
133


## 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. 
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
134
135


Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
<details>
  <summary>Code</summary> 
    ```{r remapping}
    dir.create("Aligned_Cond_RegPhase")
    setwd("Aligned_Cond_RegPhase")
    # ================================== ALLIGN CONDITIONS =========================================
    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"))
    }
    # doi: https://doi.org/10.1101/576827
    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("../")
    ```
</details>
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
165
166
167
168
169
170



## 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].
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
171
172


Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
173
174
175
176
177
178
179
<details>
  <summary>Code</summary> 
    ```{r Clustering}
    # ================================== Clustering =========================================
    dir.create("Clusters")
    setwd("Clusters")
    Combined <- ICSWrapper::reduce_dim(Combined,project=project,assay = "SCT")$Combined#,resolution=c(0.1))$Combined
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
180
    # ====== Reorder Conditions
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
181
182
    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"))
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
183
    # ====== PLot
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
184
185
186
187
    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()
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
188
    # Quality Plots
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
189
190
191
192
193
194
195
196
197
198
199
200
    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"))
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
201
    # Sum up Plots
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
202
203
204
205
206
207
208
    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)
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
209
    dev.off()
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
210
211
212
213
    # ---------------------------------------------------------------------------------------
    res<-ICSWrapper::scatter_gene(Combined,features = c("nCount_RNA","nFeature_RNA","percent.mito","percent.rb"),size=0.9)
    pdf("Combined_QC.pdf")
    print(res)
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
214
    # Free Space
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
215
216
217
218
219
220
221
    dev.off()
    Return_fun <- NULL
    Seurat.combined <- NULL
    pink.list <- NULL
    #save.image("IPSCs_PINK.RData")
    ```
</details>
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
222
223
224



Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
225
## iPSCs Differentiation
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
226

Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
227
A short list of manually curated markers was used in order to validate the different stages of the differentiation process. 
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
228

Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
<details>
  <summary>Code</summary> 
    ```{r  Developmental_Markers}
    # ================================== Developmental Stages =========================================
    dir.create("Developmental_Markers")
    setwd("Developmental_Markers")
    DefaultAssay(Combined) <- "RNA"
    file <- paste0(WORKDIR,"/Gene_Lists/Paper_IPCS_genes.txt")
    genes_state <-read.table(file)
    pdf("Cell_Assignment_Plots.pdf")
    res <- cell_type_assignment(object=Combined,tab_name = "Identity",group_by="Cluster",file,assign=TRUE,color_list = color_clust)
    Combined$Identity <- as.vector(Combined$Cluster)
    for (level in levels(Combined$Cluster)){
        Combined$Identity[as.vector(Combined$Cluster) == as.numeric(level)] <- res$radar$Identity[as.numeric(level)]
    }
    Combined$Identity <- as.factor(Combined$Identity)
    DimPlot(Combined,group.by = c("Identity","Cluster"))
    dev.off()
    for(category in levels(as.factor(genes_state$V1))){
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
248
249
250
251
252
253
254
        category_genes <- toupper(as.vector(genes_state[genes_state$V1==category,2]))
        category_genes_l <- category_genes[category_genes%in%rownames(Combined)]
        Combined <- AddModuleScore(Combined,features = list(category_genes_l),name = category)
        pdf(paste0(category,"_umap_projection_condition_regPhase.pdf"),width = 8,height = 8)
        res <- ICSWrapper::scatter_gene(Combined,features = category_genes_l,ncol = 2,nrow = 2,size=1.1)
        plot(res)
        dev.off()
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
    }
    features <- c("iPSC_identity1","Mda_identity_stage11", "Mda_identity_stage21","Mda_identity_stage31","Mda_identity_stage41", "Non.Mda1")    
    pdf("Development_umap_projection_condition_regPhase.pdf",width = 12,height = 8)
    res <- ICSWrapper::scatter_gene(Combined,features = features,ncol = 3,nrow = 2,size=1.1)
    print(ggarrange(plotlist=res,ncol = 3,nrow = 2))
    dev.off()
    Combined <- ScaleData(Combined,rownames(Combined))
    category_genes <- toupper(as.vector(genes_state[,2]))
    category_genes_l <- category_genes[category_genes%in%rownames(Combined)]
    ICSWrapper::annotated_heat(Combined,row_annotation = c(1),gene_list = category_genes_l,ordering = "condition",title="Development_Markers",color_list = color_list)
    ics_scanpy(Combined,features = category_genes_l,group.by = "condition",Rowv = NA,scale="c1")
    setwd("../")
    # --------------------------------------------------------------------------------------------------
    ```
</details>
  
Dimitrios Kyriakis's avatar
Figure3    
Dimitrios Kyriakis committed
271
## Pairwise Differential Expression
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
272
273


Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
<details>
  <summary>Code</summary> 
    ```{r Pairwise DF}
    # =============================== 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("../")
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
334
    }
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
335
    # Apply DF
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
336
337
338
    mclapply(c(1:dim(cl_combinations)[2]),FUN=pairwise_df,object=Combined,cl_combinations=cl_combinations,mc.cores=1)
</details>

Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
339
340


Dimitrios Kyriakis's avatar
Figure3    
Dimitrios Kyriakis committed
341
## Intersection of DF genes
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
342

Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
343
344
345
346
347
348
349
350
351
<details>
  <summary>Code</summary> 
    ```{r gene intersection}
    dirs_pairs <- list.dirs("C:/Users/dimitrios.kyriakis/Desktop/PhD/Projects/Michi_Data/DF_Pairwise_Networks/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()
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
352
    # ===== Iterate through different DF files
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
    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)
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
376
    # ==== PLOT VENN
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
    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)
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
397
    dev.off()
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
398
    # ======= PINK VENN
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
    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()
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
420
    setwd("../")
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
421
422
423
    # ----------------------------------------------------------------------------------------------
    ```
</details>