-
Notifications
You must be signed in to change notification settings - Fork 0
/
DR_PRJCA001063_PDAC_LocalCC.R
510 lines (372 loc) · 27 KB
/
DR_PRJCA001063_PDAC_LocalCC.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
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
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
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
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
## (scRNA-seq data analysis for H5AD files)
#############
rm(list = ls()) # Clean variable
memory.limit(150000)
############# Library list #############
library(SummarizedExperiment)
library(Seurat)
library(SeuratDisk)
library(stringr)
library(SeuratWrappers)
library(monocle3)
library(AnnotationDbi)
library(org.Mm.eg.db)
library('org.Hs.eg.db')
library(Hmisc)
library(tidyverse)
library(garnett)
# library(cicero)
# detach("package:monocle", unload = TRUE)
# 錯誤: package 'monocle' is required by 'cicero' so will not be detached
############# Import files settings #############
## General setting
PathName = setwd(getwd())
RVersion = "20210525V1"
dir.create(paste0(PathName,"/",RVersion))
## Marker genes file
Marker_file_Name <- c("NAKAMURA_METASTASIS_MODEL_UP")
Marker_file <- paste0(PathName,"/",Marker_file_Name,".txt")
Marker_List <- read.delim(Marker_file,header=F,sep= c("\t"))
Marker_List2 <- as.data.frame(Marker_List[-1:-2,])
Garnett_Marker_file_Name <- c("NAKAMURA_METASTASIS_MODEL_M18483")
Garnett_Marker_file <- paste0(PathName,"/marker_file_",Garnett_Marker_file_Name,".txt")
## Cell cycle genes file
cc.genes_list <- read.csv(paste0(PathName,"/Cell cycle/regev_lab_cell_cycle_genesCh.csv")) # A list of cell cycle markers, from Tirosh et al, 2015, is loaded with Seurat.
# cc: Cell-Cycle
############# Marker genes file (Old Version) #############
# Marker_file_Name <- c("NAKAMURA_METASTASIS_MODEL_M18483")
# Marker_file <- paste0(PathName,"/marker_file_",Marker_file_Name,".txt")
# Marker_List <- read.table(Marker_file,header=F,sep= c(","),stringsAsFactors = FALSE, fill = TRUE)
# library(stringr)
# Marker_List_1 <- Marker_List[2,1]
# Marker_List_2 <- str_replace_all(Marker_List_1,"expressed: ","")
# Marker_List <- str_trim(Marker_List[2,-1], side = c("both"))
# Marker_List <- c(Marker_List_2,Marker_List)
############# Marker genes file (Old Version) #############
############# Parameter setting #############
## Gene list of interest
Main = c("TOP2A")
Main_Group = c("TOP2A","TP53","CGAS","PTK2")
Main_Group2 = c("KRAS","EXO1","NSUN2","MUC1","AMBP","FXYD2","TOP2B","CCNE1")
EMT_Meta = c("ANLN","APLP2","CD63","CDH2","CLIC4","CTSB","CX3CR1","DSG2","EDNRB")
candidates14 = c("BRIP1","KIF23","TOP2A","FOSL1","FAM25A","ANLN","NCAPH","KRT9","MCM4","CKAP2L","CENPE","RACGAP1","DTL","RAD51AP1")
DREAM_complex= c("RBL2","E2F4","E2F5","TFDP1","TFDP2")
Regulators= c("TP53","YBX1","E2F1")
## Color setting
colors_cc <- c("#FF9912B3", "#32CD3299", "#4169E1B3") ## Color for Cell-Cycle
## Format of data
GeneNAFMT <- c("HuGSymbol") # Gene names format of data: HuGSymbol,MouGSymbol,HuENSEMBL,MouENSEMBL
## Cluster cells setting
k_cds_sub_DucT2 <- c(100) # k for ductal cell type2
## Threshold of PCA scores
PCAThreshold_Pos <- 0.03
PCAThreshold_Neg <- -0.03
#********************************************************************************************************************#
################## Function setting ##################
## Call function
filePath <- ""
#匯入 同一個資料夾中的R檔案
getFilePath <- function(fileName) {
# path <- setwd("~") #專案資料夾絕對路徑
path <- setwd(getwd())
#字串合併無間隔
# 「<<-」為全域變數給值的指派
filePath <<- paste0(path ,"/" , fileName)
# 載入檔案
sourceObj <- source(filePath)
return(sourceObj)
}
#********************************************************************************************************************#
############# Import raw data #############
library(SummarizedExperiment)
library(Seurat)
library(SeuratDisk)
## Convert h5ad to h5seurat
Convert(paste0(PathName,"/StdWf1_PRJCA001063_CRC_besca2.raw.h5ad"), "PRJCA001063.h5seurat", assay = "RNA",) # This creates a copy of this .h5ad object reformatted into .h5seurat inside the example_dir directory
seuratObject <- LoadH5Seurat(paste0(PathName,"/PRJCA001063.h5seurat")) # This .d5seurat object can then be read in manually
## Convert Seurat Object to Monocle3 Object
library(SeuratWrappers)
cds <- as.cell_data_set(seuratObject) # Convert objects to Monocle3 'cell_data_set' objects
#---------------------------------------------------------------------------------------------------------------------#
############# Run Monocle3 #############
library(monocle3)
cds <- estimate_size_factors(cds) # issues with cds object in monocle3 #54 # https://github.com/satijalab/seurat-wrappers/issues/54
###### Pre-process the data ######
cds <- preprocess_cds(cds, num_dim = 100)
plot_pc_variance_explained(cds)
###### Reduce dimensionality and visualize the cells ######
##### UMAP #####
cds <- reduce_dimension(cds,preprocess_method = 'PCA')
plot_cells(cds)
##### (UMAP) Plot genes #####
plot_cells(cds, genes=c("TOP2A","TOP2B","TP53")) #error
## !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
cds@rowRanges@elementMetadata@listData$gene_short_name <- cds@assays@data@listData[["counts"]]@Dimnames[[1]]
plot_cells(cds, genes=c("TOP2A","TOP2B","TP53","CCNE1")) #ok
plot_cells(cds, genes=c(Main),cell_size=1,label_cell_groups = FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, genes=c(Main_Group),cell_size=0.5,label_cell_groups = FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, genes=c(Main_Group2),cell_size=0.5,label_cell_groups = FALSE, show_trajectory_graph = FALSE)
##### (UMAP) Plot different phenotype #####
plot_cells(cds, color_cells_by="Cell_type", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, color_cells_by="Type", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, color_cells_by="Patient", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, color_cells_by="CONDITION", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
##### Group cells into clusters ######
cds <- cluster_cells(cds)
plot_cells(cds, color_cells_by = "partition", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, color_cells_by = "cluster", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
############ Cell-Cycle Scoring and Regression - Monocle3 & Seurat Mutual conversion #############
############# Run Seurat #############
## Load package
library(Seurat)
library(SummarizedExperiment)
library(AnnotationDbi)
library(org.Mm.eg.db)
library('org.Hs.eg.db')
library(Hmisc)
###### Convert Monocle3 Object to Seurat Object ######
getFilePath("Monocle3_To_Seurat.R")
marrow <- Monocle3_To_Seurat(cds,"cds") #這個function存在於Monocle3_To_Seurat.R裡面
###### Assign Cell-Cycle Scores ######
getFilePath("Cell-Cycle Scoring and Regression.R")
marrow <- CCScorReg(GeneNAFMT,marrow) #這個function存在於Cell-Cycle Scoring and Regression.R裡面
RidgePlot(marrow,cols = colors_cc, features = c(Main), ncol = 1)
RidgePlot(marrow,cols = colors_cc, features = c(Main_Group), ncol = 2,y.max = 100)
RidgePlot(marrow,cols = colors_cc, features = c(Main_Group), ncol = 2,log=TRUE)
###### Insert the cell cycle results from Seurat into the Monocle3 cds object ######
cds@colData@listData$cell_cycle <- marrow@active.ident
# cds@colData@listData$cell_cycle <- marrow@meta.data[["Phase"]]
plot_cells(cds, color_cells_by="cell_cycle", label_cell_groups=FALSE ,show_trajectory_graph = F) + scale_color_manual(values = colors_cc)
## Plot the violin diagram
Maingroup_ciliated_genes <- c(Main_Group)
cds_marrow_cc <- cds[rowData(cds)$gene_short_name %in% Maingroup_ciliated_genes,]
plot_genes_violin(cds_marrow_cc, group_cells_by="cell_cycle", ncol=2, log_scale = FALSE)+ scale_fill_manual(values = colors_cc)
plot_genes_violin(cds_marrow_cc, group_cells_by="cell_cycle", ncol=2, log_scale = T)+ scale_fill_manual(values = colors_cc)
plot_genes_violin(cds_marrow_cc, group_cells_by="cell_cycle", ncol=2, log_scale = T)+ scale_fill_manual(values = colors_cc)+
geom_boxplot(width=0.1, fill="white") + theme(axis.text.x=element_text(angle=45, hjust=1))
############## Annotate your cells according to type (Custom Marker) ##############
#################### Cell discrimination by AddModuleScore ####################
getFilePath("Monocle3_AddModuleScore.R")
PDAC_Marker_file_Name <- c("GRUETZMANN_PANCREATIC_CANCER_UP")
PDAC_Marker_Name <- c("PDAC_Marker")
cds <- Monocle3_AddModuleScore(PDAC_Marker_file_Name,PDAC_Marker_Name,marrow,cds)
plot_cells(cds, color_cells_by= PDAC_Marker_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
#################### Cell discrimination by Garnett ####################
Human_classifier_cds <- train_cell_classifier(cds = cds,
marker_file = Garnett_Marker_file, # Import the marker_file
db=org.Hs.eg.db::org.Hs.eg.db, cds_gene_id_type = "SYMBOL",
#num_unknown = 2215, max_training_samples = 10000,
marker_file_gene_id_type = "SYMBOL",cores=8)
cds_Garnett <- classify_cells(cds, Human_classifier_cds, db = org.Hs.eg.db::org.Hs.eg.db,
cluster_extend = TRUE, cds_gene_id_type = "SYMBOL")
plot_cells(cds_Garnett,group_cells_by="cluster",cell_size=1.5,
color_cells_by="cluster_ext_type", show_trajectory_graph = FALSE)
plot_cells(cds_Garnett,group_cells_by="cluster",cell_size=1.5,
color_cells_by="cluster_ext_type",label_cell_groups=FALSE, show_trajectory_graph = FALSE)
####################### Constructing single-cell trajectories #######################
cds <- learn_graph(cds)
plot_cells(cds,
color_cells_by = "cluster",
label_cell_groups=FALSE, label_leaves=FALSE, label_branch_points=FALSE, graph_label_size=1.5)
cds <- order_cells(cds)
plot_cells(cds,
color_cells_by = "pseudotime",
label_cell_groups=FALSE, label_leaves=FALSE, label_branch_points=FALSE, graph_label_size=1.5)
MainGroup_lineage_cds <- cds[rowData(cds)$gene_short_name %in% Main_Group]
plot_genes_in_pseudotime(MainGroup_lineage_cds, color_cells_by="cell_cycle",cell_size=2,
min_expr=0.5)+ scale_color_manual(values = colors_cc)
#************************************************************************************************************************#
###################################### cds_subset ########################################
################## Grab specific terms ##################
## grepl Stroma
Stroma_cds <- cds[,grepl("Stromal", colData(cds)$Broad.cell.type, ignore.case=TRUE)]
plot_cells(Stroma_cds, reduction_method="tSNE", color_cells_by="partition")
######################## DucT2 ##########################
cds_sub_DucT2 <- choose_cells(cds)
#cds_subset <- reduce_dimension(cds_subset)
plot_cells(cds_sub_DucT2, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds_sub_DucT2, color_cells_by = "cluster", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
cds_sub_DucT2_NewK <- cluster_cells(cds_sub_DucT2,k = k_cds_sub_DucT2, resolution=1e-5)
plot_cells(cds_sub_DucT2_NewK, color_cells_by = "cluster", label_cell_groups=FALSE, show_trajectory_graph = FALSE)
################ Cell-Cycle Scoring and Regression (DucT2) ################
## Convert Monocle3 Object to Seurat Object # getFilePath("Monocle3_To_Seurat.R")
marrow_sub_DucT2_NewK <- Monocle3_To_Seurat(cds_sub_DucT2_NewK,"sub_DT2TOP2ACTR") #sub_DT2TOP2ACTR:sub_DucT2_TOP2ACenter
## Assign Cell-Cycle Scores # getFilePath("Cell-Cycle Scoring and Regression.R")
marrow_sub_DucT2_NewK <- CCScorReg(GeneNAFMT, marrow_sub_DucT2_NewK) #這個function存在於Cell-Cycle Scoring and Regression.R裡面
## Plot the RidgePlot
RidgePlot(marrow_sub_DucT2_NewK,cols = colors_cc, features = c(Main), ncol = 1)
## Insert the cell cycle results from Seurat into the Monocle3 cds object
cds_sub_DucT2_NewK@colData@listData$cell_cycle <- marrow_sub_DucT2_NewK@active.ident
plot_cells(cds_sub_DucT2_NewK, genes=c("TOP2A"),cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds_sub_DucT2_NewK, color_cells_by="cell_cycle",cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE) + scale_color_manual(values = colors_cc)
plot_cells(cds_sub_DucT2, color_cells_by="cell_cycle",cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE) + scale_color_manual(values = colors_cc)
############ Cell discrimination by AddModuleScore (DucT2) ############
## getFilePath("Monocle3_AddModuleScore.R")
PDAC_Marker_file_Name <- c("GRUETZMANN_PANCREATIC_CANCER_UP")
PDAC_Marker_Name <- c("PDAC_Marker")
cds_sub_DucT2_NewK <- Monocle3_AddModuleScore(PDAC_Marker_file_Name,PDAC_Marker_Name,marrow_sub_DucT2_NewK,cds_sub_DucT2_NewK)
plot_cells(cds_sub_DucT2_NewK, color_cells_by= PDAC_Marker_Name, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
############ Find marker genes expressed by each cluster (DucT2) ############
marker_test_res_DucT2 <- top_markers(cds_sub_DucT2_NewK, group_cells_by="cluster")
top_specific_markers_DucT2 <- marker_test_res_DucT2 %>% filter(fraction_expressing >= 0.10) %>%
group_by(cell_group) %>% top_n(10, pseudo_R2)
top_specific_marker_ids_DucT2 <- unique(top_specific_markers_DucT2 %>% pull(gene_id))
plot_genes_by_group(cds_sub_DucT2_NewK, top_specific_marker_ids_DucT2, group_cells_by="cluster",
ordering_type="maximal_on_diag", max.size=3)
plot_genes_by_group(cds_sub_DucT2_NewK, top_specific_marker_ids_DucT2,group_cells_by="cluster",
ordering_type="cluster_row_col",max.size=3)
## Get marker genes from each cluster
top_specific_markers_DucT2_Sub1 <- top_specific_markers_DucT2[top_specific_markers_DucT2$cell_group =="1",]
top_specific_markers_DucT2_Sub2 <- top_specific_markers_DucT2[top_specific_markers_DucT2$cell_group =="2",]
#...
marker_test_res_DucT2_Sub1 <- marker_test_res_DucT2[marker_test_res_DucT2$cell_group =="1",]
marker_test_res_DucT2_Sub2 <- marker_test_res_DucT2[marker_test_res_DucT2$cell_group =="2",]
#...
## Export a marker genes information file
write.table(marker_test_res_DucT2, file=paste0(PathName,"/",RVersion,"/",RVersion,"_",
"DucT2_marker_test_res.txt"), sep="\t", row.names=FALSE)
## Generate a Garnett file
# Require that markers have at least JS specificty score > 0.1 and be significant in the logistic test for identifying their cell type:
garnett_markers_DucT2 <- marker_test_res_DucT2 %>% filter(marker_test_q_value < 0.01 & specificity >= 0.1) %>%
group_by(cell_group) %>% top_n(100, marker_score)
# # Exclude genes that are good markers for more than one cell type:
# garnett_markers_DucT2 <- garnett_markers_DucT2 %>%
# group_by(gene_short_name) %>%
# filter(n() == 1)
generate_garnett_marker_file(garnett_markers_DucT2,max_genes_per_group = 100,
file=paste0(PathName,"/",RVersion,"/",RVersion,"_","DucT2_marker_Garnett.txt"))
######################## DucT2_TOP2A_Center ##########################
cds_sub_DucT2_TOP2ACenter <- choose_cells(cds_subset_NewK)
cds_sub_DucT2_TOP2ACenter_Ori <- cds_sub_DucT2_TOP2ACenter
plot_cells(cds_sub_DucT2_TOP2ACenter_Ori, label_cell_groups=FALSE)
plot_cells(cds_sub_DucT2_TOP2ACenter_Ori, label_cell_groups=FALSE, show_trajectory_graph = FALSE, cell_size = 2)
plot_cells(cds_sub_DucT2_TOP2ACenter_Ori, label_cell_groups=FALSE, show_trajectory_graph = FALSE, cell_size = 2, color_cells_by="cell_cycle")+ scale_color_manual(values = colors_cc)
plot_cells(cds_sub_DucT2_TOP2ACenter_Ori, genes=c(Main), label_cell_groups=FALSE, show_trajectory_graph = FALSE, cell_size = 2)
###### Convert Monocle3 Object to Seurat Object ######
# getFilePath("Monocle3_To_Seurat.R")
marrow_sub_DucT2_TOP2ACenter <- Monocle3_To_Seurat(cds_sub_DucT2_TOP2ACenter,"sub_DT2TOP2ACTR") #sub_DT2TOP2ACTR:sub_DucT2_TOP2ACenter
###### Assign Cell-Cycle Scores ######
# getFilePath("Cell-Cycle Scoring and Regression.R")
marrow_sub_DucT2_TOP2ACenter <- CCScorReg(GeneNAFMT,marrow_sub_DucT2_TOP2ACenter) #這個function存在於Cell-Cycle Scoring and Regression.R裡面
# view cell cycle scores and phase assignments
head(marrow_sub_DucT2_TOP2ACenter[[]])
## Plot the RidgePlot
RidgePlot(marrow_sub_DucT2_TOP2ACenter,cols = colors_cc, features = c(Main), ncol = 1)
RidgePlot(marrow_sub_DucT2_TOP2ACenter,cols = colors_cc, features = c(Main_Group), ncol = 2,log=TRUE)
RidgePlot(marrow_sub_DucT2_TOP2ACenter,cols = colors_cc, features = c(Main_Group), ncol = 2,y.max = 100)
png(paste0(PathName,"/",RVersion,"/",RVersion,"_","CellCycle_RidgePlot_sub_DT2TOP2ACTR_V2.png")) # 設定輸出圖檔
RidgePlot(marrow_sub_DucT2_TOP2ACenter,cols = colorsT, features = c(Main), ncol = 1)
dev.off() # 關閉輸出圖檔
###### Insert the cell cycle results from Seurat into the Monocle3 cds object ######
cds_sub_DucT2_TOP2ACenter@colData@listData$cell_cycle <- marrow_sub_DucT2_TOP2ACenter@active.ident
plot_cells(cds_sub_DucT2_TOP2ACenter, genes=c("TOP2A"),cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
plot_cells(cds_sub_DucT2_TOP2ACenter, color_cells_by="cell_cycle",cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE) + scale_color_manual(values = colors_cc)
plot_cells(cds_sub_DucT2_TOP2ACenter_Ori, color_cells_by="cell_cycle",cell_size=2, label_cell_groups=FALSE, show_trajectory_graph = FALSE) + scale_color_manual(values = colors_cc)
## Plot the Violin Plot
cds_sub_DT2TOP2ACTR_Maingroup <- cds_sub_DucT2_TOP2ACenter[rowData(cds_sub_DucT2_TOP2ACenter)$gene_short_name %in% Main_Group,]
plot_genes_violin(cds_sub_DT2TOP2ACTR_Maingroup, group_cells_by="cell_cycle", ncol=2, log_scale = FALSE) +
scale_fill_manual(values = colors_cc) +
theme(axis.text.x=element_text(angle=45, hjust=1))
png(paste0(PathName,"/",RVersion,"/",RVersion,"_","CellCycle_Violin_Main_sub_DT2TOP2ACTR_V2.png")) # 設定輸出圖檔
plot_genes_violin(cds_sub_DT2TOP2ACTR_Maingroup, group_cells_by="cell_cycle", ncol=2, log_scale = FALSE) +
scale_fill_manual(values = colors_cc) +
theme(axis.text.x=element_text(angle=45, hjust=1))
dev.off() # 關閉輸出圖檔
##
png(paste0(PathName,"/",RVersion,"/",RVersion,"_","UMAP_CellCycle_sub_DT2TOP2ACTR_V2.png")) # 設定輸出圖檔
plot_cells(cds_sub_DucT2_TOP2ACenter, color_cells_by="cell_cycle",cell_size=3, label_cell_groups=FALSE, show_trajectory_graph = FALSE) + scale_color_manual(values = colors_cc)
dev.off() # 關閉輸出圖檔
png(paste0(PathName,"/",RVersion,"/",RVersion,"_","UMAP_",Main,"_sub_DT2TOP2ACTR_V2.png")) # 設定輸出圖檔
plot_cells(cds_sub_DucT2_TOP2ACenter, genes=c(Main),cell_size=3, label_cell_groups=FALSE, show_trajectory_graph = FALSE)
dev.off() # 關閉輸出圖檔
MainGroup_lineage_sub_DT2TOP2ACTR <- cds_sub_DucT2_TOP2ACenter[rowData(cds_sub_DucT2_TOP2ACenter)$gene_short_name %in% Main_Group]
plot_genes_in_pseudotime(MainGroup_lineage_sub_DT2TOP2ACTR,
color_cells_by="cell_cycle",cell_size=2,
min_expr=0.5)+ scale_color_manual(values = colors_cc)
#************************************************************************************************************************#
######################## DucT2_TOP2ACenter trajectories ##########################
for (i in c(1:8)) {
cds_sub_DucT2_TOP2ACenter_Tn <- choose_graph_segments(cds_sub_DucT2 ,clear_cds = FALSE)
plot_cells(cds_sub_DucT2_TOP2ACenter_Tn, color_cells_by="cell_cycle",cell_size=2,
label_cell_groups=FALSE) + scale_color_manual(values = colors_cc)
###### Convert Monocle3 Object to Seurat Object ######
# getFilePath("Monocle3_To_Seurat.R")
marrow_sub_DucT2_TOP2ACenter_Tn <- Monocle3_To_Seurat(cds_sub_DucT2_TOP2ACenter_Tn,paste0("sub_DT2TOP2ACTR_T", i)) #sub_DT2TOP2ACTR:sub_DucT2_TOP2ACenter
###### Assign Cell-Cycle Scores ######
# getFilePath("Cell-Cycle Scoring and Regression.R")
marrow_sub_DucT2_TOP2ACenter_Tn <- CCScorReg(GeneNAFMT,marrow_sub_DucT2_TOP2ACenter_Tn) #這個function存在於Cell-Cycle Scoring and Regression.R裡面
assign(paste0("marrow_sub_DucT2_TOP2ACenter_T", i),marrow_sub_DucT2_TOP2ACenter_Tn)
###### Insert the cell cycle results from Seurat into the Monocle3 cds object ######
cds_sub_DucT2_TOP2ACenter_Tn@colData@listData$cell_cycle <- marrow_sub_DucT2_TOP2ACenter_Tn@active.ident
plot_cells(cds_sub_DucT2_TOP2ACenter_Tn, color_cells_by="cell_cycle", label_cell_groups=FALSE) + scale_color_manual(values = colors_cc)
assign(paste0("cds_sub_DucT2_TOP2ACenter_T", i),cds_sub_DucT2_TOP2ACenter_Tn)
###### PCA Scores for finding significantly different genes at the endpoints ######
getFilePath("PCA_Threshold.R")
PCA_DT2TOP2ACTR_Tn <- assign(paste0("PCA_DT2TOP2ACTR_T", i),marrow_sub_DucT2_TOP2ACenter_Tn@reductions[["pca"]]@feature.loadings)
assign(paste0("PCA_DT2TOP2ACTR_T", i,"_PC_Sum"),PCA_Threshold_Pos(PCA_DT2TOP2ACTR_Tn, i ,PCAThreshold_Pos))
assign(paste0("PCA_DT2TOP2ACTR_T", i,"_NC_Sum"),PCA_Threshold_Neg(PCA_DT2TOP2ACTR_Tn, i ,PCAThreshold_Neg))
rm(cds_sub_DucT2_TOP2ACenter_Tn,marrow_sub_DucT2_TOP2ACenter_Tn)
}
#************************************************************************************************************************#
######################## Heterogeneity center and Ori Ductal2 ##########################
cds_sub_HeteroCent_OriDucT2 <- choose_cells(cds)
#cds_subset <- reduce_dimension(cds_subset)
plot_cells(cds_sub_HeteroCent_OriDucT2, label_cell_groups=FALSE, show_trajectory_graph = FALSE,cell_size = 2)
cds_sub_HeteroCent_K100 <- cluster_cells(cds_sub_HeteroCent_OriDucT2,k = 100, resolution=1e-5)
png(paste0(PathName,"/",RVersion,"/",RVersion,"_","UMAP_","_sub_HeteroCent_OriDucT_cluster_clu_K100_1",".png")) # 設定輸出圖檔
plot_cells(cds_sub_HeteroCent_K100, label_cell_groups=FALSE, color_cells_by = "cluster", show_trajectory_graph = FALSE,cell_size = 2)
dev.off() # 關閉輸出圖檔
## Find marker genes expressed by each cluster
marker_test_HeteroCent_OriDucT2 <- top_markers(cds_sub_HeteroCent_K100, group_cells_by="cluster")
top_specific_markers_HeteroCent_OriDucT2 <- marker_test_HeteroCent_OriDucT2 %>%
filter(fraction_expressing >= 0.10) %>%
group_by(cell_group) %>%
top_n(25, pseudo_R2)
top_specific_marker_ids_HeteroCent_OriDucT2 <- unique(top_specific_markers_HeteroCent_OriDucT2 %>% pull(gene_id))
plot_genes_by_group(cds_sub_HeteroCent_K100,
top_specific_marker_ids_HeteroCent_OriDucT2,
group_cells_by="cluster",
ordering_type="maximal_on_diag",
max.size=3)
top_specific_markers_HeteroCent_OriDucT2 <- data.frame(top_specific_markers_HeteroCent_OriDucT2)
top_marker_HeteroCent_OriDucT2_Sub1 <- top_specific_markers_HeteroCent_OriDucT2[top_specific_markers_HeteroCent_OriDucT2$cell_group =="1",]
top_marker_HeteroCent_OriDucT2_Sub2 <- top_specific_markers_HeteroCent_OriDucT2[top_specific_markers_HeteroCent_OriDucT2$cell_group =="2",]
#************************************************************************************************************************#
############(ERROR) Finding modules of co-regulated genes ############
ciliated_cds_pr_test_res <- graph_test(cds_subTra2, neighbor_graph="principal_graph", cores=4)
pr_deg_ids <- row.names(subset(ciliated_cds_pr_test_res, q_value < 0.05))
gene_module_df <- find_gene_modules(cds_subTra2[pr_deg_ids,], resolution=1e-2)
cell_group_df <- tibble::tibble(cell=row.names(colData(cds_subTra2)),
cell_group=partitions(cds)[colnames(cds_subTra2)])
agg_mat <- aggregate_gene_expression(cds_subTra2, gene_module_df, cell_group_df)
row.names(agg_mat) <- stringr::str_c("Module ", row.names(agg_mat))
colnames(agg_mat) <- stringr::str_c("Partition ", colnames(agg_mat))
pheatmap::pheatmap(agg_mat, cluster_rows=TRUE, cluster_cols=TRUE,
scale="column", clustering_method="ward.D2",
fontsize=6)
#************************************************************************************************************************#
############ Working with 3D trajectories ############
cds_3d <- reduce_dimension(cds, max_components = 3,preprocess_method = 'PCA')
cds_3d <- cluster_cells(cds_3d)
cds_3d <- learn_graph(cds_3d)
cds_3d <- order_cells(cds_3d, root_pr_nodes=get_earliest_principal_node(cds))
# Error in get_earliest_principal_node(cds) :
# 沒有這個函數 "get_earliest_principal_node"
cds_3d_plot_obj <- plot_cells_3d(cds_3d, color_cells_by="partition")
plot_cells_3d(cds_3d)
plot_cells_3d(cds_3d, color_cells_by="cluster", show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, color_cells_by="Cell_type", show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, color_cells_by="Type", show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, color_cells_by="Patient", show_trajectory_graph = FALSE)
# plot_cells_3d(cds_3d, color_cells_by="CONDITION", show_trajectory_graph = FALSE)
cds_3d@colData@listData$PDAC_Marker <- marrow_PDAC_Marker@meta.data[["PDAC_Marker1"]]
plot_cells_3d(cds_3d, color_cells_by="PDAC_Marker", show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, genes = Main, show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d, color_cells_by="cell_cycle", show_trajectory_graph = FALSE)
cds_3d_sub_DucT2_TOP2ACenter <- reduce_dimension(cds_sub_DucT2_TOP2ACenter, max_components = 3,preprocess_method = 'PCA')
plot_cells_3d(cds_3d_sub_DucT2_TOP2ACenter, genes = Main, show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d_sub_DucT2_TOP2ACenter, color_cells_by="cell_cycle", show_trajectory_graph = FALSE)
cds_3d_sub_DucT2 <- reduce_dimension(cds_sub_DucT2, max_components = 3,preprocess_method = 'PCA')
plot_cells_3d(cds_3d_sub_DucT2, genes = Main, show_trajectory_graph = FALSE)
plot_cells_3d(cds_3d_sub_DucT2, color_cells_by="cell_cycle", show_trajectory_graph = FALSE)