Log In Sign Up
Models:
Wanda Brown
WandaB/
ImmunogenomicLandscape-BC
0
Downloads: 1
[8e0848]: / preprocessing / Preprocessing_ALL_subtyping.R

Download this file

257 lines (191 with data), 13.0 kB 

  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
# created with 3.3.3 Rtsne_0.13

# first make cancer map and validate clusters:

# gexp annot:

source("/research/users/ppolonen/git_home/Hemap_published_scripts/Cancermap.R")

source("/research/users/ppolonen/git_home/Hemap_published_scripts/Plot_color_vector.R")

source("/research/users/ppolonen/git_home/Hemap_published_scripts/Plot_cancermap_clusters.R")

source("/research/users/ppolonen/git_home/Hemap_published_scripts/Example_useCases/useCase3/Find_correlated_genes.R")

source("/research/users/ppolonen/git_home/common_scripts/statistics/functions_statistics.R")

source("/research/users/ppolonen/git_home/common_scripts/statistics/statistics_wrappers.R")



# cancermap coordinates:

library(reshape2)

library(gridExtra)

library(GSVA)

library(RColorBrewer)

library(ggplot2)

library(Rtsne)

library(LPCM)

library(parallel)



setwd("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/")



# gexp data

data=get(load("/research/groups/sysgen/PROJECTS/HEMAP/HEMAP/dat2figs/data/data9544_with_gene_symbols.RData"))



# annotations

annot = get(load("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/data/Hemap_immunology_Annotations.Rdata"))

gexp.hemap=t(data[rownames(data)%in%annot[annot$colorClass=="pre-B-ALL"&annot$Sample.type=="Cancer",1],])

annot.sub=annot[annot$colorClass=="pre-B-ALL"&annot$Sample.type=="Cancer",]



load("Hemap_ALL_subtypes.Rdata")



# cancermap from pre-B-ALL, gu used about 1000 genes ~ 5% var.

coord_hemap=CancerMap(data = t(as.matrix(gexp.hemap)), name = "Hemap_pre-B-ALL", VAR = 5, BW = 1.25, perplexity = 30, PATH_OUTPUT = "/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/")



plot.scatter(x=coord_hemap$x, y = coord_hemap$y, group =  coordinates.subtype$subtype, namev = coordinates.subtype$subtype, main = "Hemap ALL", rasterize = F, width = 70*2, height = 74*2, SIZE = 0.5)



coord_hemap=read.delim("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/cancermap_Hemap_pre-B-ALL_5pct_genes_BH-SNE_mean-shift_BW1.75.txt", header=T, stringsAsFactors = F)

peaks_hemap=read.delim("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/cancermap_Hemap_pre-B-ALL_5pct_genes_BH-SNE_mean-shift_BW1.75_cluster_centroids.txt", header=T, stringsAsFactors = F)



a1=Plot_cancermap_clusters(X = coord_hemap, peaks = peaks_hemap)



#*********************** make genesets from St judes ALL: ********************

load("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/PecanALL_subtypes.Rdata")



# clusters=coordinates.subtype$subtype

# # make gene sets

# genesets.cor=unlist(mclapply(unique(clusters), Find_correlated_genes_new, gexp, clusters, mc.cores=7), recursive=F)

# 

# clusters2=annot$`2nd subtype`

# 

# # make gene sets

# genesets.cor.2=unlist(mclapply(unique(clusters2), Find_correlated_genes_new, gexp, clusters2, mc.cores=7), recursive=F)

# 

# genesets.cor=append(genesets.cor, genesets.cor.2)





# use predefined gene sets from Gu et al:



genesets.up=lapply(3:16, function(i){

  genes=openxlsx::read.xlsx("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/data/Pecan_ALL/B-ALL-subtype-signature.xlsx", sheet = i, colNames = TRUE)

  genes=genes$geneName[genes$log2FoldChange>1&genes$padj<0.001]

  genes=genes[!is.na(genes)]

})



names(genesets.up)=c("PAX5 P80R", "PAX5alt", "IKZF1 N159Y", "Hyperdiploid", "ETV6-RUNX1", "KMT2A", "Ph", "TCF3-PBX1", "Low hypodiploid", "DUX4", "ZNF384", "MEF2D", "iAMP21", "BCL2 MYC")

  



genesets.down=lapply(3:16, function(i){

  genes=openxlsx::read.xlsx("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/data/Pecan_ALL/B-ALL-subtype-signature.xlsx", sheet = i, colNames = TRUE)

  genes=genes$geneName[genes$log2FoldChange>1&genes$padj<0.001]

  genes=genes[!is.na(genes)]

  genes=head(genes, 20)

})



names(genesets.down)=c("PAX5 P80R", "PAX5alt", "IKZF1 N159Y", "Hyperdiploid", "ETV6-RUNX1", "KMT2A", "Ph", "TCF3-PBX1", "Low hypodiploid", "DUX4", "ZNF384", "MEF2D", "iAMP21", "BCL2 MYC")



# cancermap from pre-B-ALL, gu used about 1000 genes ~ 5% var.

coord_pecan=CancerMap(data = t(as.matrix(gexp)), name = "Pecan_pre-B-ALL", VAR = 10, BW = 1.75, perplexity = 30, PATH_OUTPUT = "/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/")

plot.scatter(x=coord_pecan$x, y = coord_pecan$y, group =  coordinates.subtype$`primary subtype`, namev = coordinates.subtype$`primary subtype`, main = "Pecan ALL", rasterize = F, width = 70*2, height = 74*2, SIZE = 0.5)



# gsva_es <- gsva(as.matrix(gexp), method="gsva", genesets.up, mx.diff=F, tau=0.25, verbose=T, min.sz=5, max.sz=10500, parallel.sz=8)

# save(gsva_es, file="gsva_es_Pecan_ALL_pecan_theirsets.Rdata")



pecan_gsva=get(load("gsva_es_Pecan_ALL_pecan_theirsets.Rdata"))



# plot clusters

coord_pecan=read.delim("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/cancermap_Pecan_pre-B-ALL_10pct_genes_BH-SNE_mean-shift_BW1.75.txt", header=T, stringsAsFactors = F)

peaks_pecan=read.delim("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/cancermap_Pecan_pre-B-ALL_10pct_genes_BH-SNE_mean-shift_BW1.75_cluster_centroids.txt", header=T, stringsAsFactors = F)



a1=Plot_cancermap_clusters(X = coord_pecan, peaks = peaks_pecan)



feats=rownames(pecan_gsva)



gsva_es2=t(scale(t(pecan_gsva)))



Plot_GSVA_scores=function(feat, data_plot, VALUE, SIZE, CLUSTER_CENTRE, coord, peaks){

  

  # Find specified feature(feat) from data_plot

  data=data_plot[rownames(data_plot)%in%feat,]

  

  # Transform matrix to numeric. 

  data=as.numeric(data)

  

  # Color vector for gradient colors from blue to red.

  rbPal <- colorRampPalette(c('blue','red'))

  

  # Adjust data for gradient colors.

  data=c(data, 2, -2) # adjust range

  datCol <- rbPal(10)[as.numeric(cut(data,breaks = 10))]

  datCol=datCol[-c(length(datCol)-1, length(datCol))]

  data=data[-c(length(data)-1, length(data))]

  

  # Samples below cutoff colored grey.

  datCol[abs(data)<VALUE]="grey75"

  front=abs(data)>VALUE

  

  # Prepare coordinate data for plotting.

  dat2show <- cbind(coord$x, coord$y)

  df=as.data.frame(dat2show)

  colnames(df) = c("X1","X2")

  

  # Generate plot title. 

  cutoff=paste("GSVA score >", VALUE)

  plotname=paste(feat, cutoff, sep="\n")

  

  # Call actual plotting function.

  drawFig(df, CLUSTER_CENTRE, datCol, front, plotname, SIZE, peaks) 

}



# Plot GSVA scores to Hemap cancermap, obtain plotting data

p.all=lapply(feats, Plot_GSVA_scores, gsva_es2, VALUE=0.5, SIZE=0.2, CLUSTER_CENTRE=T, coord_hemap, peaks_hemap)

p.all=p.all[!sapply(p.all, is.null)] # remove empty just in case



# Save PDF figure (A4) with multiple panels

ggsave(paste0("PECAN_ALL_theirsubtypes_multipage.pdf"),

       do.call(marrangeGrob, list(grobs=p.all, nrow=4, ncol=3)), width = 210, height = 297, units = "mm", dpi=150)



#************************ change the name to match with their gene sets: ************************



# cohort=openxlsx::read.xlsx("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/data/Pecan_ALL/B-ALL-subtype-signature.xlsx", sheet = 1, colNames = TRUE)

# 

# gexp=gexp[,match(cohort$patient, colnames(gexp))]

# 

# # cancermap from pre-B-ALL, gu used about 1000 genes ~ 5% var.

# coordinates.subtype=CancerMap(data = t(as.matrix(gexp)), name = "Pecan_pre-B-ALL_835", VAR = 10, BW = 1.75, perplexity = 30, PATH_OUTPUT = "/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/")

# 

# coordinates.subtype$subtype=cohort$primary.subtype

# plot.scatter(x=coordinates.subtype$x, y = coordinates.subtype$y, group =  coordinates.subtype$subtype, namev = coordinates.subtype$subtype, main = "Pecan ALL", rasterize = F, width = 70*2, height = 74*2, SIZE = 0.5)

# 

# save(list = c("gexp", "coordinates.subtype"), file = "Pecan_ALL_835_subtypes.Rdata")

# 



#*******************************************************************************



# run GSVA

gsva_es <- gsva(as.matrix(gexp.hemap), method="gsva", genesets.up, mx.diff=F, tau=0.25, verbose=T, min.sz=5, max.sz=10500, parallel.sz=8)

save(gsva_es, file="gsva_es_hemap_ALL_pecan_theirsets.Rdata")

load("gsva_es_hemap_ALL_pecan_theirsets.Rdata")



#********************************************* Step 3: Plot GSVA scores to hemap cancermap ************************************************************

feats=rownames(gsva_es)



gsva_es2=t(scale(t(gsva_es)))



Plot_GSVA_scores=function(feat, data_plot, VALUE, SIZE, CLUSTER_CENTRE, coord, peaks){

  

  # Find specified feature(feat) from data_plot

  data=data_plot[rownames(data_plot)%in%feat,]

  

  # Transform matrix to numeric. 

  data=as.numeric(data)

  

  # Color vector for gradient colors from blue to red.

  rbPal <- colorRampPalette(c('blue','red'))

  

  # Adjust data for gradient colors.

  data=c(data, 2, -2) # adjust range

  datCol <- rbPal(10)[as.numeric(cut(data,breaks = 10))]

  datCol=datCol[-c(length(datCol)-1, length(datCol))]

  data=data[-c(length(data)-1, length(data))]

  

  # Samples below cutoff colored grey.

  datCol[abs(data)<VALUE]="grey75"

  front=abs(data)>VALUE

  

  # Prepare coordinate data for plotting.

  dat2show <- cbind(coord$x, coord$y)

  df=as.data.frame(dat2show)

  colnames(df) = c("X1","X2")

  

  # Generate plot title. 

  cutoff=paste("GSVA score >", VALUE)

  plotname=paste(feat, cutoff, sep="\n")

  

  # Call actual plotting function.

  drawFig(df, CLUSTER_CENTRE, datCol, front, plotname, SIZE, peaks) 

}



# Plot GSVA scores to Hemap cancermap, obtain plotting data

p.all=lapply(feats, Plot_GSVA_scores, gsva_es2, VALUE=0.5, SIZE=0.2, CLUSTER_CENTRE=T, coord_hemap, peaks_hemap)

p.all=p.all[!sapply(p.all, is.null)] # remove empty just in case



# Save PDF figure (A4) with multiple panels

ggsave(paste0("Hemap_ALL_PECAN_ALL_theirsubtypes_multipage.pdf"),

       do.call(marrangeGrob, list(grobs=p.all, nrow=4, ncol=3)), width = 210, height = 297, units = "mm", dpi=150)



coordinates.subtype=coord_hemap

# checked the genetics and combined the subtypes as larger subtypes:

coordinates.subtype$subtype[coord_hemap$cluster%in%c(23)]="PAX5 P80R"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(12)]="PAX5alt"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(17,14,31,20,6)]="ETV6-RUNX1"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(9)]="DUX4"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(21,4,22, 27)]="KMT2A"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(1,29)]="ZNF384"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(13,10,25,24,33)]="Ph"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(19,16,32)]="Hyperdiploid"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(2,15)]="TCF3-PBX1"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(26)]="MEF2D"

coordinates.subtype$subtype[coord_hemap$cluster%in%c(3, 30, 5,11,8,18,28,7)]="Other"





Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_BCR.ABL1==1, "darkgreen", "grey75"))

Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_hyperdiploid==1, "darkgreen", "grey75"))

Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_hypodiploid==1, "darkgreen", "grey75"))

Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_TEL.AML1==1, "darkgreen", "grey75"))

Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_TCF3_PBX1==1, "darkgreen", "grey75"))

Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_IgH_cMYC==1, "darkgreen", "grey75"))

Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_MLL==1, "darkgreen", "grey75"))

Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_other==1, "darkgreen", "grey75"))

Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_TELdel==1, "darkgreen", "grey75"))

Plot_color_vector(coord_hemap, color = ifelse(annot.sub$GENETICS_preBALL_trisomy_chr4_chr10==1, "darkgreen", "grey75"))



aml.gexp=as.matrix(gexp)



ord=c("PAX5 P80R", "PAX5alt","ETV6-RUNX1","DUX4", "KMT2A", "ZNF384", "Ph", "Hyperdiploid", "TCF3-PBX1", "MEF2D", "other")

pl=gsva_es[,order(match(coordinates.subtype$subtype, ord))]

pl=t(scale(t(pl)))

pl[pl>2]=2



ha=HeatmapAnnotation("TSNE"=as.character(coordinates.subtype)[order(match(coordinates.subtype, ord))])



pdf("PECAN_ALL_subtype_hemap_ALL.pdf", height = 8, width = 8)

Heatmap(pl[match(ord, rownames(pl)),],top_annotation = ha, cluster_rows = F, cluster_columns=F, show_column_names = F, cluster_column_slices = T)

dev.off()



ha=HeatmapAnnotation("TSNE"=as.character(coordinates.subtype$subtype)[order(match(coordinates.subtype$subtype, ord))], df = annot.sub[order(match(coordinates.subtype$subtype, ord)),grepl("GENETICS_preBALL", colnames(annot.sub))])



pdf("PECAN_ALL_subtype_hemap_ALL_validation.pdf", height = 8, width = 16)

Heatmap(pl[match(ord, rownames(pl)),],top_annotation = ha, cluster_rows = F, cluster_columns=F, show_column_names = F, cluster_column_slices = T)

dev.off()





gexp=gexp.hemap



save(list = c("gexp", "coordinates.subtype"), file="Hemap_ALL_subtypes.Rdata")