README.md 21.1 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
Figures    
Dimitrios Kyriakis committed
8
![Figure1](Figures/Figure1.jpg)
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
9
**Figure 1:** Experimental design.
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
10
11

![Figure2](Figures/Figure2.jpg)
Dimitrios Kyriakis's avatar
Figure1    
Dimitrios Kyriakis committed
12
**Figure 2:** Generation and classification of iPS cell lines.
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
13
14
15
16
17
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 +/-.

Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
18

Dimitrios Kyriakis's avatar
figure3    
Dimitrios Kyriakis committed
19
![Figure3](Figures/Figure3.jpg)
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
20
**Figure 3:** iPSC status, differentiation and classification of mDA neurons.
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
21
22
23
24
(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)
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
25
**Figure 4:** iPSC status, differentiation and classification of mDA neurons.
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
26
27
28
29
(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)
Dimitrios Kyriakis's avatar
Figures    
Dimitrios Kyriakis committed
30
**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
31
32
33
34
35


# scRNAseq Analysis

## Libraries 
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
<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
60

Dimitrios Kyriakis's avatar
Figure3    
Dimitrios Kyriakis committed
61
## Setting Up
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
<details>
  <summary>Code</summary>  
    ```{r setup, include=FALSE}
    # ================================ 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
83
    # ========= Parameters
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
84
85
86
87
88
89
90
91
92
    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
93
     ```
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
94
</details>
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
95
96
97
98
99
100

## 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
readme    
Dimitrios Kyriakis committed
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
<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
121
122
123
124
125
126



## 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
readme    
Dimitrios Kyriakis committed
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
<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
156
157
158
159
160
161
162
163





## 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
readme    
Dimitrios Kyriakis committed
164
165
166
167
168
169
170
<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
171
    # ====== Reorder Conditions
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
172
173
    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
174
    # ====== PLot
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
175
176
177
178
    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
179
    # Quality Plots
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
180
181
182
183
184
185
186
187
188
189
190
191
    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
192
    # Sum up Plots
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
193
194
195
196
197
198
199
    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
200
    dev.off()
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
201
202
203
204
    # ---------------------------------------------------------------------------------------
    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
205
    # Free Space
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
206
207
208
209
210
211
212
    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
213
214
215



Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
216
## iPSCs Differentiation
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
217

Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
218
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
219

Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
<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
239
240
241
242
243
244
245
        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
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
    }
    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
262
## Pairwise Differential Expression
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
263
264


Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
265
266
267
268
269
270
271
272
273
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
<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
325
    }
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
326
    # Apply DF
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
327
328
329
    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
330
331


Dimitrios Kyriakis's avatar
Figure3    
Dimitrios Kyriakis committed
332
## Intersection of DF genes
Dimitrios Kyriakis's avatar
Readme    
Dimitrios Kyriakis committed
333

Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
334
335
336
337
338
339
340
341
342
<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
343
    # ===== Iterate through different DF files
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
    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
367
    # ==== PLOT VENN
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
    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
388
    dev.off()
Dimitrios Kyriakis's avatar
Dimitrios Kyriakis committed
389
    # ======= PINK VENN
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
    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
411
    setwd("../")
Dimitrios Kyriakis's avatar
readme    
Dimitrios Kyriakis committed
412
413
414
    # ----------------------------------------------------------------------------------------------
    ```
</details>