sc-TissueMapper_functions.R 43 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
#sc-TissueMapper
#Author: Gervaise H. Henry
#Email: gervaise.henry@utsouthwestern.edu
#Lab: Strand Lab, Deparment of Urology, University of Texas Southwestern Medical Center


scFolders <- function(){
  if (!dir.exists("./analysis/qc/")){
    dir.create("./analysis/qc/")
  }
11
12
13
14
15
16
  if (!dir.exists("./analysis/qc/")){
    dir.create("./analysis/qc/")
  }
  if (!dir.exists("./analysis/qc/cutoffs/")){
    dir.create("./analysis/qc/cutoffs/")
  }
17
18
19
20
21
22
23
24
25
26
27
28
  if (!dir.exists("./analysis/qc/cellcycle")){
    dir.create("./analysis/qc/cellcycle")
  }
  if (!dir.exists("./analysis/vis")){
    dir.create("./analysis/vis")
  }
  if (!dir.exists("./analysis/score_id")){
    dir.create("./analysis/score_id")
  }
  if (!dir.exists("./analysis/cor")){
    dir.create("./analysis/cor")
  }
29
30
31
32
33
34
  if (!dir.exists("./analysis/shiny")){
    dir.create("./analysis/shiny")
  }
  if (!dir.exists("./analysis/shiny")){
    dir.create("./analysis/shiny")
  }
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
  for (i in c("raw","id","id.epi","id.fmst","id.st","id.leu")){
    if (!dir.exists(paste0("./analysis/shiny/",i))){
      dir.create(paste0("./analysis/shiny/",i))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/filtered_feature_bc_matrix"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/filtered_feature_bc_matrix"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/analysis"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/analysis"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/analysis/clustering"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/analysis/clustering"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/analysis/diffexp"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/analysis/diffexp"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/analysis/pca"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/analysis/pca"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/analysis/pca/10_components"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/analysis/pca/10_components"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/analysis/tsne"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/analysis/tsne"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/analysis/tsne/2_components"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/analysis/tsne/2_components"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/analysis/umap"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/analysis/umap"))
    }
    if (!dir.exists(paste0("./analysis/shiny/",i,"/outs/analysis/umap/2_components"))){
      dir.create(paste0("./analysis/shiny/",i,"/outs/analysis/umap/2_components"))
    }
72
  }
73
74
75
}


Gervaise Henry's avatar
Gervaise Henry committed
76
scLoad <- function(p,cellranger=3,aggr=TRUE,ncell=0,nfeat=0){
77
78
79
80
  #Load and prefilter filtered_gene_bc_matrices_mex output from cellranger
  
  #Inputs:
  #p = project name
81
82
  #cellranger cellranger version number used for count/aggr, 2 or 3
  #aggr = if the samples are already aggregated, TRUE if useing the output of aggr, FALSE if using outputs of each count
83
84
  
  #Outputs:
85
86
  #sc10x = Seurat object list
  #sc10x.groups = group labels for each sample
87
88
  
  
89
90
91
92
93
94
95
  sc10x.groups <- read_csv(paste0("./analysis/DATA/",p,"-demultiplex.csv"))
  
  
  #Load filtered_gene_bc_matrices output from cellranger
  sc10x.data <- list()
  sc10x <- list()
  if (aggr==TRUE){
96
    if (cellranger==2){
97
      sc10x.data[aggr] <- Read10X(data.dir=paste0("./analysis/DATA/10x/filtered_gene_bc_matrices_mex/"))
98
    } else {
99
      sc10x.data[aggr] <- Read10X(data.dir=paste0("./analysis/DATA/10x/filtered_feature_bc_matrix/"))
100
    }
101
    sc10x[aggr] <- new("seurat",raw.data=sc10x.data[aggr])
102
  } else {
103
104
105
106
107
108
    for (i in sc10x.groups$Samples){
      if (cellranger==2){
        sc10x.data[i] <- Read10X(data.dir=paste0("./analysis/DATA/10x/",i,"/filtered_gene_bc_matrices/"))
      } else {
        sc10x.data[i] <- Read10X(data.dir=paste0("./analysis/DATA/10x/",i,"/filtered_feature_bc_matrix/"))
      }
Gervaise Henry's avatar
Gervaise Henry committed
109
      sc10x[i] <- CreateSeuratObject(counts=sc10x.data[[i]],project=p,min.cells=ncell,min.features=nfeat)
110
      sc10x[[i]]$samples <- i
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
  # #Label sample names from aggregation_csv.csv
  # if (sub==FALSE){
  #   if (cellranger==2){
  #     sc10x.aggr <- read_csv("./analysis/DATA/10x/aggregation_csv.csv")
  #   } else {
  #     sc10x.aggr <- read_csv("./analysis/DATA/10x/aggregation.csv")
  #   }
  # } else {
  #   if (cellranger==2){
  #     sc10x.aggr <- read_csv(paste0("./analysis/DATA/",p,"/10x/aggregation_csv.csv"))
  #   } else {
  #     sc10x.aggr <- read_csv(paste0("./analysis/DATA/",p,"/10x/aggregation.csv"))
  #   }
  # }
  # cell.codes <- as.data.frame(sc10x@raw.data@Dimnames[[2]])
  # colnames(cell.codes) <- "barcodes"
  # rownames(cell.codes) <- cell.codes$barcodes
  # cell.codes$lib.codes <- as.factor(gsub(pattern=".+-",replacement="",cell.codes$barcodes))
  # cell.codes$samples <- sc10x.aggr$library_id[match(cell.codes$lib.codes,as.numeric(rownames(sc10x.aggr)))]
  # sc10x <- CreateSeuratObject(counts=sc10x.data,project=p,assay="RNA",min.cells=mc,min.features=mg,meta.data=cell.codes["samples"])
  # 
  # #Create groups found in demultiplex.csv
  # for (i in 2:ncol(sc10x.demultiplex)){
  #   Idents(sc10x) <- "samples"
  #   merge.cluster <- apply(sc10x.demultiplex[,i],1,as.character)
  #   merge.cluster[merge.cluster==1] <- colnames(sc10x.demultiplex[,i])
  #   
  #   Idents(sc10x) <- plyr::mapvalues(x=Idents(sc10x),from=sc10x.demultiplex$Samples,to=merge.cluster)
  #   sc10x@meta.data[,colnames(sc10x.demultiplex[,i])] <- Idents(sc10x)
  # }
  
  
  results <- list(
    sc10x=sc10x,
    sc10x.groups=sc10x.groups
  )
  return(results)
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
}


scSubset <- function(sc10x,i="ALL",g="ALL"){
  #Subset cells based on an identity
  
  #Inputs:
  #sc10x = seruat object
  #i = identity to use
  #g = group to subset by
  
  #Outputs:
  #Seurat object
  
  
166
  Idents(sc10x) <- i
167
168
  sc10x.sub <- subset(x=sc10x,idents=g)
  
169
  
170
171
172
173
  return(sc10x.sub)
}


174
scQC <- function(sc10x,sp="hu",feature="nFeature_RNA"){
175
176
177
178
179
180
181
  #QC and filter Seurat object
  
  #Inputs:
  #sc10x = Seruat object
  #sub = Subfolder to save output files
  
  #Outputs:
182
  #result[1] = filtered Seurat object
183
184
185
186
187
188
  #result[2] = raw cell count
  #result[3] = raw gene count
  #result[4] = filtered cell count
  #result[5] = filtered gene count
  
  
189
  #Calculate percent mitochondrea
190
  if (sp=="hu"){
191
    mito.pattern <- "^MT-"
192
    ribo.pattern <- "^(RPL|RPS)"
193
194
  } else if (sp=="mu"){
    mito.pattern <- "^mt-"
195
    ribo.pattern <- "^(Rpl|Rps)"
196
  }
197
198
199
  for (i in names(sc10x)){
    sc10x.temp <- sc10x[[i]]
    sc10x.temp[["percent.mito"]] <- PercentageFeatureSet(object=sc10x.temp,pattern=mito.pattern)
200
    sc10x.temp[["percent.ribo"]] <- PercentageFeatureSet(object=sc10x.temp,pattern=ribo.pattern)
201
    #sc10x.temp <- subset(sc10x.temp,cell=names(which(is.na(sc10x.temp$percent.mito))),invert=TRUE)
202
    sc10x[i] <- sc10x.temp
203
  }
204
  
205
206
  #Calculate cutoffs
  thresh <- list()
Gervaise Henry's avatar
Gervaise Henry committed
207
208
209
  #h <- list()
  #cells.remove <- list()
  #sc10x.temp <- list()
210
  for (i in feature){
Gervaise Henry's avatar
Gervaise Henry committed
211
    if (i == "nFeature_RNA"){
212
213
214
215
216
217
218
219
220
      h <- list()
      cells.remove <- list()
      sc10x.temp <- list()
      for (j in names(sc10x)){
        h[[i]] <- hist(data.frame(sc10x[[j]][[i]])$nFeature_RNA,breaks=10,plot=FALSE)
        cutoff.temp <- mean(c(h[[i]]$mids[which.max(h[[i]]$counts)],h[[i]]$mids[-which.max(h[[i]]$counts)][which.max(h[[i]]$counts[-which.max(h[[i]]$counts)])]))
        cells.remove[[j]] <- c(cells.remove[[j]],rownames(sc10x[[j]][["nFeature_RNA"]])[sc10x[[j]][[i]][,1] < cutoff.temp])
        sc10x.temp[[j]] <- subset(sc10x[[j]],cells=setdiff(colnames(sc10x[[j]]),cells.remove[[j]]))
      }
Gervaise Henry's avatar
Gervaise Henry committed
221
222
    thresh[[i]] <- scThresh(sc10x.temp,feature=i)
    }
223
    if (i == "percent.mito"){
224
      thresh[[i]] <- scThresh(sc10x,feature=i)
Gervaise Henry's avatar
Gervaise Henry committed
225
226
227
228
229
      h <- list()
      cells.remove <- list()
      sc10x.temp <- list()
      thresh.l <- list()
      cutoff.l.mito <- list()
230
231
      for (j in names(sc10x)){
        cutoff.l.mito[[j]] <- NULL
232
233
        temp <- data.frame(sc10x[[j]][[i]])$percent.mito
        h[[i]] <- hist(temp,breaks=100,plot=FALSE)
234
235
        cutoff.temp <- mean(c(h[[i]]$mids[which.max(h[[i]]$counts)],h[[i]]$mids[-which.max(h[[i]]$counts)][which.max(h[[i]]$counts[-which.max(h[[i]]$counts)])]))
        cells.remove[[j]] <- c(cells.remove[[j]],rownames(sc10x[[j]][["percent.mito"]])[sc10x[[j]][[i]][,1] > cutoff.temp])
236
        sc10x.temp[[j]] <- subset(sc10x[[j]],cells=setdiff(colnames(sc10x[[j]]),cells.remove[[j]]))
237
        thresh.l[[i]] <- scThresh(sc10x.temp,feature=i,sub="lower")
Gervaise Henry's avatar
Gervaise Henry committed
238
239
        #cutoff.l.mito[[j]] <- thresh.l[[i]][[j]]$threshold[thresh.l[[i]][[j]]$method=="Triangle"]
        cutoff.l.mito[[j]] <- thresh.l[[i]][[j]]$threshold[thresh.l[[i]][[j]]$method=="RenyiEntropy"]
240
      }
241
    }
242
    if (i == "percent.ribo"){
243
244
245
246
      thresh[[i]] <- scThresh(sc10x,feature=i)
    }
    if (i == "nCount_RNA"){
      thresh[[i]] <- scThresh(sc10x,feature=i)
247
248
249
250
      h <- list()
      cells.remove <- list()
      sc10x.temp <- list()
      thresh.l <- list()
251
      cutoff.l.count <- list()
252
      for (j in names(sc10x)){
253
254
255
256
257
258
259
        cutoff.l.count[[j]] <- NULL
        temp <- data.frame(sc10x[[j]][[i]])$nCount_RNA
        h[[i]] <- hist(temp,breaks=seq(min(temp),max(temp),length.out=6),plot=FALSE)
        cutoff.temp <- mean(c(h[[i]]$mids[which.max(h[[i]]$counts)],h[[i]]$mids[-which.max(h[[i]]$counts)][which.max(h[[i]]$counts[-which.max(h[[i]]$counts)])]))
        cells.remove[[j]] <- c(cells.remove[[j]],rownames(sc10x[[j]][["nCount_RNA"]])[sc10x[[j]][[i]][,1] > cutoff.temp])
        sc10x.temp[[j]] <- subset(sc10x[[j]],cells=setdiff(colnames(sc10x[[j]]),cells.remove[[j]]))
        thresh.l[[i]] <- scThresh(sc10x.temp,feature=i,sub="lower")
260
        cutoff.l.count[[j]] <- thresh.l[[i]][[j]]$threshold[thresh.l[[i]][[j]]$method=="MinErrorI"]
261
262
      }
    }
263
264
265
266
267
268
  }
  
  #Plot raw stats
  max.ct <- 0
  max.ft <- 0
  max.mt <- 0
269
  max.rb <- 0
270
271
272
273
274
275
276
277
278
279
  for (i in names(sc10x)){
    if (max.ct < max(sc10x[[i]][["nCount_RNA"]])){
      max.ct <- max(sc10x[[i]][["nCount_RNA"]])
    }
    if (max.ft < max(sc10x[[i]][["nFeature_RNA"]])){
      max.ft <- max(sc10x[[i]][["nFeature_RNA"]])
    }
    if (max.mt < max(sc10x[[i]][["percent.mito"]])){
      max.mt <- max(sc10x[[i]][["percent.mito"]])
    }
280
281
282
    if (max.rb < max(sc10x[[i]][["percent.ribo"]])){
      max.rb <- max(sc10x[[i]][["percent.ribo"]])
    }
283
284
285
286
  }
  max.ct <- max.ct*1.1
  max.ft <- max.ft*1.1
  max.mt <- max.mt*1.1
287
  max.rb <- max.rb*1.1
288
  cells.remove <- list()
289
  for (i in feature){
290
291
292
293
294
295
296
    max.y <- 0
    if (i == "nCount_RNA"){
      max.y <- max.ct
    } else if (i == "nFeature_RNA"){
      max.y <- max.ft
    } else if (i == "percent.mito"){
      max.y <- max.mt
297
298
    } else if (i == "percent.ribo"){
      max.y <- max.rb
299
300
301
302
303
304
305
    }
    plots.v <- list()
    densities.s <- list()
    plots.s <- list()
    for (j in names(sc10x)){
      sc10x.temp <- sc10x[[j]]
      plots.v[[j]] <- VlnPlot(object=sc10x.temp,features=i,pt.size=0.1,)+scale_x_discrete(labels=j)+scale_y_continuous(limits=c(0,max.y))+theme(legend.position="none",axis.text.x=element_text(hjust=0.5,angle=0))
306
      if (i %in% c("nFeature_RNA","percent.mito","percent.ribo","nCount_RNA")){
307
        if (i == "nFeature_RNA"){
Gervaise Henry's avatar
Gervaise Henry committed
308
          #cutoff.l <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="MinErrorI"]
309
          cutoff.h <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="RenyiEntropy"]
Gervaise Henry's avatar
Gervaise Henry committed
310
311
          cutoff.l <- 200
          #cutoff.h <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="Huang2"]
312
        } else if (i == "percent.mito") {
Gervaise Henry's avatar
Gervaise Henry committed
313
          #cutoff.l <- cutoff.l.mito[[j]]
314
          cutoff.h <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="Triangle"]
Gervaise Henry's avatar
Gervaise Henry committed
315
          cutoff.l <- 0
316
317
318
319
        } else if (i == "percent.ribo") {
          cutoff.h <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="RenyiEntropy"]
          cutoff.l <- 0
        } else if (i == "nCount_RNA") {
320
321
          #cutoff.l <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="MinErrorI"]
          cutoff.l <- cutoff.l.count[[j]]
322
          cutoff.h <- max(sc10x[[j]][[i]])
323
324
        }
        plots.v[[j]] <- plots.v[[j]]+geom_hline(yintercept=cutoff.l,size=0.5,color="red")+geom_hline(yintercept=cutoff.h,size=0.5,color="red")
325
326
327
328
329
330
331
        if (i != "nCount_RNA"){
          densities.s[[j]] <- density(sc10x.temp$nCount_RNA,sc10x.temp[[i]][,1],n=1000)
          plots.s[[j]] <- ggplotGrob(ggplot(data.frame(cbind(sc10x.temp$nCount_RNA,sc10x.temp[[i]][,1])))+geom_point(aes(x=X1,y=X2,color=densities.s[[j]]),size=0.1)+scale_x_continuous(limits=c(0,max.ct))+scale_y_continuous(limits=c(0,max.y))+scale_color_viridis(option="inferno")+labs(x="nCount_RNA",y=i,color="Density")+ggtitle(j)+cowplot::theme_cowplot()+theme(plot.title=element_text(size=7.5),axis.title=element_text(size=7.5),axis.text=element_text(size=5,angle=45),legend.position="bottom",legend.title=element_text(size=5,face="bold",vjust=1),legend.text=element_text(size=5,angle=45))+guides(color=guide_colourbar(barwidth=5,barheight=0.5))+geom_hline(yintercept=cutoff.l,size=0.1,color="red")+geom_hline(yintercept=cutoff.h,size=0.1,color="red"))
        } else {
          densities.s[[j]] <- density(sc10x.temp$nFeature_RNA,sc10x.temp[[i]][,1],n=1000)
          plots.s[[j]] <- ggplotGrob(ggplot(data.frame(cbind(sc10x.temp$nFeature_RNA,sc10x.temp[[i]][,1])))+geom_point(aes(x=X1,y=X2,color=densities.s[[j]]),size=0.1)+scale_x_continuous(limits=c(0,max.ct))+scale_y_continuous(limits=c(0,max.y))+scale_color_viridis(option="inferno")+labs(x="nFeature_RNA",y=i,color="Density")+ggtitle(j)+cowplot::theme_cowplot()+theme(plot.title=element_text(size=7.5),axis.title=element_text(size=7.5),axis.text=element_text(size=5,angle=45),legend.position="bottom",legend.title=element_text(size=5,face="bold",vjust=1),legend.text=element_text(size=5,angle=45))+guides(color=guide_colourbar(barwidth=5,barheight=0.5))+geom_hline(yintercept=cutoff.l,size=0.1,color="red")+geom_hline(yintercept=cutoff.h,size=0.1,color="red"))
        }
332
        cells.remove[[j]] <- c(cells.remove[[j]],rownames(sc10x[[j]][[i]])[sc10x[[j]][[i]][,1] < cutoff.l | sc10x[[j]][[i]][,1] > cutoff.h])
333
      }
Gervaise Henry's avatar
Gervaise Henry committed
334
      ggsave(paste0("./analysis/qc/Violin_qc.raw.",i,".",j,".eps"),plot=plots.v[[j]])
335
      if (i %in% c("nFeature_RNA","percent.mito","percent.ribo","nCount_RNA")){
Gervaise Henry's avatar
Gervaise Henry committed
336
337
        ggsave(paste0("./analysis/qc/Plot_qc.raw.",i,".",j,".eps"),plot=plots.s[[j]])
      }
338
    }
339
  }
340
  
341
342
343
344
345
346
347
348
349
  #Record cell/gene counts pre and post filtering
  counts.cell.raw <- list()
  counts.gene.raw <- list()
  sc10x.sub <- list()
  counts.cell.filtered <- list()
  counts.gene.filtered <- list()
  for (i in names(sc10x)){
    counts.cell.raw[i] <- ncol(GetAssayData(object=sc10x[[i]],slot="counts"))
    counts.gene.raw[i] <- nrow(GetAssayData(object=sc10x[[i]],slot="counts"))
350
    sc10x.sub[[i]] <- subset(sc10x[[i]],cells=setdiff(colnames(sc10x[[i]]),cells.remove[[i]]))
351
352
353
    counts.cell.filtered[i] <- ncol(GetAssayData(object=sc10x.sub[[i]],slot="counts"))
    counts.gene.filtered[i] <- nrow(GetAssayData(object=sc10x.sub[[i]],slot="counts"))
  }
354
355
  
  #Plot filtered stats
356
357
358
  max.ct <- 0
  max.ft <- 0
  max.mt <- 0
359
  max.rb <- 0
360
361
362
363
364
365
366
367
368
369
  for (i in names(sc10x)){
    if (max.ct < max(sc10x.sub[[i]][["nCount_RNA"]])){
      max.ct <- max(sc10x.sub[[i]][["nCount_RNA"]])
    }
    if (max.ft < max(sc10x.sub[[i]][["nFeature_RNA"]])){
      max.ft <- max(sc10x.sub[[i]][["nFeature_RNA"]])
    }
    if (max.mt < max(sc10x.sub[[i]][["percent.mito"]])){
      max.mt <- max(sc10x.sub[[i]][["percent.mito"]])
    }
370
371
372
    if (max.rb < max(sc10x.sub[[i]][["percent.ribo"]])){
      max.rb <- max(sc10x.sub[[i]][["percent.ribo"]])
    }
373
  }
374
375
376
  max.ct <- max.ct*1.1
  max.ft <- max.ft*1.1
  max.mt <- max.mt*1.1
377
378
  max.rb <- max.rb*1.1
  for (i in feature){
379
380
381
382
383
384
385
    max.y <- 0
    if (i == "nCount_RNA"){
      max.y <- max.ct
    } else if (i == "nFeature_RNA"){
      max.y <- max.ft
    } else if (i == "percent.mito"){
      max.y <- max.mt
386
387
    } else if (i == "percent.ribo"){
      max.y <- max.rb
388
389
390
391
392
393
394
    }
    plots.v <- list()
    densities.s <- list()
    plots.s <- list()
    for (j in names(sc10x.sub)){
      sc10x.temp <- sc10x.sub[[j]]
      plots.v[[j]] <- VlnPlot(object=sc10x.temp,features=i,pt.size=0.1,)+scale_x_discrete(labels=j)+scale_y_continuous(limits=c(0,max.y))+theme(legend.position="none",axis.text.x=element_text(hjust=0.5,angle=0))
395
      if (i != "nCount_RNA"){
396
        densities.s[[j]] <- density(sc10x.temp$nCount_RNA,sc10x.temp[[i]][,1],n=1000)
397
398
399
400
        plots.s[[j]] <- ggplotGrob(ggplot(data.frame(cbind(sc10x.temp$nCount_RNA,sc10x.temp[[i]][,1])))+geom_point(aes(x=X1,y=X2,color=densities.s[[j]]),size=0.1)+scale_x_continuous(limits=c(0,max.ct))+scale_y_continuous(limits=c(0,max.y))+scale_color_viridis(option="inferno")+labs(x="nCount_RNA",y=i,color="Density")+ggtitle(j)+cowplot::theme_cowplot()+theme(plot.title=element_text(size=7.5),axis.title=element_text(size=7.5),axis.text=element_text(size=5,angle=45),legend.position="bottom",legend.title=element_text(size=5,face="bold",vjust=1),legend.text=element_text(size=5,angle=45))+guides(color=guide_colourbar(barwidth=5,barheight=0.5)))
      } else {
        densities.s[[j]] <- density(sc10x.temp$nFeature_RNA,sc10x.temp[[i]][,1],n=1000)
        plots.s[[j]] <- ggplotGrob(ggplot(data.frame(cbind(sc10x.temp$nFeature_RNA,sc10x.temp[[i]][,1])))+geom_point(aes(x=X1,y=X2,color=densities.s[[j]]),size=0.1)+scale_x_continuous(limits=c(0,max.ct))+scale_y_continuous(limits=c(0,max.y))+scale_color_viridis(option="inferno")+labs(x="nFeature_RNA",y=i,color="Density")+ggtitle(j)+cowplot::theme_cowplot()+theme(plot.title=element_text(size=7.5),axis.title=element_text(size=7.5),axis.text=element_text(size=5,angle=45),legend.position="bottom",legend.title=element_text(size=5,face="bold",vjust=1),legend.text=element_text(size=5,angle=45))+guides(color=guide_colourbar(barwidth=5,barheight=0.5)))
401
      }
402
      ggsave(paste0("./analysis/qc/Violin_qc.filtered.",i,".",j,".eps"),plot=plots.v[[j]])
403
      if (i %in% c("nFeature_RNA","percent.mito","percent.ribo","nCount_RNA")){
404
405
406
        ggsave(paste0("./analysis/qc/Plot_qc.filtered.",i,".",j,".eps"),plot=plots.s[[j]])
      }

407
408
409
    }
  }
  
410
411
412
413
414
415
416
417
418
419
420
  
  results <- list(
    sc10x=sc10x.sub,
    counts.cell.raw=counts.cell.raw,
    counts.gene.raw=counts.gene.raw,
    counts.cell.filtered=counts.cell.filtered,
    counts.gene.filtered=counts.gene.filtered
  )
  return(results)
}

421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
scThresh <- function(sc10x,feature,sub=FALSE){
  #Calculate thresholds and cutoffs
  
  #Inputs:
  #sc10x = Seruat object
  #feature = feature to threshold
  #sub = Subfolder to save output files
  
  #Outputs:
  #result = Threshold data
  
  
  #Make folders
  if (sub==FALSE){
    folder <- "./analysis/qc/cutoffs/"
  } else {
    folder <- paste0("./analysis/qc/cutoffs/",sub,"/")
    if (!dir.exists(folder)){
      dir.create(folder)
    }
441
442
  }
  
443
444
445
446
447
448
449
  #Calculate range of histogram based threholding and manually select methods for cutoffs
  scale <- list()
  scale.scaled <- list()
  h <- list()
  thresh <-list()
  cutoff.l <- list()
  cutoff.h <- list()
450
  thresh_methods <- c("IJDefault","Huang","Huang2","IsoData","Li","Mean","MinErrorI","Moments","Otsu","Percentile","RenyiEntropy","Shanbhag","Triangle")#,"Intermodes"
451
452
453
454
  for (i in names(sc10x)){
    scale[[i]] <- data.frame(Score=sc10x[[i]][[feature]])
    colnames(scale[[i]]) <- "Score"
    scale[[i]] <- data.frame(Score=scale[[i]]$Score[!is.na(scale[[i]]$Score)])
Gervaise Henry's avatar
Gervaise Henry committed
455
456
457
    scale.scaled[[i]] <- as.integer((scale[[i]]$Score-min(scale[[i]]$Score))/(max(scale[[i]]$Score)-min(scale[[i]]$Score))*360)
    #scale.scaled[[i]] <- as.integer(scales::rescale(scale[[i]]$Score,to=c(0,1))*360)
    h[[i]] <- hist(scale[[i]]$Score,breaks=100,plot=FALSE)
Gervaise Henry's avatar
Gervaise Henry committed
458
    thresh[[i]] <- purrr::map_chr(thresh_methods,~auto_thresh(scale.scaled[[i]],.)) %>% tibble(method = thresh_methods, threshold = .)
459
    thresh[[i]]$threshold <- as.numeric(thresh[[i]]$threshold)
Gervaise Henry's avatar
Gervaise Henry committed
460
461
    thresh[[i]]$threshold <- ((thresh[[i]]$threshold/360)*(max(scale[[i]]$Score)-min(scale[[i]]$Score)))+min(scale[[i]]$Score)
    #thresh[[i]] <- thresh[[i]] %>% mutate(threshold=(scales::rescale(as.numeric(threshold)/360,to=range(scale[[i]]$Score))))
462
463
464
465
466
467
468
469
470
    postscript(paste0(folder,"Hist_qc.",i,".",feature,".eps"))
    plot(h[[i]],main=paste0("Histogram of ",feature," of sample ",i),xlab=feature)
    abline(v=thresh[[i]]$threshold)
    mtext(thresh[[i]]$method,side=1,line=2,at=thresh[[i]]$threshold,cex=0.5)
    dev.off()
  }
  
  
  return(thresh)
471
}
472

473
scCellCycle <- function(sc10x,sub=FALSE,sp="hu"){
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
  #Runs Seurat based PCA analysis for cell cycle ID
  
  #Inputs:
  #sc10x = Seruat object
  #sub = Subfolder to save output files
  
  #Outputs:
  #results[1] = Seurat object
  #results[2] = s genes
  #results[3] = g2m genes
  
  #Make sub-folders if necessary
  if (sub==FALSE){
    folder <- "./analysis/qc/cellcycle/"
  } else {
    folder <- paste0("./analysis/qc/cellcycle/",sub,"/")
    if (!dir.exists(folder)){
      dir.create(folder)
    }}
  
  #score cell cycle
  genes.cc <- readLines(con="./genesets/regev_lab_cell_cycle_genes.txt")
  genes.s <- genes.cc[1:43]
  genes.g2m <- genes.cc[44:97]
  sc10x <- NormalizeData(object=sc10x,verbose=FALSE)
  sc10x <- ScaleData(object=sc10x,do.par=TRUE,num.cores=45,verbose=FALSE)
  sc10x <- CellCycleScoring(object=sc10x,s.features=genes.s,g2m.features=genes.g2m,set.ident=TRUE)
  
  #plot cell cycle specific genes
503
504
505
506
507
508
509
  if (sp=="hu"){
    genes=c("PCNA","TOP2A","MCM6","MKI67")
    postscript(paste0(folder,"Violin_cc.Raw.eps"))
    plot <- VlnPlot(object=sc10x,features=genes,ncol=2,pt.size=1)
    plot(plot)
    dev.off()
  }
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
  
  # sc10x <- RunPCA(object=sc10x,features=c(genes.s,genes.g2m),npcs=2,verbose=FALSE)
  # postscript(paste0(folder,"PCA_cc.Raw.eps"))
  # plot <- DimPlot(object=sc10x,reduction="pca")
  # plot(plot)
  # dev.off()
  # gc()
  # sc10x <- ScaleData(object=sc10x,vars.to.regress=c("S.Score","G2M.Score"),do.par=TRUE,num.cores=45,verbose=TRUE)
  # gc()
  # sc10x <- RunPCA(object=sc10x,features=c(genes.s,genes.g2m),npcs=2,verbose=FALSE)
  # postscript(paste0(folder,"PCA_cc.Norm.eps"))
  # plot <- DimPlot(object=sc10x,reduction="pca")
  # plot(plot)
  # dev.off()
  
  results <- list(
    sc10x=sc10x,
    genes.s=genes.s,
    genes.g2m=genes.g2m
  )
  return(results)
}


534
scPC <- function(sc10x,pc=50,hpc=0.9,file="pre.stress",print="tsne"){
535
536
537
538
539
540
541
542
543
544
545
546
547
548
  #Scale Seurat object & calculate # of PCs to use
  
  #Inputs:
  #sc10x = Seruat object
  #pc = number of PCs to cacluate
  #hpc = max variance cutoff for PCs to use"
  #file = file for output
  
  #Outputs:
  #result[1] = Seurat object
  #result[2] = # of PCs to use
  
  #Run PCA
  Idents(object=sc10x) <- "ALL"
549
  sc10x <- RunPCA(object=sc10x,npcs=pc,verbose=FALSE,assay="integrated")
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
  
  #Calculate PCs to use
  pc.use <- sc10x[["pca"]]@stdev^2
  pc.use <- pc.use/sum(pc.use)
  pc.use <- cumsum(pc.use)
  pc.use <- min(which(pc.use>=hpc))
  
  postscript(paste0("./analysis/qc/Plot_PCElbow_",file,".eps"))
  plot <- ElbowPlot(object=sc10x,ndims=pc)
  plot <- plot+geom_vline(xintercept=pc.use,size=1,color="red")
  plot(plot)
  dev.off()
  
  results <- list(
    sc10x=sc10x,
    pc.use=pc.use
  )
  return(results)
}


571
572
scCCA <-  function(sc10x.l){
  for (i in 1:length(sc10x.l)){
573
    #sc10x.l[[i]] <- NormalizeData(sc10x.l[[i]],verbose=FALSE)
574
    gc()
575
576
    #sc10x.l[[i]] <- ScaleData(sc10x.l[[i]],vars.to.regress=c("nFeature_RNA","percent.mito"),verbose = FALSE)
    sc10x.l[[i]] <- SCTransform(sc10x.l[[i]],vars.to.regress=c("nFeature_RNA","percent.mito"),verbose=FALSE,assay="RNA")
577
    gc()
578
    #sc10x.l[[i]] <- FindVariableFeatures(sc10x.l[[i]],selection.method="vst",nfeatures=2000,verbose=FALSE)
579
580
  }
  
581
582
  sc10x.features <- SelectIntegrationFeatures(object.list=sc10x.l,nfeatures=3000)
  sc10x.l <- PrepSCTIntegration(object.list=sc10x.l,anchor.features=sc10x.features,verbose=FALSE)
583
584
585
586

  sc10x.l <- lapply(sc10x.l,FUN=function(x) { RunPCA(x,features=sc10x.features,verbose=FALSE) })
  
  sc10x.anchors <- FindIntegrationAnchors(object.list=sc10x.l,normalization.method="SCT",anchor.features=sc10x.features,verbose=FALSE,reduction="rpca",dims=1:30)
587
  sc10x <- IntegrateData(anchorset=sc10x.anchors,normalization.method="SCT",verbose=FALSE)
588
  
589
590
591
592
593
594
  #sc10x <- FindIntegrationAnchors(object.list=sc10x.l,dims=1:30,scale=FALSE)
  #sc10x <- IntegrateData(anchorset=sc10x,dims=1:30)
  
  #gc()
  #sc10x <- ScaleData(object=sc10x,do.par=TRUE,num.cores=45,verbose=FALSE,assay="integrated")
  #gc()
595
  
596
  gc()
597
  sc10x <- SCTransform(sc10x,vars.to.regress=c("nFeature_RNA","percent.mito"),verbose=FALSE,return.only.var.genes=FALSE,assay="RNA")
598
  gc()
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
  
  return(sc10x)
}


scCluster <- function(sc10x,res=0.1,red="pca",dim,print="tsne",folder=FALSE){
  #Cluster Seurat object and produce visualizations
  
  #Inputs:
  #sc10x = Seruat object
  #res = resolution to calculate clustering
  #red = rediction type to use for clustering
  #dim = number of dimentions to use for display
  #print = dimentionality reduction to use for display
  #folder = folder for output
  
  #Outputs:
  #result = Seurat object
  
  #Create subfolder if necessary
  if (folder==FALSE){
    sub <- ""
  } else {
    if (!dir.exists(paste0("./analysis/vis/",folder))){
      dir.create(paste0("./analysis/vis/",folder))
    }
    sub <- paste0(folder,"/")
    
  }
  
629
630
  DefaultAssay(sc10x) <- "integrated"

631
  #Calculste Vis
632
633
  sc10x <- RunTSNE(sc10x,dims=1:dim,reduction="pca",assay="integrated")
  sc10x <- RunUMAP(sc10x,dims=1:dim,reduction="pca",assay="integrated")
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
  
  sc10x <- FindNeighbors(sc10x,reduction=red,verbose=FALSE)
  
  for (i in res){
    sc10x <- FindClusters(sc10x,resolution=i,verbose=FALSE)
    
    plot1 <- DimPlot(sc10x,reduction="pca",label=TRUE,repel=TRUE)+theme(legend.position="none")
    plot2 <- DimPlot(sc10x,reduction="tsne",label=TRUE,repel=TRUE)+theme(legend.position="none")
    plot3 <- DimPlot(sc10x,reduction="umap",label=TRUE,repel=TRUE)+theme(legend.position="none")
    
    if (print=="tsne"){
      postscript(paste0("./analysis/vis/",sub,"tSNE_",i,".eps"))
      print(print2)
      dev.off()
    } else if (print=="umap"){
      postscript(paste0("./analysis/vis/",sub,"UMAP_",i,".eps"))
      print(print3)
      dev.off()
    } else if (print=="2"){
      plot2 <- plot2+theme(legend.position="none")
      plot3 <- plot3+theme(legend.position="none")
      postscript(paste0("./analysis/vis/",sub,"2Vis_",i,".eps"))
      grid.arrange(plot2,plot3,ncol=1)
      dev.off()
    } else if (print=="3"){
      plot1 <- plot1+theme(legend.position="none")
      plot2 <- plot2+theme(legend.position="none")
      plot3 <- plot3+theme(legend.position="none")
      postscript(paste0("./analysis/vis/",sub,"3Vis_",i,".eps"))
      grid.arrange(plot1,plot2,plot3,ncol=1)
      dev.off()
    }}
  
Gervaise Henry's avatar
Gervaise Henry committed
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
  for (i in c("samples","HTO_maxID","hashID")[c("samples","HTO_maxID","hash.ID") %in% colnames(sc10x@meta.data)]){
    plot1 <- DimPlot(sc10x,reduction="pca",group.by=i)
    plot2 <- DimPlot(sc10x,reduction="tsne",group.by=i)
    plot3 <- DimPlot(sc10x,reduction="umap",group.by=i)
    legend <- cowplot::get_legend(plot1)
    
    if (print=="tsne"){
      postscript(paste0("./analysis/vis/",sub,"tSNE_",i,".eps"))
      grid.arrange(plot2,legend,ncol=1)
      dev.off()
    } else if (print=="umap"){
      postscript(paste0("./analysis/vis/",sub,"UMAP_",i,".eps"))
      grid.arrange(plot3,legend,ncol=1)
      dev.off()
    } else if (print=="2"){
      plot2 <- plot2+theme(legend.position="none")
      plot3 <- plot3+theme(legend.position="none")
      postscript(paste0("./analysis/vis/",sub,"2Vis_",i,".eps"))
      grid.arrange(plot2,plot3,legend,ncol=1)
      dev.off()
    } else if (print=="3"){
      plot1 <- plot1+theme(legend.position="none")
      plot2 <- plot2+theme(legend.position="none")
      plot3 <- plot3+theme(legend.position="none")
      postscript(paste0("./analysis/vis/",sub,"3Vis_",i,".eps"))
      grid.arrange(plot1,plot2,plot3,legend,ncol=1)
      dev.off()
    }
695
  }
Gervaise Henry's avatar
Gervaise Henry committed
696
  
697
  DefaultAssay(sc10x) <- "SCT"
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
  
  return(sc10x)
}


scScore <- function(sc10x,score,geneset,cut.pt=0.9,anchor=FALSE){
  #Runs custom PCA analysis for stress ID
  
  #Inputs:
  #sc10x = Seruat object
  #score = name of geneset for scoring
  #geneset = geneset to use for ID
  #cut.pt = % of cells to keep
  
  #Outputs:
  #results[1] = Seurat object (original + score)
  #results[2] = Seurat object (negatively filtered)
  #results[3] = Seurat object (positively filtered)
  
  #Make subdirectory
  if (!dir.exists(paste0("./analysis/score_id/",score))){
    dir.create(paste0("./analysis/score_id/",score))
  }
  if (!dir.exists(paste0("./analysis/vis/",score))){
    dir.create(paste0("./analysis/vis/",score))
  }
  
  #Score geneset
726
  sc10x <- AddModuleScore(object=sc10x,features=geneset,name=score,assay="SCT")
727
728
729
730
  Idents(object=sc10x) <- paste0(score,"1")
  
  #CDF
  cdf <- ecdf(as.numeric(levels(sc10x)))
Gervaise Henry's avatar
Gervaise Henry committed
731
  if (cut.pt == "renyi"){
732
733
734
735
736
737

        h <- hist(data.frame(sc10x[[paste0(score,"1")]])[,paste0(score,"1")],breaks=1000,plot=FALSE)
	cutoff.temp <- mean(c(h$mids[which.max(h$counts)],h$mids[-which.max(h$counts)][which.max(h$counts[-which.max(h$counts)])]))
        cells.remove <- rownames(sc10x[[paste0(score,"1")]])[sc10x[[paste0(score,"1")]][,1] < cutoff.temp]
        sc10x.temp <- subset(sc10x,cells=setdiff(colnames(sc10x),cells.remove))
 
738
    thresh <- list()
739
    thresh[["all"]] <- scThresh(list(all=sc10x.temp),feature=paste0(score,"1"),sub=score)
Gervaise Henry's avatar
Gervaise Henry committed
740
    cut.x <- thresh$all$all$threshold[thresh$all$all$method=="RenyiEntropy"]
741
742
743
744
  } else {
    cut.x <- quantile(cdf,probs=cut.pt)
    cut.x <- unname(cut.x)
  }
745
746
747
748
749
750
751
752
753
754
755
756
757
758
  postscript(paste0("./analysis/score_id/",score,"/CDF_",score,".eps"))
  plot(cdf,main=paste0("Cumulative Distribution of ",score," Score"),xlab=paste0(score," Score"),ylab="CDF")
  abline(v=cut.x,col="red")
  dev.off()  
  
  #KDE
  postscript(paste0("./analysis/score_id/",score,"/Histo_",score,".eps"))
  plot(ggplot(data.frame(Score=as.numeric(levels(sc10x))),aes(x=Score))+geom_histogram(bins=100,aes(y=..density..))+geom_density()+geom_vline(xintercept=cut.x,size=1,color="red")+ggtitle(paste0(score," Score"))+cowplot::theme_cowplot())
  dev.off()
  
  Idents(object=sc10x) <- "ALL"
  predicate <- paste0(score,"1 >= ",cut.x)
  Idents(object=sc10x, cells = WhichCells(object=sc10x,expression= predicate)) <- score
  sc10x[[score]] <- Idents(object=sc10x)
759
  Idents(sc10x) <- score
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
  sc10x.negative <- subset(x=sc10x,idents="ALL")
  sc10x.positive <- subset(x=sc10x,idents=score)
  
  #Generate plots
  postscript(paste0("./analysis/vis/",score,"/3Vis_",score,".eps"))
  plot1 <- DimPlot(sc10x,reduction="pca",label=TRUE,repel=TRUE)+theme(legend.position="none")+ggtitle("ALL")
  plot2 <- DimPlot(sc10x.negative,reduction="pca",label=TRUE,repel=TRUE)+theme(legend.position="none")
  plot3 <- DimPlot(sc10x.positive,reduction="pca",label=TRUE,repel=TRUE)+theme(legend.position="none")
  plot4 <- DimPlot(sc10x,reduction="tsne",label=TRUE,repel=TRUE)+theme(legend.position="none")+ggtitle("Negative")
  plot5 <- DimPlot(sc10x.negative,reduction="tsne",label=TRUE,repel=TRUE)+theme(legend.position="none")
  plot6 <- DimPlot(sc10x.positive,reduction="tsne",label=TRUE,repel=TRUE)+theme(legend.position="none")
  plot7 <- DimPlot(sc10x,reduction="umap",label=TRUE,repel=TRUE)+theme(legend.position="none")+ggtitle("Positive")
  plot8 <- DimPlot(sc10x.negative,reduction="umap",label=TRUE,repel=TRUE)+theme(legend.position="none")
  plot9 <- DimPlot(sc10x.positive,reduction="umap",label=TRUE,repel=TRUE)+theme(legend.position="none")
  grid.arrange(plot1,plot2,plot3,plot4,plot5,plot6,plot7,plot8,plot9,ncol=3)
  dev.off()
  
  #Generate violin plot of gene exvpression
  if (anchor!=FALSE){
    postscript(paste0("./analysis/score_id/",score,"/Violin_",score,".eps"))
780
    plot <- VlnPlot(object=sc10x,features=anchor,pt.size=0.1,assay="SCT")
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
    plot(plot)
    dev.off()
  }
  
  results <- list(
    sc10x <- sc10x,
    sc10x.negative <- sc10x.negative,
    sc10x.positive <- sc10x.positive
  )
  return(results)
}


scQuSAGE <- function(sc10x,gs,save=FALSE,type,id,ds=0,nm="pops",print="tsne"){
  #Runs QuSAGE
  
  #Inputs:
  #sc10x = Seruat object
  #gs = geneset to use for correlation
  #save = save ID
  #type = type of qusage to run (id: create ID's based on run, sm: cor only using small genesets, lg: cor only with large genesets)
  #id = ident to use
  #nm = name of test
  #print = dimentionality reduction to use for display
  
  #Outputs:
  #results[1] = Seurat object
  #results[2] = correlation table
  #results[3] = correlation results
  
  if (!dir.exists(paste0("./analysis/cor/",nm))){
    dir.create(paste0("./analysis/cor/",nm))
  }
  if (!dir.exists(paste0("./analysis/cor/",nm,"/geneset"))){
    dir.create(paste0("./analysis/cor/",nm,"/geneset"))
  }
  if (!dir.exists(paste0("./analysis/cor/",nm,"/cluster"))){
    dir.create(paste0("./analysis/cor/",nm,"/cluster"))
  }
  if (!dir.exists(paste0("./analysis/vis/",nm))){
    dir.create(paste0("./analysis/vis/",nm))
  }
  
  Idents(object=sc10x) <- id
  number.clusters <- length(unique(levels(x=sc10x)))
  
  labels <- paste0("Cluster_",as.vector(Idents(object=sc10x)))
  
  cell.sample <- NULL
  for (i in unique(labels)){
    cell <- WhichCells(sc10x,ident=sub("Cluster_","",i))
    if (length(cell)>ds & ds!=0){
833
      set.seed(71682)
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
      rnd <- sample(1:length(cell),ds)
      cell <- cell[rnd]
    }
    cell.sample <- c(cell.sample,cell)
  }
  data <- as.data.frame(as.matrix(GetAssayData(sc10x[,colnames(sc10x) %in% cell.sample])))
  labels <- labels[colnames(sc10x) %in% cell.sample]
  groups <- sort(unique(labels))
  
  col <- hcl(h=(seq(15,375-375/length(groups),length=length(groups))),c=100,l=65)
  
  #Make labels for QuSAGE
  clust <- list()
  clust.comp <- list()
  for (i in groups){
    t <- labels
    t[t != i] <- "REST"
    clust[i] <- list(i=t)
    rm(t)
    clust.comp[i] <- paste0(i,"-REST")
  }
  
  #Run QuSAGE
  for (i in groups){
    assign(paste0("results.",i),qusage(data,unlist(clust[i]),unlist(clust.comp[i]),gs))
  }
  
  #Generate ID table
  results.cor <- NULL
  results.cor <- qsTable(get(paste0("results.",groups[1])),number=length(gs))
  results.cor$Cluster <- groups[1]
  for (i in groups[-1]){
    qs <- qsTable(get(paste0("results.",i)),number=length(gs))
    qs$Cluster <- i
    results.cor <- rbind(results.cor,qs)
  }
  results.cor <- results.cor[,-3]
  rownames(results.cor) <- NULL
  
  results.clust.id <- NULL
874
875
876
877
  #if (max(results.cor[results.cor[,4]==groups[1] & results.cor[,3]<=0.05,][,2],na.rm=TRUE)>=0){
  #  results.clust.id <- results.cor[results.cor[,4]==groups[1] & results.cor[,3]<=0.05,][which.max(results.cor[results.cor[,4]==groups[1] & results.cor[,3]<=0.05,][,2]),]
  if (max(results.cor[results.cor[,4]==groups[1],][,2],na.rm=TRUE)>=0){
    results.clust.id <- results.cor[results.cor[,4]==groups[1],][which.max(results.cor[results.cor[,4]==groups[1],][,2]),]
878
879
880
881
882
883
884
885
  } else {
    results.clust.id$pathway.name <- "Unknown"
    results.clust.id$log.fold.change <- 0
    results.clust.id$FDR <- 0
    results.clust.id$Cluster <- groups[1]
    results.clust.id <- as.data.frame(results.clust.id)
  }
  for (i in groups[-1]){
886
887
888
889
    #if (max(results.cor[results.cor[,4]==i & results.cor[,3]<=0.05,][,2],na.rm=TRUE)>=0){
    #  results.clust.id <- rbind(results.clust.id,results.cor[results.cor[,4]==i & results.cor[,3]<=0.05,][which.max(results.cor[results.cor[,4]==i & results.cor[,3]<=0.05,][,2]),])
    if (max(results.cor[results.cor[,4]==i,][,2],na.rm=TRUE)>=0){
      results.clust.id <- rbind(results.clust.id,results.cor[results.cor[,4]==i,][which.max(results.cor[results.cor[,4]==i,][,2]),])
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
    } else {
      results.clust.id <- rbind(results.clust.id,data.frame(pathway.name="Unknown",log.fold.change=0,FDR=0,Cluster=i))
    }}
  rownames(results.clust.id) <- NULL
  
  #Determine axes for correlation plots
  max.x.rg <- 0
  min.x.rg <- 0
  max.y.rg <- 0
  for (i in groups){
    qs <- get(paste0("results.",i))
    if (max(qs$path.mean)>max.x.rg){
      max.x.rg <- max(qs$path.mean)
    }
    if (min(qs$path.mean)<min.x.rg){
      min.x.rg <- min(qs$path.mean)
    }
    if (max(qs$path.PDF)>max.y.rg){
      max.y.rg <- max(qs$path.PDF)
    }}
  if (type=="sm"){
911
912
913
914
915
916
917
918
919
920
921
922
923
924
    #Plot correlation plots by geneset
    for (i in 1:length(gs)){
      postscript(paste0("./analysis/cor/",nm,"/geneset/QuSAGE_",nm,".",names(gs)[i],".eps"))
      for (j in groups){
        qs <- get(paste0("results.",j))
        if (j==groups[1]){
          plotDensityCurves(qs,path.index=i,col=col[match(j,groups)],main=names(gs)[i],xlim=c(min.x.rg-0.05,max.x.rg+0.05),ylim=c(0,50*ceiling(max.y.rg/50)),xlab="Gene Set Activation",lwd=5,cex.main=2.5,cex.axis=1.5,cex.lab=2)
        } else {
          plotDensityCurves(qs,path.index=i,add=TRUE,col=col[match(j,groups)],lwd=5)
        }}
      legend("topright",col=col,legend=groups,lty=1,lwd=5,cex=2,ncol=1,bty="n",pt.cex=2)
      box(lwd=5)
      dev.off()
    }
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
    #Plot correlation plots by cluster
    for (i in groups){
      qs <- get(paste0("results.",i))
      postscript(paste0("./analysis/cor/",nm,"/cluster/QuSAGE_",nm,"_",i,".eps"))
      for (j in 1:length(gs)){
        if (j==1){
          plotDensityCurves(qs,path.index=j,col=viridis(length(gs))[j],main=i,xlim=c(min.x.rg-0.05,max.x.rg+0.05),ylim=c(0,50*ceiling(max.y.rg/50)),xlab="Gene Set Activation",lwd=5,cex.main=2.5,cex.axis=1.5,cex.lab=2)
        } else {
          plotDensityCurves(qs,path.index=j,add=TRUE,col=viridis(length(gs))[j],lwd=5)
        }}
      legend("topright",col=viridis(length(gs)),legend=names(gs),lty=1,lwd=5,cex=1,ncol=2,bty="n",pt.cex=2)
      box(lwd=5)
      dev.off()
    }} else {
      for (i in groups){
        qs <- get(paste0("results.",i))
        postscript(paste0("./analysis/cor/",nm,"/cluster/QuSAGE_",nm,"_",i,".eps"))
        plotCIs(qs,path.index=1:numPathways(qs),cex.lab=1.5)
        dev.off()
      }}
  
  if (save==TRUE){
    merge.cluster <- NULL
    for (i in groups){
949
950
951
952
      #if (max(qsTable(get(paste0("results.",i)),number=length(gs))[qsTable(get(paste0("results.",i)),number=length(gs))[,4]<=0.05,][,2],na.rm=TRUE)>=0){
      #  sc10x<-eval(parse(text=paste0("RenameIdents(object=sc10x,'",sub("Cluster_","",i),"' = '",qsTable(get(paste0("results.",i)),number=length(gs))[qsTable(get(paste0("results.",i)),number=length(gs))[2]==max(qsTable(get(paste0("results.",i)),number=length(gs))[qsTable(get(paste0("results.",i)),number=length(gs))[,4]<=0.05,][,2],na.rm=TRUE)][1],"')")))
      if (max(qsTable(get(paste0("results.",i)),number=length(gs))[,2],na.rm=TRUE)>=0){
        sc10x<-eval(parse(text=paste0("RenameIdents(object=sc10x,'",sub("Cluster_","",i),"' = '",qsTable(get(paste0("results.",i)),number=length(gs))[qsTable(get(paste0("results.",i)),number=length(gs))[2]==max(qsTable(get(paste0("results.",i)),number=length(gs))[,2],na.rm=TRUE)][1],"')")))
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
      } else {
        sc10x<-eval(parse(text=paste0("RenameIdents(object=sc10x,'",sub("Cluster_","",i),"' = 'Unknown')")))
      }}
    sc10x[[nm]] <- Idents(object=sc10x)
  }
  
  plot1 <- DimPlot(sc10x,reduction="pca",label=TRUE,repel=TRUE)+theme(legend.position="none")
  plot2 <- DimPlot(sc10x,reduction="tsne",label=TRUE,repel=TRUE)+theme(legend.position="none")
  plot3 <- DimPlot(sc10x,reduction="umap",label=TRUE,repel=TRUE)+theme(legend.position="none")
  if (print=="tsne"){
    postscript(paste0("./analysis/vis/",nm,"/tSNE_",id,"_",nm,".eps"))
    print(print2)
    dev.off()
  } else if (print=="umap"){
    postscript(paste0("./analysis/vis/",nm,"/UMAP_",id,"_",nm,".eps"))
    print(print3)
    dev.off()
  } else if (print=="2"){
    postscript(paste0("./analysis/vis/",nm,"/2Vis_",id,"_",nm,".eps"))
    grid.arrange(plot2,plot3,ncol=1)
    dev.off()
  } else if (print=="3"){
    postscript(paste0("./analysis/vis/",nm,"/3Vis_",id,"_",nm,".eps"))
    grid.arrange(plot1,plot2,plot3,ncol=1)
    dev.off()
  }
  
  results <- list(
    sc10x=sc10x,
    results.cor=results.cor,
    results.clust.id=results.clust.id
  )
  names(results)=c("sc10x",paste0("results.cor.",nm),paste0("results.clust.",nm,".id"))
  return(results)
}
988

989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
scShinyOutput <- function(sc10x,anal="raw"){
  write_delim(as.data.frame(colnames(sc10x)),path=paste0("./analysis/shiny/",anal,"/outs/filtered_feature_bc_matrix/barcodes.tsv.gz"),delim="\t",col_names=FALSE)
  features <- rownames(sc10x)
  features <- c(features,c("nFeature","nCount","percent.mito","percent.ribo","Stress.score"))
  features <- data.frame(ENSG=features,Feature=features,Label="feature")
  write_delim(features,path=paste0("./analysis/shiny/",anal,"/outs/filtered_feature_bc_matrix/features.tsv.gz"),delim="\t",col_names=FALSE)
  exp <- GetAssayData(sc10x,slot="scale.data")
  exp.extra <- matrix(nrow=5,ncol=ncol(sc10x))
  exp.extra[1,] <- as.numeric(sc10x$nFeature_RNA)
  exp.extra[2,] <- as.numeric(sc10x$nCount_RNA)
  exp.extra[3,] <- as.numeric(sc10x$percent.mito)
  exp.extra[4,] <- as.numeric(sc10x$percent.ribo)
  exp.extra[5,] <- as.numeric(sc10x$Stress1)
  exp <- rbind(exp,exp.extra)
Gervaise Henry's avatar
Gervaise Henry committed
1003
  Matrix::writeMM(as(exp,"dgCMatrix"),file=paste0("./analysis/shiny/",anal,"/outs/filtered_feature_bc_matrix/matrix.mtx.gz"))
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
  for (i in c("pca","tsne","umap")){
    dr <- Embeddings(sc10x,i)
    if (i != "pca"){
      colnames(dr) <- c(paste0(toupper(i),"-",1:2))
    } else {
      dr <- dr[,1:10]
      colnames(dr) <- c(paste0(toupper(i),"-",1:10))
    }
    dr <- cbind(dr,Barcode=rownames(dr))
    dr <- dr[,c(3,1,2)]
    dr <- as.data.frame(dr,row.names=FALSE)
    if (i != "pca"){
1016
      write_csv(dr,paste0("./analysis/shiny/",anal,"/outs/analysis/",i,"/2_components/projection.csv"),col_names=TRUE)
1017
    } else {
1018
      write_csv(dr,paste0("./analysis/shiny/",anal,"/outs/analysis/",i,"/10_components/projection.csv"),col_names=TRUE)
1019
1020
    }
  }
1021
  sc10x <- NormalizeData(sc10x,assay="RNA")
Gervaise Henry's avatar
Gervaise Henry committed
1022
  clusters <- c("samples","samples_HTO",paste0("integrated_snn_res.",res),"lin","pops","leu","scDWSpr","HTO_maxID","hash.ID")
1023
1024
  clusters <- intersect(clusters,names(sc10x@meta.data))
  for (i in clusters){
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
    if (nrow(unique(sc10x[[i]]))>1){
      if (!dir.exists(paste0("./analysis/shiny/",anal,"/outs/analysis/clustering/",gsub("integrated_snn_res.","res_",i)))){
        dir.create(paste0("./analysis/shiny/",anal,"/outs/analysis/clustering/",gsub("integrated_snn_res.","res_",i)))
      }
      clust <- as.matrix(sc10x[[i]])
      colnames(clust) <- "Cluster"
      clust <- cbind(clust,Barcode=rownames(clust))
      clust <- clust[,c(2,1)]
      clust <- as.data.frame(clust,row.names=FALSE)
      clust[,2] <- paste0("Cluster ",clust[,2])
      write_csv(clust,paste0("./analysis/shiny/",anal,"/outs/analysis/clustering/",gsub("integrated_snn_res.","res_",i),"/clusters.csv"),col_names=TRUE)
  
      if (!dir.exists(paste0("./analysis/shiny/",anal,"/outs/analysis/diffexp/",gsub("integrated_snn_res.","res_",i)))){
        dir.create(paste0("./analysis/shiny/",anal,"/outs/analysis/diffexp/",gsub("integrated_snn_res.","res_",i)))
      }
      Idents(sc10x) <- i
      deg <- FindAllMarkers(sc10x,assay="RNA",slot="data",logfc.threshold=0,test.use="MAST",min.pct=0.25,min.diff.pct=0.25,max.cells.per.ident=500)
      dexp <- data.frame("Feature ID"=unique(deg$gene),"Feature Name"=unique(deg$gene))
      for (cluster in unique(deg$cluster)){
        dexp[,paste0("Cluster.",cluster,".Mean.Counts")] <- deg$pct.1[deg$cluster==cluster][match(dexp$Feature.ID,deg$gene[deg$cluster==cluster])]
        dexp[,paste0("Cluster.",cluster,".Log2.fold.change")] <- deg$avg_logFC[deg$cluster==cluster][match(dexp$Feature.ID,deg$gene[deg$cluster==cluster])]
        dexp[,paste0("Cluster.",cluster,".Adjusted.p.value")] <- deg$p_val_adj[deg$cluster==cluster][match(dexp$Feature.ID,deg$gene[deg$cluster==cluster])]
      }
      colnames(dexp) <- gsub("\\."," ",colnames(dexp))
      write_csv(dexp,paste0("./analysis/shiny/",anal,"/outs/analysis/diffexp/",gsub("integrated_snn_res.","res_",i),"/differential_expression.csv"),col_names=TRUE)
1050
    }
1051
  }
1052
}