sc-TissueMapper_functions.R 39.3 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
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
  if (!dir.exists("./analysis/shiny")){
    dir.create("./analysis/shiny")
  }
  if (!dir.exists("./analysis/shiny")){
    dir.create("./analysis/shiny")
  }
  if (!dir.exists("./analysis/shiny/raw")){
    dir.create("./analysis/shiny/raw")
  }
  if (!dir.exists("./analysis/shiny/raw/outs")){
    dir.create("./analysis/shiny/raw/outs")
  }
  if (!dir.exists("./analysis/shiny/raw/outs/analysis")){
    dir.create("./analysis/shiny/raw/outs/analysis")
  }
  if (!dir.exists("./analysis/shiny/raw/outs/analysis/clustering")){
    dir.create("./analysis/shiny/raw/outs/analysis/clustering")
  }
  if (!dir.exists("./analysis/shiny/raw/outs/analysis/diffexp")){
    dir.create("./analysis/shiny/raw/outs/analysis/diffexp")
  }
  if (!dir.exists("./analysis/shiny/raw/outs/analysis/pca")){
    dir.create("./analysis/shiny/raw/outs/analysis/pca")
  }
  if (!dir.exists("./analysis/shiny/raw/outs/analysis/pca/10_components")){
    dir.create("./analysis/shiny/raw/outs/analysis/pca/10_components")
  }
  if (!dir.exists("./analysis/shiny/raw/outs/analysis/tsne")){
    dir.create("./analysis/shiny/raw/outs/analysis/tsne")
  }
  if (!dir.exists("./analysis/shiny/raw/outs/analysis/tsne/2_components")){
    dir.create("./analysis/shiny/raw/outs/analysis/tsne/2_components")
  }
  if (!dir.exists("./analysis/shiny/raw/outs/analysis/umap")){
    dir.create("./analysis/shiny/raw/outs/analysis/umap")
  }
  if (!dir.exists("./analysis/shiny/raw/outs/analysis/umap/2_components")){
    dir.create("./analysis/shiny/raw/outs/analysis/umap/2_components")
  }
68
69
70
}


Gervaise Henry's avatar
Gervaise Henry committed
71
scLoad <- function(p,cellranger=3,aggr=TRUE,ncell=0,nfeat=0){
72
73
74
75
  #Load and prefilter filtered_gene_bc_matrices_mex output from cellranger
  
  #Inputs:
  #p = project name
76
77
  #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
78
79
  
  #Outputs:
80
81
  #sc10x = Seurat object list
  #sc10x.groups = group labels for each sample
82
83
  
  
84
85
86
87
88
89
90
  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){
91
    if (cellranger==2){
92
      sc10x.data[aggr] <- Read10X(data.dir=paste0("./analysis/DATA/10x/filtered_gene_bc_matrices_mex/"))
93
    } else {
94
      sc10x.data[aggr] <- Read10X(data.dir=paste0("./analysis/DATA/10x/filtered_feature_bc_matrix/"))
95
    }
96
    sc10x[aggr] <- new("seurat",raw.data=sc10x.data[aggr])
97
  } else {
98
99
100
101
102
103
    for (i in sc10x.groups$Samples){
      if (cellranger==2){
        sc10x.data[i] <- Read10X(data.dir=paste0("./analysis/DATA/10x/",i,"/filtered_gene_bc_matrices/"))
      } else {
        sc10x.data[i] <- Read10X(data.dir=paste0("./analysis/DATA/10x/",i,"/filtered_feature_bc_matrix/"))
      }
Gervaise Henry's avatar
Gervaise Henry committed
104
      sc10x[i] <- CreateSeuratObject(counts=sc10x.data[[i]],project=p,min.cells=ncell,min.features=nfeat)
105
      sc10x[[i]]$samples <- i
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
  # #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)
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
}


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
  
  
161
  Idents(sc10x) <- i
162
163
  sc10x.sub <- subset(x=sc10x,idents=g)
  
164
  
165
166
167
168
  return(sc10x.sub)
}


169
scQC <- function(sc10x,sp="hu",feature="nFeature_RNA"){
170
171
172
173
174
175
176
  #QC and filter Seurat object
  
  #Inputs:
  #sc10x = Seruat object
  #sub = Subfolder to save output files
  
  #Outputs:
177
  #result[1] = filtered Seurat object
178
179
180
181
182
183
  #result[2] = raw cell count
  #result[3] = raw gene count
  #result[4] = filtered cell count
  #result[5] = filtered gene count
  
  
184
  #Calculate percent mitochondrea
185
  if (sp=="hu"){
186
    mito.pattern <- "^MT-"
187
    ribo.pattern <- "^(RPL|RPS)"
188
189
  } else if (sp=="mu"){
    mito.pattern <- "^mt-"
190
    ribo.pattern <- "^(Rpl|Rps)"
191
  }
192
193
194
  for (i in names(sc10x)){
    sc10x.temp <- sc10x[[i]]
    sc10x.temp[["percent.mito"]] <- PercentageFeatureSet(object=sc10x.temp,pattern=mito.pattern)
195
    sc10x.temp[["percent.ribo"]] <- PercentageFeatureSet(object=sc10x.temp,pattern=ribo.pattern)
196
    #sc10x.temp <- subset(sc10x.temp,cell=names(which(is.na(sc10x.temp$percent.mito))),invert=TRUE)
197
    sc10x[i] <- sc10x.temp
198
  }
199
  
200
201
  #Calculate cutoffs
  thresh <- list()
Gervaise Henry's avatar
Gervaise Henry committed
202
203
204
  #h <- list()
  #cells.remove <- list()
  #sc10x.temp <- list()
205
  for (i in feature){
Gervaise Henry's avatar
Gervaise Henry committed
206
    if (i == "nFeature_RNA"){
207
208
209
210
211
212
213
214
215
      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]]$counts)])]))
        cells.remove[[j]] <- c(cells.remove[[j]],rownames(sc10x[[j]][["nFeature_RNA"]])[sc10x[[j]][[i]][,1] < cutoff.temp])
        sc10x.temp[[j]] <- subset(sc10x[[j]],cells=setdiff(colnames(sc10x[[j]]),cells.remove[[j]]))
      }
Gervaise Henry's avatar
Gervaise Henry committed
216
217
    thresh[[i]] <- scThresh(sc10x.temp,feature=i)
    }
218
    if (i == "percent.mito"){
Gervaise Henry's avatar
Gervaise Henry committed
219
220
221
222
223
      h <- list()
      cells.remove <- list()
      sc10x.temp <- list()
      thresh.l <- list()
      cutoff.l.mito <- list()
224
225
      for (j in names(sc10x)){
        cutoff.l.mito[[j]] <- NULL
Gervaise Henry's avatar
Gervaise Henry committed
226
        h[[i]] <- hist(data.frame(sc10x[[j]][[i]])$percent.mito,breaks=100,plot=FALSE)
227
228
        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])
229
        sc10x.temp[[j]] <- subset(sc10x[[j]],cells=setdiff(colnames(sc10x[[j]]),cells.remove[[j]]))
230
        thresh.l[[i]] <- scThresh(sc10x.temp,feature=i,sub="lower")
Gervaise Henry's avatar
Gervaise Henry committed
231
232
233
        #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"]
        thresh[[i]] <- scThresh(sc10x,feature=i)
234
      }
235
    }
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
    if (i == "percent.ribo"){
      h <- list()
      cells.remove <- list()
      sc10x.temp <- list()
      thresh.l <- list()
      cutoff.l.ribo <- list()
      for (j in names(sc10x)){
        cutoff.l.ribo[[j]] <- NULL
        h[[i]] <- hist(data.frame(sc10x[[j]][[i]])$percent.ribo,breaks=100,plot=FALSE)
        thresh[[i]] <- scThresh(sc10x,feature=i)
      }
    }
    if (i == "nCount_RNA"){
      thresh[[i]] <- scThresh(sc10x,feature=i)
    }
251
252
253
254
255
256
  }
  
  #Plot raw stats
  max.ct <- 0
  max.ft <- 0
  max.mt <- 0
257
  max.rb <- 0
258
259
260
261
262
263
264
265
266
267
  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"]])
    }
268
269
270
    if (max.rb < max(sc10x[[i]][["percent.ribo"]])){
      max.rb <- max(sc10x[[i]][["percent.ribo"]])
    }
271
272
273
274
  }
  max.ct <- max.ct*1.1
  max.ft <- max.ft*1.1
  max.mt <- max.mt*1.1
275
  max.rb <- max.rb*1.1
276
  cells.remove <- list()
277
  for (i in feature){
278
279
280
281
282
283
284
    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
285
286
    } else if (i == "percent.ribo"){
      max.y <- max.rb
287
288
289
290
291
292
293
    }
    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))
294
      if (i %in% c("nFeature_RNA","percent.mito","percent.ribo","nCount_RNA")){
295
        if (i == "nFeature_RNA"){
Gervaise Henry's avatar
Gervaise Henry committed
296
          #cutoff.l <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="MinErrorI"]
297
          cutoff.h <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="RenyiEntropy"]
Gervaise Henry's avatar
Gervaise Henry committed
298
299
          cutoff.l <- 200
          #cutoff.h <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="Huang2"]
300
        } else if (i == "percent.mito") {
Gervaise Henry's avatar
Gervaise Henry committed
301
          #cutoff.l <- cutoff.l.mito[[j]]
302
          cutoff.h <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="Triangle"]
Gervaise Henry's avatar
Gervaise Henry committed
303
          cutoff.l <- 0
304
305
306
307
308
309
        } else if (i == "percent.ribo") {
          cutoff.h <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="RenyiEntropy"]
          cutoff.l <- 0
        } else if (i == "nCount_RNA") {
          cutoff.l <- thresh[[i]][[j]]$threshold[thresh[[i]][[j]]$method=="Triangle"]
          cutoff.h <- max(sc10x[[j]][[i]])
310
311
        }
        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")
312
313
314
315
316
317
318
        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"))
        }
319
        cells.remove[[j]] <- c(cells.remove[[j]],rownames(sc10x[[j]][[i]])[sc10x[[j]][[i]][,1] < cutoff.l | sc10x[[j]][[i]][,1] > cutoff.h])
320
      }
Gervaise Henry's avatar
Gervaise Henry committed
321
      ggsave(paste0("./analysis/qc/Violin_qc.raw.",i,".",j,".eps"),plot=plots.v[[j]])
322
      if (i %in% c("nFeature_RNA","percent.mito","percent.ribo","nCount_RNA")){
Gervaise Henry's avatar
Gervaise Henry committed
323
324
        ggsave(paste0("./analysis/qc/Plot_qc.raw.",i,".",j,".eps"),plot=plots.s[[j]])
      }
325
    }
326
  }
327
  
328
329
330
331
332
333
334
335
336
  #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"))
337
    sc10x.sub[[i]] <- subset(sc10x[[i]],cells=setdiff(colnames(sc10x[[i]]),cells.remove[[i]]))
338
339
340
    counts.cell.filtered[i] <- ncol(GetAssayData(object=sc10x.sub[[i]],slot="counts"))
    counts.gene.filtered[i] <- nrow(GetAssayData(object=sc10x.sub[[i]],slot="counts"))
  }
341
342
  
  #Plot filtered stats
343
344
345
  max.ct <- 0
  max.ft <- 0
  max.mt <- 0
346
  max.rb <- 0
347
348
349
350
351
352
353
354
355
356
  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"]])
    }
357
358
359
    if (max.rb < max(sc10x.sub[[i]][["percent.ribo"]])){
      max.rb <- max(sc10x.sub[[i]][["percent.ribo"]])
    }
360
  }
361
362
363
  max.ct <- max.ct*1.1
  max.ft <- max.ft*1.1
  max.mt <- max.mt*1.1
364
365
  max.rb <- max.rb*1.1
  for (i in feature){
366
367
368
369
370
371
372
    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
373
374
    } else if (i == "percent.ribo"){
      max.y <- max.rb
375
376
377
378
379
380
381
    }
    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))
382
      if (i != "nCount_RNA"){
383
        densities.s[[j]] <- density(sc10x.temp$nCount_RNA,sc10x.temp[[i]][,1],n=1000)
384
385
386
387
        plots.s[[j]] <- ggplotGrob(ggplot(data.frame(cbind(sc10x.temp$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)))
388
      }
389
      ggsave(paste0("./analysis/qc/Violin_qc.filtered.",i,".",j,".eps"),plot=plots.v[[j]])
390
      if (i %in% c("nFeature_RNA","percent.mito","percent.ribo","nCount_RNA")){
391
392
393
        ggsave(paste0("./analysis/qc/Plot_qc.filtered.",i,".",j,".eps"),plot=plots.s[[j]])
      }

394
395
396
    }
  }
  
397
398
399
400
401
402
403
404
405
406
407
  
  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)
}

408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
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)
    }
428
429
  }
  
430
431
432
433
434
435
436
  #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()
437
  thresh_methods <- c("IJDefault","Huang","Huang2","IsoData","Li","Mean","MinErrorI","Moments","Otsu","Percentile","RenyiEntropy","Shanbhag","Triangle")#,"Intermodes"
438
439
440
441
  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
442
443
444
    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
445
    thresh[[i]] <- purrr::map_chr(thresh_methods,~auto_thresh(scale.scaled[[i]],.)) %>% tibble(method = thresh_methods, threshold = .)
446
    thresh[[i]]$threshold <- as.numeric(thresh[[i]]$threshold)
Gervaise Henry's avatar
Gervaise Henry committed
447
448
    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))))
449
450
451
452
453
454
455
456
457
    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)
458
}
459

460
scCellCycle <- function(sc10x,sub=FALSE,sp="hu"){
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
  #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
490
491
492
493
494
495
496
  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()
  }
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
  
  # 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)
}


521
scPC <- function(sc10x,pc=50,hpc=0.9,file="pre.stress",print="tsne"){
522
523
524
525
526
527
528
529
530
531
532
533
534
535
  #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"
536
  sc10x <- RunPCA(object=sc10x,npcs=pc,verbose=FALSE,assay="integrated")
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
  
  #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)
}


558
559
scCCA <-  function(sc10x.l){
  for (i in 1:length(sc10x.l)){
560
    #sc10x.l[[i]] <- NormalizeData(sc10x.l[[i]],verbose=FALSE)
561
    gc()
562
563
    #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")
564
    gc()
565
    #sc10x.l[[i]] <- FindVariableFeatures(sc10x.l[[i]],selection.method="vst",nfeatures=2000,verbose=FALSE)
566
567
  }
  
568
569
  sc10x.features <- SelectIntegrationFeatures(object.list=sc10x.l,nfeatures=3000)
  sc10x.l <- PrepSCTIntegration(object.list=sc10x.l,anchor.features=sc10x.features,verbose=FALSE)
570
571
572
573

  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)
574
  sc10x <- IntegrateData(anchorset=sc10x.anchors,normalization.method="SCT",verbose=FALSE)
575
  
576
577
578
579
580
581
  #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()
582
  
583
  gc()
584
  sc10x <- SCTransform(sc10x,vars.to.regress=c("nFeature_RNA","percent.mito"),verbose=FALSE,return.only.var.genes=FALSE,assay="RNA")
585
  gc()
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
  
  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,"/")
    
  }
  
616
617
  DefaultAssay(sc10x) <- "integrated"

618
  #Calculste Vis
619
620
  sc10x <- RunTSNE(sc10x,dims=1:dim,reduction="pca",assay="integrated")
  sc10x <- RunUMAP(sc10x,dims=1:dim,reduction="pca",assay="integrated")
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
  
  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()
    }}
  
  plot1 <- DimPlot(sc10x,reduction="pca",group.by="samples")
  plot2 <- DimPlot(sc10x,reduction="tsne",group.by="samples")
  plot3 <- DimPlot(sc10x,reduction="umap",group.by="samples")
  legend <- cowplot::get_legend(plot1)
658
  
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
  if (print=="tsne"){
    postscript(paste0("./analysis/vis/",sub,"tSNE_samples.eps"))
    grid.arrange(plot2,legend,ncol=1)
    dev.off()
  } else if (print=="umap"){
    postscript(paste0("./analysis/vis/",sub,"UMAP_samples.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_samples.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_samples.eps"))
    grid.arrange(plot1,plot2,plot3,legend,ncol=1)
    dev.off()
  }
681
682

  DefaultAssay(sc10x) <- "SCT"
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
  
  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
711
  sc10x <- AddModuleScore(object=sc10x,features=geneset,name=score,assay="SCT")
712
713
714
715
  Idents(object=sc10x) <- paste0(score,"1")
  
  #CDF
  cdf <- ecdf(as.numeric(levels(sc10x)))
Gervaise Henry's avatar
Gervaise Henry committed
716
  if (cut.pt == "renyi"){
717
718
719
720
721
722

        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))
 
723
    thresh <- list()
724
    thresh[["all"]] <- scThresh(list(all=sc10x.temp),feature=paste0(score,"1"),sub=score)
Gervaise Henry's avatar
Gervaise Henry committed
725
    cut.x <- thresh$all$all$threshold[thresh$all$all$method=="RenyiEntropy"]
726
727
728
729
  } else {
    cut.x <- quantile(cdf,probs=cut.pt)
    cut.x <- unname(cut.x)
  }
730
731
732
733
734
735
736
737
738
739
740
741
742
743
  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)
744
  Idents(sc10x) <- score
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
  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"))
765
    plot <- VlnPlot(object=sc10x,features=anchor,pt.size=0.1,assay="SCT")
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
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
    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){
818
      set.seed(71682)
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
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
      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
859
860
861
862
  #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]),]
863
864
865
866
867
868
869
870
  } 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]){
871
872
873
874
    #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]),])
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
    } 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"){
896
897
898
899
900
901
902
903
904
905
906
907
908
909
    #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()
    }
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
    #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){
934
935
936
937
      #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],"')")))
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
      } 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)
}
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005

scShinyOutput <- function(){
  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"){
      write_csv(dr,paste0("./analysis/shiny/raw/outs/analysis/",i,"/2_components/projection.csv"),col_names=TRUE)
    } else {
      write_csv(dr,paste0("./analysis/shiny/raw/outs/analysis/",i,"/10_components/projection.csv"),col_names=TRUE)
    }
  }
  for (i in c("samples",paste0("integrated_snn_res.",res))){
    if (!dir.exists(paste0("./analysis/shiny/raw/outs/analysis/clustering/",gsub("integrated_snn_res.","res_",i)))){
      dir.create(paste0("./analysis/shiny/raw/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)
    write_csv(clust,paste0("./analysis/shiny/raw/outs/analysis/clustering/",gsub("integrated_snn_res.","res_",i),"/clusters.csv"),col_names=TRUE)
  }
  
  #Idents(sc10x) <- "integrated_snn_res.0.1"
  #deg <- FindAllMarkers(sc10x,assay="SCT",slot="scale.data",logfc.threshold=0,test.use="MAST")
1006
}