server.R 36.8 KB
Newer Older
dmattek's avatar
dmattek committed
1

2

dmattek's avatar
dmattek committed
3

dmattek's avatar
dmattek committed
4
5
6
7
8
9
10
11
12
13
# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#

library(shiny)
library(shinyjs) #http://deanattali.com/shinyjs/
library(data.table)
library(ggplot2)
dmattek's avatar
dmattek committed
14
library(gplots) # for heatmap.2
dmattek's avatar
dmattek committed
15
library(plotly)
dmattek's avatar
dmattek committed
16
17
18
19
20
library(d3heatmap) # for interactive heatmap
library(dendextend) # for color_branches
library(RColorBrewer)
library(sparcl) # sparse hierarchical and k-means
library(scales) # for percentages on y scale
dmattek's avatar
dmattek committed
21

22
# increase file upload limit
dmattek's avatar
Added:    
dmattek committed
23
options(shiny.maxRequestSize = 80 * 1024 ^ 2)
dmattek's avatar
dmattek committed
24

dmattek's avatar
dmattek committed
25
shinyServer(function(input, output, session) {
26
  useShinyjs()
dmattek's avatar
dmattek committed
27
  
28
29
30
31
32
33
34
35
  # This is only set at session start
  # we use this as a way to determine which input was
  # clicked in the dataInBoth reactive
  counter <- reactiveValues(
    # The value of inDataGen1,2 actionButton is the number of times they were pressed
    dataGen1     = isolate(input$inDataGen1),
    dataLoadNuc  = isolate(input$inButLoadNuc)
    #dataLoadStim = isolate(input$inButLoadStim)
dmattek's avatar
dmattek committed
36
37
  )
  
dmattek's avatar
dmattek committed
38
39
40
  ####
  ## UI for side panel
  
dmattek's avatar
dmattek committed
41
  # FILE LOAD
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
  # This button will reset the inFileLoad
  observeEvent(input$inButReset, {
    reset("inFileLoadNuc")  # reset is a shinyjs function
    #reset("inButLoadStim")  # reset is a shinyjs function
  })
  
  # generate random dataset 1
  dataGen1 <- eventReactive(input$inDataGen1, {
    cat("dataGen1\n")
    
    return(userDataGen())
  })
  
  # load main data file
  dataLoadNuc <- eventReactive(input$inButLoadNuc, {
    cat("dataLoadNuc\n")
    locFilePath = input$inFileLoadNuc$datapath
    
    counter$dataLoadNuc <- input$inButLoadNuc - 1
    
    if (is.null(locFilePath) || locFilePath == '')
      return(NULL)
    else {
      return(fread(locFilePath))
    }
  })
  
dmattek's avatar
dmattek committed
69
70
71
72
73
74
75
  # This button will reset the inFileLoad
  observeEvent(input$butReset, {
    reset("inFileLoadNuc")  # reset is a shinyjs function
    #    reset("inFileStimLoad")  # reset is a shinyjs function
    
  })
  
dmattek's avatar
dmattek committed
76
77
  
  # COLUMN SELECTION
dmattek's avatar
dmattek committed
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
  output$varSelTrackLabel = renderUI({
    cat(file = stderr(), 'UI varSelTrackLabel\n')
    locCols = getDataNucCols()
    locColSel = locCols[locCols %like% 'rack'][1] # index 1 at the end in case more matches; select 1st
    
    cat(locColSel, '\n')
    selectInput(
      'inSelTrackLabel',
      'Select Track Label (e.g. objNuc_Track_ObjectsLabel):',
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  output$varSelTime = renderUI({
    cat(file = stderr(), 'UI varSelTime\n')
    locCols = getDataNucCols()
    locColSel = locCols[locCols %like% 'RealTime'][1] # index 1 at the end in case more matches; select 1st
    
    cat(locColSel, '\n')
    selectInput(
      'inSelTime',
      'Select time column (e.g. RealTime):',
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  # This is main field to select plot facet grouping
  # It's typically a column with the entire experimental description,
  # e.g. in Yannick's case it's Stim_All_Ch or Stim_All_S.
  # In Coralie's case it's a combination of 3 columns called Stimulation_...
  output$varSelGroup = renderUI({
    cat(file = stderr(), 'UI varSelGroup\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols)) {
      locColSel = locCols[locCols %like% 'ite']
      if (length(locColSel) == 0)
        locColSel = locCols[locCols %like% 'eries'][1] # index 1 at the end in case more matches; select 1st
      else if (length(locColSel) > 1) {
        locColSel = locColSel[1]
      }
      #    cat('UI varSelGroup::locColSel ', locColSel, '\n')
      selectInput(
        'inSelGroup',
        'Select one or more facet groupings (e.g. Site, Well, Channel):',
        locCols,
        width = '100%',
        selected = locColSel,
        multiple = TRUE
      )
    }
    
  })
  
  output$varSelSite = renderUI({
    cat(file = stderr(), 'UI varSelSite\n')
    
dmattek's avatar
Added:    
dmattek committed
139
140
141
142
143
144
145
146
147
148
149
150
151
    if (!input$chBtrackUni) {
      locCols = getDataNucCols()
      locColSel = locCols[locCols %like% 'ite'][1] # index 1 at the end in case more matches; select 1st
      
      cat(locColSel, '\n')
      selectInput(
        'inSelSite',
        'Select FOV (e.g. Metadata_Site or Metadata_Series):',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
dmattek's avatar
dmattek committed
152
153
154
155
156
157
158
159
160
161
  })
  
  
  
  
  output$varSelMeas1 = renderUI({
    cat(file = stderr(), 'UI varSelMeas1\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols)) {
dmattek's avatar
dmattek committed
162
163
      locColSel = locCols[locCols %like% 'objCyto_Intensity_MeanIntensity_imErkCor.*' |
                            locCols %like% 'Ratio'][1] # index 1 at the end in case more matches; select 1st
dmattek's avatar
dmattek committed
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
      #    cat(locColSel, '\n')
      selectInput(
        'inSelMeas1',
        'Select 1st measurement:',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
  })
  
  
  output$varSelMeas2 = renderUI({
    cat(file = stderr(), 'UI varSelMeas2\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols) &&
        !(input$inSelMath %in% c('', '1 / '))) {
      locColSel = locCols[locCols %like% 'objNuc_Intensity_MeanIntensity_imErkCor.*'][1] # index 1 at the end in case more matches; select 1st
      #    cat(locColSel, '\n')
      selectInput(
        'inSelMeas2',
        'Select 2nd measurement',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
  })
  
dmattek's avatar
dmattek committed
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
  # UI for trimming x-axis (time)
  output$uiSlTimeTrim = renderUI({
    cat(file = stderr(), 'UI uiSlTimeTrim\n')
    
    if (input$chBtimeTrim) {
      locTpts  = getDataTpts()
      
      if(is.null(locTpts))
        return(NULL)
      
      locRTmin = min(locTpts)
      locRTmax = max(locTpts)
      
      sliderInput(
        'slTimeTrim',
        label = 'Time range to include',
        min = locRTmin,
        max = locRTmax,
        value = c(locRTmin, locRTmax),
        step = 1
      )
      
    }
  })
dmattek's avatar
dmattek committed
218
  
dmattek's avatar
dmattek committed
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
  # UI for normalization
  
  output$uiChBnorm = renderUI({
    cat(file = stderr(), 'UI uiChBnorm\n')
    
    if (input$chBnorm) {
      radioButtons(
        'rBnormMeth',
        label = 'Select method',
        choices = list('fold-change' = 'mean', 'z-score' = 'z.score')
      )
    }
  })
  
  output$uiSlNorm = renderUI({
    cat(file = stderr(), 'UI uiSlNorm\n')
    
    if (input$chBnorm) {
      locTpts  = getDataTpts()
      
      if(is.null(locTpts))
        return(NULL)
      
      locRTmin = min(locTpts)
      locRTmax = max(locTpts)
      
      sliderInput(
        'slNormRtMinMax',
        label = 'Time range for norm.',
        min = locRTmin,
        max = locRTmax,
dmattek's avatar
dmattek committed
250
251
        value = c(locRTmin, 0.1 * locRTmax), 
        step = 1
dmattek's avatar
dmattek committed
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
      )
    }
  })
  
  output$uiChBnormRobust = renderUI({
    cat(file = stderr(), 'UI uiChBnormRobust\n')
    
    if (input$chBnorm) {
      checkboxInput('chBnormRobust',
                    label = 'Robust stats',
                    FALSE)
    }
  })
  
  output$uiChBnormGroup = renderUI({
    cat(file = stderr(), 'UI uiChBnormGroup\n')
    
    if (input$chBnorm) {
      radioButtons('chBnormGroup',
dmattek's avatar
Mod:    
dmattek committed
271
272
                   label = 'Normalisation grouping',
                   choices = list('Entire dataset' = 'none', 'Per facet' = 'group', 'Per trajectory (Korean way)' = 'id'))
dmattek's avatar
dmattek committed
273
274
275
276
    }
  })
  
  
dmattek's avatar
dmattek committed
277
278
279
280
281
282
  # UI for removing outliers
  
  output$uiSlOutliers = renderUI({
    cat(file = stderr(), 'UI uiSlOutliers\n')
    
    if (input$chBoutliers) {
dmattek's avatar
Mod:    
dmattek committed
283
      
dmattek's avatar
dmattek committed
284
285
286
287
288
289
290
291
      sliderInput(
        'slOutliersPerc',
        label = 'Percentage of middle data',
        min = 90,
        max = 100,
        value = 99, 
        step = 0.1
      )
dmattek's avatar
dmattek committed
292
      
dmattek's avatar
Mod:    
dmattek committed
293
      
dmattek's avatar
dmattek committed
294
295
296
    }
  })
  
dmattek's avatar
dmattek committed
297
298
299
300
301
302
303
304
305
  output$uiTxtOutliers = renderUI({
    if (input$chBoutliers) {
      
      p("Total tracks")
      
    }
    
  })
  
dmattek's avatar
dmattek committed
306
  
dmattek's avatar
dmattek committed
307
308
309
310
311
312
313
314
315
  ####
  ## data processing
  
  # generate random dataset 1
  dataGen1 <- eventReactive(input$inDataGen1, {
    cat("dataGen1\n")
    
    return(userDataGen())
  })
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
  
  dataInBoth <- reactive({
    # Without direct references to inDataGen1,2 and inFileLoad, inDataGen2
    #    does not trigger running this reactive once inDataGen1 is used.
    # This is one of the more nuanced areas of reactive programming in shiny
    #    due to the if else logic, it isn't fetched once inDataGen1 is available
    # The morale is use direct retrieval of inputs to guarantee they are available
    #    for if else logic checks!
    
    locInGen1 = input$inDataGen1
    locInLoadNuc = input$inButLoadNuc
    #locInLoadStim = input$inButLoadStim
    
    cat(
      "dataInBoth\ninGen1: ",
      locInGen1,
      "   prev=",
      isolate(counter$dataGen1),
      "\ninDataNuc: ",
      locInLoadNuc,
      "   prev=",
      isolate(counter$dataLoadNuc),
      # "\ninDataStim: ",
      # locInLoadStim,
      # "   prev=",
      # isolate(counter$dataLoadStim),
      "\n"
    )
    
    # isolate the checks of counter reactiveValues
    # as we set the values in this same reactive
    if (locInGen1 != isolate(counter$dataGen1)) {
      cat("dataInBoth if inDataGen1\n")
      dm = dataGen1()
      # no need to isolate updating the counter reactive values!
      counter$dataGen1 <- locInGen1
    } else if (locInLoadNuc != isolate(counter$dataLoadNuc)) {
      cat("dataInBoth if inDataLoadNuc\n")
      dm = dataLoadNuc()
      # no need to isolate updating the counter reactive values!
      counter$dataLoadNuc <- locInLoadNuc
    } else {
      cat("dataInBoth else\n")
      dm = NULL
    }
    return(dm)
  })
  
  # return column names of the main dt
dmattek's avatar
dmattek committed
365
  getDataNucCols <- reactive({
366
367
368
369
370
371
372
373
374
375
376
    cat(file = stderr(), 'getDataNucCols: in\n')
    loc.dt = dataInBoth()
    
    if (is.null(loc.dt))
      return(NULL)
    else
      return(colnames(loc.dt))
  })
  
  # return dt with an added column with unique track object label
  dataMod <- reactive({
dmattek's avatar
dmattek committed
377
    cat(file = stderr(), 'dataMod\n')
378
379
    loc.dt = dataInBoth()
    
dmattek's avatar
dmattek committed
380
    if (is.null(loc.dt))
381
382
      return(NULL)
    
dmattek's avatar
Added:    
dmattek committed
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
    if (!input$chBtrackUni) {
      loc.types = lapply(loc.dt, class)
      if(loc.types[[input$inSelTrackLabel]] %in% c('numeric', 'integer') & loc.types[[input$inSelSite]] %in% c('numeric', 'integer'))
      {
        loc.dt[, trackObjectsLabelUni := paste(sprintf("%03d", get(input$inSelSite)),
                                               sprintf("%04d", get(input$inSelTrackLabel)),
                                               sep = "_")]
      } else if(loc.types[[input$inSelTrackLabel]] %in% c('numeric', 'integer')) {
        loc.dt[, trackObjectsLabelUni := paste(sprintf("%s", get(input$inSelSite)),
                                               sprintf("%04d", get(input$inSelTrackLabel)),
                                               sep = "_")]
      } else if(loc.types[[input$inSelSite]] %in% c('numeric', 'integer')) {
        loc.dt[, trackObjectsLabelUni := paste(sprintf("%03d", get(input$inSelSite)),
                                               sprintf("%s", get(input$inSelTrackLabel)),
                                               sep = "_")]
      } else {
        loc.dt[, trackObjectsLabelUni := paste(sprintf("%s", get(input$inSelSite)),
                                               sprintf("%s", get(input$inSelTrackLabel)),
                                               sep = "_")]
      }
dmattek's avatar
Added:    
dmattek committed
403
    } else {
dmattek's avatar
Added:    
dmattek committed
404
      loc.dt[, trackObjectsLabelUni := get(input$inSelTrackLabel)]
dmattek's avatar
Added:    
dmattek committed
405
406
    }
    
dmattek's avatar
dmattek committed
407
    
408
409
410
    return(loc.dt)
  })
  
dmattek's avatar
dmattek committed
411
412
413
414
415
  # return all unique track object labels (created in dataMod)
  # This will be used to display in UI for trajectory highlighting
  getDataTrackObjLabUni <- reactive({
    cat(file = stderr(), 'getDataTrackObjLabUni\n')
    loc.dt = dataMod()
416
    
dmattek's avatar
dmattek committed
417
418
419
420
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt$trackObjectsLabelUni))
421
422
  })
  
dmattek's avatar
dmattek committed
423
  # return all unique track object labels (created in dataMod)
dmattek's avatar
dmattek committed
424
  # This will be used to display in UI for trajectory highlighting
dmattek's avatar
dmattek committed
425
426
427
428
429
430
431
432
433
  getDataTrackObjLabUni_afterTrim <- reactive({
    cat(file = stderr(), 'getDataTrackObjLabUni_afterTrim\n')
    loc.dt = data4trajPlot()
    
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt$id))
  })
dmattek's avatar
Mod:    
dmattek committed
434
  
dmattek's avatar
dmattek committed
435
436
437
  # return all unique time points (real time)
  # This will be used to display in UI for box-plot
  # These timepoints are from the original dt and aren't affected by trimming of x-axis
dmattek's avatar
dmattek committed
438
439
440
  getDataTpts <- reactive({
    cat(file = stderr(), 'getDataTpts\n')
    loc.dt = dataMod()
441
    
dmattek's avatar
dmattek committed
442
443
444
445
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt[[input$inSelTime]]))
446
447
  })
  
dmattek's avatar
dmattek committed
448
449
450
451
452
453
454
455
456
457
458
459
460
  # return dt with cell IDs and their corresponding condition name
  # The condition is the column defined by facet groupings
  getDataCond <- reactive({
    cat(file = stderr(), 'getDataCond\n')
    loc.dt = data4trajPlot()
    
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt[, .(id, group)]))
    
  })
  
461
462
463
  
  # prepare data for plotting time courses
  # returns dt with these columns:
dmattek's avatar
dmattek committed
464
  #    realtime - selected from input
dmattek's avatar
dmattek committed
465
  #    y        - measurement selected from input
dmattek's avatar
dmattek committed
466
467
468
469
470
  #               (can be a single column or result of an operation on two cols)
  #    id       - trackObjectsLabelUni (created in dataMod)
  #    group    - grouping variable for facetting from input
  #    mid.in   - column with trajectory selection status from the input file or
  #               highlight status from UI
471
  data4trajPlot <- reactive({
dmattek's avatar
dmattek committed
472
    cat(file = stderr(), 'data4trajPlot\n')
473
474
    
    loc.dt = dataMod()
dmattek's avatar
dmattek committed
475
    if (is.null(loc.dt))
476
477
478
      return(NULL)
    
    
dmattek's avatar
dmattek committed
479
    if (input$inSelMath == '')
480
481
482
483
484
485
486
487
488
      loc.s.y = input$inSelMeas1
    else if (input$inSelMath == '1 / ')
      loc.s.y = paste0(input$inSelMath, input$inSelMeas1)
    else
      loc.s.y = paste0(input$inSelMeas1, input$inSelMath, input$inSelMeas2)
    
    # create expression for parsing
    # creates a merged column based on other columns from input
    # used for grouping of plot facets
dmattek's avatar
dmattek committed
489
490
491
492
    if(length(input$inSelGroup) == 0)
      return(NULL)
    loc.s.gr = sprintf("paste(%s, sep=';')",
                       paste(input$inSelGroup, sep = '', collapse = ','))
493
494
495
    
    loc.s.rt = input$inSelTime
    
dmattek's avatar
dmattek committed
496
497
498
499
    # Assign tracks selected for highlighting in UI
    loc.tracks.highlight = input$inSelHighlight
    locBut = input$chBhighlightTraj
    
dmattek's avatar
Added:    
dmattek committed
500
501
    
    # Find column names with position
dmattek's avatar
Mod:    
dmattek committed
502
503
    loc.s.pos.x = names(loc.dt)[names(loc.dt) %like% c('.*ocation.*X') | names(loc.dt) %like% c('.*os.x')]
    loc.s.pos.y = names(loc.dt)[names(loc.dt) %like% c('.*ocation.*Y') | names(loc.dt) %like% c('.*os.y')]
dmattek's avatar
Added:    
dmattek committed
504
505
506
507
508
509
    
    if (length(loc.s.pos.x) == 1 & length(loc.s.pos.y) == 1)
      locPos = TRUE
    else
      locPos = FALSE
    
510
511
512
    # if dataset contains column mid.in with trajectory filtering status,
    # then, include it in plotting
    if (sum(names(loc.dt) %in% 'mid.in') > 0) {
dmattek's avatar
Added:    
dmattek committed
513
514
515
516
517
518
      if (locPos) # position columns present
        loc.out = loc.dt[, .(
          y = eval(parse(text = loc.s.y)),
          id = trackObjectsLabelUni,
          group = eval(parse(text = loc.s.gr)),
          realtime = eval(parse(text = loc.s.rt)),
dmattek's avatar
Mod:    
dmattek committed
519
520
          pos.x = get(loc.s.pos.x),
          pos.y = get(loc.s.pos.y),
dmattek's avatar
Added:    
dmattek committed
521
          mid.in = mid.in
dmattek's avatar
Mod:    
dmattek committed
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
        )] else
          loc.out = loc.dt[, .(
            y = eval(parse(text = loc.s.y)),
            id = trackObjectsLabelUni,
            group = eval(parse(text = loc.s.gr)),
            realtime = eval(parse(text = loc.s.rt)),
            mid.in = mid.in
          )]
        
        
        
        
        # add 3rd level with status of track selection
        # to a column with trajectory filtering status
        if (locBut) {
          loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', mid.in)]
        }
        
540
    } else {
dmattek's avatar
Added:    
dmattek committed
541
542
543
544
545
      if (locPos) # position columns present
        loc.out = loc.dt[, .(
          y = eval(parse(text = loc.s.y)),
          id = trackObjectsLabelUni,
          group = eval(parse(text = loc.s.gr)),
dmattek's avatar
Mod:    
dmattek committed
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
          realtime = eval(parse(text = loc.s.rt)),
          pos.x = get(loc.s.pos.x),
          pos.y = get(loc.s.pos.y)
        )] else
          loc.out = loc.dt[, .(
            y = eval(parse(text = loc.s.y)),
            id = trackObjectsLabelUni,
            group = eval(parse(text = loc.s.gr)),
            realtime = eval(parse(text = loc.s.rt))
          )]
        
        
        # add a column with status of track selection
        if (locBut) {
          loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', 'NOT SEL')]
        }
562
    }
563
    
dmattek's avatar
Added:    
dmattek committed
564
565
    # add XY location if present in the dataset
    
dmattek's avatar
dmattek committed
566
567
    # remove NAs
    loc.out = loc.out[complete.cases(loc.out)]
dmattek's avatar
Mod:    
dmattek committed
568
    
dmattek's avatar
dmattek committed
569
570
571
572
    # Trim x-axis (time)
    if(input$chBtimeTrim) {
      loc.out = loc.out[realtime >= input$slTimeTrim[[1]] & realtime <= input$slTimeTrim[[2]] ]
    }
dmattek's avatar
dmattek committed
573
574
    
    # Normalization
dmattek's avatar
dmattek committed
575
    # F-n myNorm adds additional column with .norm suffix
dmattek's avatar
dmattek committed
576
577
578
579
580
581
582
583
584
585
586
587
    if (input$chBnorm) {
      loc.out = myNorm(
        in.dt = loc.out,
        in.meas.col = 'y',
        in.rt.col = 'realtime',
        in.rt.min = input$slNormRtMinMax[1],
        in.rt.max = input$slNormRtMinMax[2],
        in.type = input$rBnormMeth,
        in.robust = input$chBnormRobust,
        in.by.cols = if(input$chBnormGroup %in% 'none') NULL else input$chBnormGroup
      )
      
dmattek's avatar
dmattek committed
588
589
      # Column with normalized data is renamed to the original name
      # Further code assumes column name y produced by data4trajPlot
dmattek's avatar
dmattek committed
590
591
592
593
      loc.out[, y := NULL]
      setnames(loc.out, 'y.norm', 'y')
    }
    
dmattek's avatar
dmattek committed
594
595
596
597
598
599
    ##### MOD HERE
    ## display number of filtered tracks in textUI: uiTxtOutliers
    ## How? 
    ## 1. through reactive values?
    ## 2. through additional comumn to tag outliers?
    
dmattek's avatar
dmattek committed
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
    # Remove outliers
    # 1. Scale all points (independently per track)
    # 2. Pick time points that exceed the bounds
    # 3. Identify IDs of outliers
    # 4. Select cells that don't have these IDs
    
    cat('Ncells orig = ', length(unique(loc.out$id)), '\n')
    
    if (input$chBoutliers) {
      loc.out[, y.sc := scale(y)]  
      loc.tmp = loc.out[ y.sc < quantile(y.sc, (1 - input$slOutliersPerc * 0.01)*0.5) | 
                           y.sc > quantile(y.sc, 1 - (1 - input$slOutliersPerc * 0.01)*0.5)]
      loc.out = loc.out[!(id %in% unique(loc.tmp$id))]
      loc.out[, y.sc := NULL]
    }
    
    cat('Ncells trim = ', length(unique(loc.out$id)), '\n')
dmattek's avatar
Mod:    
dmattek committed
617
    
dmattek's avatar
dmattek committed
618
    return(loc.out)
dmattek's avatar
dmattek committed
619
620
  })
  
dmattek's avatar
dmattek committed
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
  
  
  # prepare data for clustering
  # return a matrix with:
  # cells as columns
  # time points as rows
  data4clust <- reactive({
    cat(file = stderr(), 'data4clust\n')
    
    loc.dt = data4trajPlot()
    if (is.null(loc.dt))
      return(NULL)
    
    loc.out = dcast(loc.dt, id ~ realtime, value.var = 'y')
    loc.rownames = loc.out$id
    
dmattek's avatar
Mod:    
dmattek committed
637
    
dmattek's avatar
dmattek committed
638
639
640
    loc.out = as.matrix(loc.out[, -1])
    rownames(loc.out) = loc.rownames
    return(loc.out)
dmattek's avatar
Mod:    
dmattek committed
641
  }) 
dmattek's avatar
dmattek committed
642
  
dmattek's avatar
dmattek committed
643
644
  
  # get cell IDs with cluster assignments depending on dendrogram cut
dmattek's avatar
dmattek committed
645
646
  getDataCl = function(in.dend, in.k, in.ids) {
    cat(file = stderr(), 'getDataCl \n')
dmattek's avatar
Mod:    
dmattek committed
647
    
dmattek's avatar
dmattek committed
648
    loc.dt.cl = data.table(id = in.ids,
dmattek's avatar
dmattek committed
649
650
651
                           cl = cutree(as.dendrogram(in.dend), k = in.k))
  }
  
dmattek's avatar
dmattek committed
652
653
  ####
  ## UI for trajectory plot
dmattek's avatar
dmattek committed
654
  
dmattek's avatar
dmattek committed
655
656
  output$varSelHighlight = renderUI({
    cat(file = stderr(), 'UI varSelHighlight\n')
dmattek's avatar
dmattek committed
657
    
dmattek's avatar
dmattek committed
658
659
660
    locBut = input$chBhighlightTraj
    if (!locBut)
      return(NULL)
dmattek's avatar
dmattek committed
661
    
dmattek's avatar
dmattek committed
662
    loc.v = getDataTrackObjLabUni()
dmattek's avatar
dmattek committed
663
    if (!is.null(loc.v)) {
664
      selectInput(
dmattek's avatar
dmattek committed
665
666
667
        'inSelHighlight',
        'Select one or more rajectories:',
        loc.v,
668
        width = '100%',
dmattek's avatar
dmattek committed
669
        multiple = TRUE
670
      )
dmattek's avatar
dmattek committed
671
672
673
    }
  })
  
dmattek's avatar
Mod:    
dmattek committed
674
  callModule(modTrajPlot, 'modTrajPlot', data4trajPlot)
dmattek's avatar
dmattek committed
675
  
dmattek's avatar
Added:    
dmattek committed
676
677
  ###### Box-plot
  callModule(tabBoxPlot, 'tabBoxPlot', data4trajPlot)
dmattek's avatar
dmattek committed
678
  
dmattek's avatar
dmattek committed
679
680
  
  
dmattek's avatar
dmattek committed
681
682
683
  ###### Scatter plot
  callModule(tabScatterPlot, 'tabScatter', data4trajPlot)
  
dmattek's avatar
dmattek committed
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
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
  ##### Hierarchical clustering
  
  output$uiPlotHierClSel = renderUI({
    if(input$chBPlotHierClSel) {
      selectInput('inPlotHierClSel', 'Select clusters to display', 
                  choices = seq(1, input$inPlotHierNclust, 1),
                  multiple = TRUE, 
                  selected = 1)
    }
  })
  
  userFitDendHier <- reactive({
    dm.t = data4clust()
    if (is.null(dm.t)) {
      return()
    }
    
    cl.dist = dist(dm.t, method = s.cl.diss[as.numeric(input$selectPlotHierDiss)])
    cl.hc = hclust(cl.dist, method = s.cl.linkage[as.numeric(input$selectPlotHierLinkage)])
    cl.lev = rev(row.names(dm.t))
    
    dend <- as.dendrogram(cl.hc)
    dend <- color_branches(dend, k = input$inPlotHierNclust)
    
    return(dend)
  })
  
  # Function instead of reactive as per:
  # http://stackoverflow.com/questions/26764481/downloading-png-from-shiny-r
  # This function is used to plot and to downoad a pdf
  plotHier <- function() {
    
    loc.dm = data4clust()
    if (is.null(loc.dm))
      return(NULL)
    
    loc.dend <- userFitDendHier()
    if (is.null(loc.dend))
      return(NULL)
    
    if (input$inPlotHierRevPalette)
      my_palette <-
      rev(colorRampPalette(brewer.pal(9, input$selectPlotHierPalette))(n = 99))
    else
      my_palette <-
      colorRampPalette(brewer.pal(9, input$selectPlotHierPalette))(n = 99)
    
    
    col_labels <- get_leaves_branches_col(loc.dend)
    col_labels <- col_labels[order(order.dendrogram(loc.dend))]
    
    if (input$selectPlotHierDend) {
      assign("var.tmp.1", loc.dend)
      var.tmp.2 = "row"
    } else {
      assign("var.tmp.1", FALSE)
      var.tmp.2 = "none"
    }
    
    loc.p = heatmap.2(
      loc.dm,
      Colv = "NA",
      Rowv = var.tmp.1,
      srtCol = 90,
      dendrogram = var.tmp.2,
      trace = "none",
      key = input$selectPlotHierKey,
      margins = c(input$inPlotHierMarginX, input$inPlotHierMarginY),
      col = my_palette,
      na.col = grey(input$inPlotHierNAcolor),
      denscol = "black",
      density.info = "density",
      RowSideColors = col_labels,
      colRow = col_labels,
dmattek's avatar
Mod:    
dmattek committed
758
759
760
      #      sepcolor = grey(input$inPlotHierGridColor),
      #      colsep = 1:ncol(loc.dm),
      #      rowsep = 1:nrow(loc.dm),
dmattek's avatar
dmattek committed
761
762
763
764
765
766
767
768
769
770
771
772
773
774
      cexRow = input$inPlotHierFontX,
      cexCol = input$inPlotHierFontY,
      main = paste(
        "Distance measure: ",
        s.cl.diss[as.numeric(input$selectPlotHierDiss)],
        "\nLinkage method: ",
        s.cl.linkage[as.numeric(input$selectPlotHierLinkage)]
      )
    )
    
    return(loc.p)
  }
  
  
dmattek's avatar
Mod:    
dmattek committed
775
776
777
778
779
  # prepare data for plotting trajectories per cluster
  # outputs dt as data4trajPlot but with an additional column 'cl' that holds cluster numbers
  # additionally some clusters are omitted according to manual selection
  data4trajPlotCl <- reactive({
    cat(file = stderr(), 'data4trajPlotCl: in\n')
dmattek's avatar
dmattek committed
780
    
dmattek's avatar
Mod:    
dmattek committed
781
    loc.dt = data4trajPlot()
dmattek's avatar
dmattek committed
782
783
    
    if (is.null(loc.dt)) {
dmattek's avatar
Mod:    
dmattek committed
784
      cat(file = stderr(), 'data4trajPlotCl: dt is NULL\n')
dmattek's avatar
dmattek committed
785
786
787
      return(NULL)
    }
    
dmattek's avatar
Mod:    
dmattek committed
788
    cat(file = stderr(), 'data4trajPlotCl: dt not NULL\n')
dmattek's avatar
dmattek committed
789
790
    
    # get cellIDs with cluster assignments based on dendrogram cut
dmattek's avatar
dmattek committed
791
    loc.dt.cl = getDataCl(userFitDendHier(), input$inPlotHierNclust, getDataTrackObjLabUni_afterTrim())
dmattek's avatar
dmattek committed
792
793
794
    loc.dt = merge(loc.dt, loc.dt.cl, by = 'id')
    
    # display only selected clusters
dmattek's avatar
Mod:    
dmattek committed
795
796
    if(input$chBPlotHierClSel)
      loc.dt = loc.dt[cl %in% input$inPlotHierClSel]
dmattek's avatar
dmattek committed
797
    
dmattek's avatar
Mod:    
dmattek committed
798
799
    return(loc.dt)    
  })
dmattek's avatar
dmattek committed
800
  
dmattek's avatar
Mod:    
dmattek committed
801
802
803
804
  callModule(modTrajPlot, 'modPlotHierTraj', data4trajPlotCl, 'cl',  paste0('clust_hierch_tCourses_',
                                                                            s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                                                                            '_',
                                                                            s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.pdf'))
dmattek's avatar
dmattek committed
805
  
dmattek's avatar
Added:    
dmattek committed
806
  # download a list of cellIDs with cluster assignments
dmattek's avatar
dmattek committed
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
833
  output$downCellCl <- downloadHandler(
    filename = function() {
      paste0('clust_hierch_data_',
             s.cl.diss[as.numeric(input$selectPlotHierDiss)],
             '_',
             s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.csv')
    },
    
    content = function(file) {
      write.csv(x = getDataCl(userFitDendHier(), input$inPlotHierNclust, getDataTrackObjLabUni_afterTrim()), file = file, row.names = FALSE)
    }
  )
  
  output$downCellClSpar <- downloadHandler(
    filename = function() {
      paste0('clust_hierchSpar_data_',
             s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
             '_',
             s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.csv')
    },
    
    content = function(file) {
      write.csv(x = getDataCl(userFitDendHierSpar(), input$inPlotHierSparNclust, getDataTrackObjLabUni_afterTrim()), file = file, row.names = FALSE)
    }
  )
  
  
dmattek's avatar
Mod:    
dmattek committed
834
835
836
837
838
839
  # callModule(downCellCl, 'downDataHier', paste0('clust_hierch_data_',
  #                                               s.cl.diss[as.numeric(input$selectPlotHierDiss)],
  #                                               '_',
  #                                               s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.csv'),
  #            getDataCl(userFitDendHier, input$inPlotHierNclust, getDataTrackObjLabUni_afterTrim))
  # 
dmattek's avatar
Added:    
dmattek committed
840
  
dmattek's avatar
Mod:    
dmattek committed
841
842
843
844
845
846
847
848
849
850
851
  output$downloadDataClean <- downloadHandler(
    filename = 'tCoursesSelected_clean.csv',
    content = function(file) {
      write.csv(data4trajPlot(), file, row.names = FALSE)
    }
  )
  
  
  # prepare data for barplot with distribution of items per condition  
  data4clDistPlot <- reactive({
    cat(file = stderr(), 'data4clDistPlot: in\n')
dmattek's avatar
dmattek committed
852
853
    
    # get cell IDs with cluster assignments depending on dendrogram cut
dmattek's avatar
Mod:    
dmattek committed
854
    loc.dend <- userFitDendHier()
dmattek's avatar
dmattek committed
855
856
857
858
859
860
861
862
863
    if (is.null(loc.dend)) {
      cat(file = stderr(), 'plotClDist: loc.dend is NULL\n')
      return(NULL)
    }
    
    loc.dt.cl = data.table(id = getDataTrackObjLabUni_afterTrim(),
                           cl = cutree(as.dendrogram(loc.dend), k = input$inPlotHierNclust))
    
    
dmattek's avatar
dmattek committed
864
    # get cellIDs with condition name
dmattek's avatar
Mod:    
dmattek committed
865
    loc.dt.gr = getDataCond()
dmattek's avatar
dmattek committed
866
867
868
869
870
871
872
873
    if (is.null(loc.dt.gr)) {
      cat(file = stderr(), 'plotClDist: loc.dt.gr is NULL\n')
      return(NULL)
    }
    
    loc.dt = merge(loc.dt.cl, loc.dt.gr, by = 'id')
    
    # display only selected clusters
dmattek's avatar
Mod:    
dmattek committed
874
875
    if(input$chBPlotHierClSel)
      loc.dt = loc.dt[cl %in% input$inPlotHierClSel]
dmattek's avatar
dmattek committed
876
877
878
    
    loc.dt.aggr = loc.dt[, .(nCells = .N), by = .(group, cl)]
    
dmattek's avatar
Mod:    
dmattek committed
879
    return(loc.dt.aggr)
dmattek's avatar
dmattek committed
880
    
dmattek's avatar
Mod:    
dmattek committed
881
  })
dmattek's avatar
dmattek committed
882
  
dmattek's avatar
Mod:    
dmattek committed
883

dmattek's avatar
dmattek committed
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
  #  Hierarchical - display heatmap
  getPlotHierHeatMapHeight <- function() {
    return (input$inPlotHierHeatMapHeight)
  }
  
  output$outPlotHier <- renderPlot({
    locBut = input$butPlotHierHeatMap
    
    if (locBut == 0) {
      cat(file = stderr(), 'outPlotHier: Go button not pressed\n')
      
      return(NULL)
    }
    
    plotHier()
  }, height = getPlotHierHeatMapHeight)
  
  
dmattek's avatar
dmattek committed
902
903
904
905
906
907
  #  Hierarchical - Heat Map - download pdf
  callModule(downPlot, "downPlotHier",       paste0('clust_hierch_heatMap_',
                                                    s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                                                    '_',
                                                    s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.pdf'), plotHier)

dmattek's avatar
Mod:    
dmattek committed
908
909
910
911
912
913
  callModule(modClDistPlot, 'hierClDistPlot', data4clDistPlot,
             paste0('clust_hierch_clDist_',
                    s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                    '_',
                    s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.pdf'))
  
dmattek's avatar
dmattek committed
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
  
  ##### Sparse hierarchical clustering using sparcl
  
  # UI for advanced options
  output$uiPlotHierSparNperms = renderUI({
    if (input$inHierSparAdv)
      sliderInput(
        'inPlotHierSparNperms',
        'Number of permutations',
        min = 1,
        max = 20,
        value = 1,
        step = 1,
        ticks = TRUE
      )
  })
  
  # UI for advanced options
  output$uiPlotHierSparNiter = renderUI({
    if (input$inHierSparAdv)
      sliderInput(
        'inPlotHierSparNiter',
        'Number of iterations',
        min = 1,
        max = 50,
        value = 1,
        step = 1,
        ticks = TRUE
      )
  })
  
  output$uiPlotHierSparClSel = renderUI({
    if(input$chBPlotHierSparClSel) {
      selectInput('inPlotHierSparClSel', 'Select clusters to display', 
                  choices = seq(1, input$inPlotHierSparNclust, 1),
                  multiple = TRUE, 
                  selected = 1)
    }
  })
  
dmattek's avatar
Mod:    
dmattek committed
954
  
dmattek's avatar
dmattek committed
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
988
989
990
991
992
993
994
995
996
997
998
999
1000
  getPlotHierSparHeatMapHeight <- function() {
    return (input$inPlotHierSparHeatMapHeight)
  }
  
  userFitHierSpar <- reactive({
    dm.t = data4clust()
    if (is.null(dm.t)) {
      return()
    }
    
    #cat('rownames: ', rownames(dm.t), '\n')
    
    perm.out <- HierarchicalSparseCluster.permute(
      dm.t,
      wbounds = NULL,
      nperms = ifelse(input$inHierSparAdv, input$inPlotHierSparNperms, 1),
      dissimilarity = s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)]
    )
    
    sparsehc <- HierarchicalSparseCluster(
      dists = perm.out$dists,
      wbound = perm.out$bestw,
      niter = ifelse(input$inHierSparAdv, input$inPlotHierSparNiter, 1),
      method = s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)],
      dissimilarity = s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)]
    )
    return(sparsehc)
  })
  
  
  userFitDendHierSpar <- reactive({
    sparsehc = userFitHierSpar()
    if (is.null(sparsehc)) {
      return()
    }
    
    dend <- as.dendrogram(sparsehc$hc)
    dend <- color_branches(dend, k = input$inPlotHierSparNclust)
    
    return(dend)
  })
  
  # Function instead of reactive as per:
  # http://stackoverflow.com/questions/26764481/downloading-png-from-shiny-r
  # This function is used to plot and to downoad a pdf
  plotHierSpar <- function() {