server.R 24.4 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
library(d3heatmap) # for interactive heatmap
library(dendextend) # for color_branches
18
library(colorspace) # for palettes (used to colour dendrogram)
dmattek's avatar
dmattek committed
19
library(RColorBrewer)
20
# sparcl temporarily unavailable on CRAN
dmattek's avatar
dmattek committed
21
library(sparcl) # sparse hierarchical and k-means
dmattek's avatar
dmattek committed
22
library(scales) # for percentages on y scale
dmattek's avatar
Added:  
dmattek committed
23 24
library(dtw) # for dynamic time warping
library(imputeTS) # for interpolating NAs
25
library(tca) # for time series manipulatiom, e.g. normTraj, genTraj, plotTrajRibbon
dmattek's avatar
dmattek committed
26

27
# increase file upload limit
dmattek's avatar
Added:  
dmattek committed
28
options(shiny.maxRequestSize = 200 * 1024 ^ 2)
dmattek's avatar
dmattek committed
29

dmattek's avatar
dmattek committed
30
shinyServer(function(input, output, session) {
31
  useShinyjs()
dmattek's avatar
dmattek committed
32
  
33 34 35 36 37 38
  # 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$inDataGen1),
dmattek's avatar
Added:  
dmattek committed
39 40
    dataLoadNuc  = isolate(input$inButLoadNuc),
    dataLoadTrajRem = isolate(input$inButLoadTrajRem)
41
    #dataLoadStim = isolate(input$inButLoadStim)
dmattek's avatar
dmattek committed
42 43
  )
  
dmattek's avatar
dmattek committed
44 45 46
  ####
  ## UI for side panel
  
dmattek's avatar
dmattek committed
47
  # FILE LOAD
48 49 50 51 52 53 54 55 56 57
  # 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")
    
58
    return(tca::genTraj(in.nwells = 3))
59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
  })
  
  # 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
75 76 77 78 79 80 81
  # 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
Added:  
dmattek committed
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
  # UI for loading csv with cell IDs for trajectory removal
  output$uiFileLoadTrajRem = renderUI({
    cat(file = stderr(), 'UI uiFileLoadTrajRem\n')
    
    if(input$chBtrajRem) 
      fileInput(
        'inFileLoadTrajRem',
        'Select data file (e.g. badTraj.csv) and press "Load Data"',
        accept = c('text/csv', 'text/comma-separated-values,text/plain')
      )
  })
  
  output$uiButLoadTrajRem = renderUI({
    cat(file = stderr(), 'UI uiButLoadTrajRem\n')
    
    if(input$chBtrajRem)
      actionButton("inButLoadTrajRem", "Load Data")
  })

  # load main data file
  dataLoadTrajRem <- eventReactive(input$inButLoadTrajRem, {
    cat(file = stderr(), "dataLoadTrajRem\n")
    locFilePath = input$inFileLoadTrajRem$datapath
    
    counter$dataLoadTrajRem <- input$inButLoadTrajRem - 1
    
    if (is.null(locFilePath) || locFilePath == '')
      return(NULL)
    else {
      return(fread(locFilePath))
    }
  })
  
dmattek's avatar
dmattek committed
115 116
  
  # COLUMN SELECTION
dmattek's avatar
dmattek committed
117 118 119
  output$varSelTrackLabel = renderUI({
    cat(file = stderr(), 'UI varSelTrackLabel\n')
    locCols = getDataNucCols()
120
    locColSel = locCols[grep('(T|t)rack|ID|id', locCols)[1]] # index 1 at the end in case more matches; select 1st; matches TrackLabel, tracklabel, Track Label etc
dmattek's avatar
dmattek committed
121 122 123
    
    selectInput(
      'inSelTrackLabel',
dmattek's avatar
dmattek committed
124
      'Select Track Label (e.g. objNuc_TrackObjects_Label):',
dmattek's avatar
dmattek committed
125 126 127 128 129 130 131 132 133
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  output$varSelTime = renderUI({
    cat(file = stderr(), 'UI varSelTime\n')
    locCols = getDataNucCols()
134
    locColSel = locCols[grep('(T|t)ime|Metadata_T', locCols)[1]] # index 1 at the end in case more matches; select 1st; matches RealTime, realtime, real time, etc.
dmattek's avatar
dmattek committed
135 136 137
    
    selectInput(
      'inSelTime',
dmattek's avatar
dmattek committed
138
      'Select time column (e.g. Metadata_T, RealTime):',
dmattek's avatar
dmattek committed
139 140 141 142 143
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
144 145 146 147

  output$varSelTimeFreq = renderUI({
    cat(file = stderr(), 'UI varSelTimeFreq\n')
    
148 149 150 151 152 153 154 155 156 157
    if (input$chBtrajInter) {
      numericInput(
        'inSelTimeFreq',
        'Provide time frequency:',
        min = 1,
        step = 1,
        width = '100%',
        value = 1
      )
    }
158
  })
dmattek's avatar
dmattek committed
159 160 161 162 163 164 165 166
  
  # 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')
    
dmattek's avatar
dmattek committed
167 168 169 170 171
    if (input$chBgroup) {
      
      locCols = getDataNucCols()
      
      if (!is.null(locCols)) {
172 173 174
        locColSel = locCols[grep('(G|g)roup|(S|s)tim_All|(S|s)timulation|(S|s)ite', locCols)[1]]

        #cat('UI varSelGroup::locColSel ', locColSel, '\n')
dmattek's avatar
dmattek committed
175 176 177 178 179 180 181 182
        selectInput(
          'inSelGroup',
          'Select one or more facet groupings (e.g. Site, Well, Channel):',
          locCols,
          width = '100%',
          selected = locColSel,
          multiple = TRUE
        )
dmattek's avatar
dmattek committed
183 184 185 186 187 188 189
      }
    }
  })
  
  output$varSelSite = renderUI({
    cat(file = stderr(), 'UI varSelSite\n')
    
190
    if (input$chBtrackUni) {
dmattek's avatar
Added:  
dmattek committed
191
      locCols = getDataNucCols()
192
      locColSel = locCols[grep('(S|s)ite|(S|s)eries', locCols)[1]] # index 1 at the end in case more matches; select 1st
dmattek's avatar
Added:  
dmattek committed
193 194 195 196 197 198 199 200 201
      
      selectInput(
        'inSelSite',
        'Select FOV (e.g. Metadata_Site or Metadata_Series):',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
dmattek's avatar
dmattek committed
202 203 204 205 206 207 208 209 210 211
  })
  
  
  
  
  output$varSelMeas1 = renderUI({
    cat(file = stderr(), 'UI varSelMeas1\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols)) {
212
      locColSel = locCols[grep('objCyto_Intensity_MeanIntensity_imErkCor|(R|r)atio|(I|i)ntensity|y', locCols)[1]] # index 1 at the end in case more matches; select 1st
dmattek's avatar
dmattek committed
213

dmattek's avatar
dmattek committed
214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230
      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 / '))) {
231
      locColSel = locCols[grep('objNuc_Intensity_MeanIntensity_imErkCor', locCols)[1]] # index 1 at the end in case more matches; select 1st
dmattek's avatar
dmattek committed
232

dmattek's avatar
dmattek committed
233 234 235 236 237 238 239 240 241 242
      selectInput(
        'inSelMeas2',
        'Select 2nd measurement',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
  })
  
dmattek's avatar
dmattek committed
243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266
  # 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
267
  
dmattek's avatar
dmattek committed
268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298
  # 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
299 300
        value = c(locRTmin, 0.1 * locRTmax), 
        step = 1
dmattek's avatar
dmattek committed
301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319
      )
    }
  })
  
  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
320
                   label = 'Normalisation grouping',
321
                   choices = list('Entire dataset' = 'none', 'Per facet' = 'group', 'Per trajectory' = 'id'))
dmattek's avatar
dmattek committed
322 323 324 325
    }
  })
  
  
dmattek's avatar
dmattek committed
326 327 328 329 330
  # UI for removing outliers
  output$uiSlOutliers = renderUI({
    cat(file = stderr(), 'UI uiSlOutliers\n')
    
    if (input$chBoutliers) {
dmattek's avatar
Mod:  
dmattek committed
331
      
dmattek's avatar
dmattek committed
332 333 334 335 336
      sliderInput(
        'slOutliersPerc',
        label = 'Percentage of middle data',
        min = 90,
        max = 100,
dmattek's avatar
Fixed:  
dmattek committed
337
        value = 99.5, 
dmattek's avatar
dmattek committed
338 339
        step = 0.1
      )
dmattek's avatar
dmattek committed
340
      
dmattek's avatar
Mod:  
dmattek committed
341
      
dmattek's avatar
dmattek committed
342 343 344
    }
  })
  
dmattek's avatar
dmattek committed
345 346 347 348 349 350 351 352 353
  output$uiTxtOutliers = renderUI({
    if (input$chBoutliers) {
      
      p("Total tracks")
      
    }
    
  })
  
dmattek's avatar
dmattek committed
354
  
dmattek's avatar
dmattek committed
355 356 357
  ####
  ## data processing
  
358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405
  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
406
  getDataNucCols <- reactive({
407 408 409 410 411 412 413 414 415 416 417
    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
418
    cat(file = stderr(), 'dataMod\n')
419 420
    loc.dt = dataInBoth()
    
dmattek's avatar
dmattek committed
421
    if (is.null(loc.dt))
422 423
      return(NULL)
    
424
    if (input$chBtrackUni) {
dmattek's avatar
Added:  
dmattek committed
425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443
      loc.types = lapply(loc.dt, class)
      if(loc.types[[input$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$inSelTrackLabel)),
                                               sep = "_")]
      }
dmattek's avatar
Added:  
dmattek committed
444
    } else {
dmattek's avatar
Added:  
dmattek committed
445
      loc.dt[, trackObjectsLabelUni := get(input$inSelTrackLabel)]
dmattek's avatar
Added:  
dmattek committed
446 447
    }
    
dmattek's avatar
dmattek committed
448
    
dmattek's avatar
Added:  
dmattek committed
449 450 451 452 453 454
    # remove trajectories based on uploaded csv

    if (input$chBtrajRem) {
      cat(file = stderr(), 'dataMod: trajRem not NULL\n')
      
      loc.dt.rem = dataLoadTrajRem()
dmattek's avatar
dmattek committed
455
      loc.dt = loc.dt[!(trackObjectsLabelUni %in% loc.dt.rem[[1]])]
dmattek's avatar
Added:  
dmattek committed
456 457
    }
    
458 459 460
    return(loc.dt)
  })
  
dmattek's avatar
dmattek committed
461 462 463 464 465
  # 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()
466
    
dmattek's avatar
dmattek committed
467 468 469 470
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt$trackObjectsLabelUni))
471 472
  })
  
dmattek's avatar
Mod:  
dmattek committed
473
  
dmattek's avatar
dmattek committed
474 475 476
  # 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
477 478 479
  getDataTpts <- reactive({
    cat(file = stderr(), 'getDataTpts\n')
    loc.dt = dataMod()
480
    
dmattek's avatar
dmattek committed
481 482 483 484
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt[[input$inSelTime]]))
485 486
  })
  
dmattek's avatar
dmattek committed
487
  
488 489 490
  
  # prepare data for plotting time courses
  # returns dt with these columns:
dmattek's avatar
dmattek committed
491
  #    realtime - selected from input
dmattek's avatar
dmattek committed
492
  #    y        - measurement selected from input
dmattek's avatar
dmattek committed
493
  #               (can be a single column or result of an operation on two cols)
494 495
  #    id       - trackObjectsLabelUni; created in dataMod based on TrackObjects_Label
  #               and FOV column such as Series or Site (if TrackObjects_Label not unique across entire dataset)
dmattek's avatar
dmattek committed
496 497
  #    group    - grouping variable for facetting from input
  #    mid.in   - column with trajectory selection status from the input file or
498 499 500 501
  #               highlight status from UI 
  #               (column created if mid.in present in uploaded data or tracks are selected in the UI)
  #    obj.num  - created if ObjectNumber column present in the input data 
  #    pos.x,y  - created if columns with x and y positions present in the input data
502
  data4trajPlot <- reactive({
dmattek's avatar
dmattek committed
503
    cat(file = stderr(), 'data4trajPlot\n')
504 505
    
    loc.dt = dataMod()
dmattek's avatar
dmattek committed
506
    if (is.null(loc.dt))
507 508
      return(NULL)
    
509
    # create expression for 'y' column based on measurements and math operations selected in UI
dmattek's avatar
dmattek committed
510
    if (input$inSelMath == '')
511 512 513 514 515 516
      loc.s.y = input$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)
    
517
    # create expression for 'group' column
518 519
    # creates a merged column based on other columns from input
    # used for grouping of plot facets
dmattek's avatar
dmattek committed
520 521 522 523 524 525 526 527 528 529 530
    if (input$chBgroup) {
      if(length(input$inSelGroup) == 0)
        return(NULL)
      
      loc.s.gr = sprintf("paste(%s, sep=';')",
                         paste(input$inSelGroup, sep = '', collapse = ','))
    } else {
      # if no grouping required, fill 'group' column with 0
      # because all the plotting relies on the presence of the group column
      loc.s.gr = "paste('0')"
    }
531
    
dmattek's avatar
dmattek committed
532 533

    # column name with time
534 535
    loc.s.rt = input$inSelTime
    
dmattek's avatar
dmattek committed
536 537
    # Assign tracks selected for highlighting in UI
    loc.tracks.highlight = input$inSelHighlight
538
    locButHighlight = input$chBhighlightTraj
dmattek's avatar
dmattek committed
539
    
dmattek's avatar
Added:  
dmattek committed
540 541
    
    # Find column names with position
542
    loc.s.pos.x = names(loc.dt)[grep('(L|l)ocation.*X|(P|p)os.x|(P|p)osx', names(loc.dt))[1]]
543
    loc.s.pos.y = names(loc.dt)[grep('(L|l)ocation.*Y|(P|p)os.y|(P|p)osy', names(loc.dt))[1]]
dmattek's avatar
Added:  
dmattek committed
544
    
545
    cat('Position columns: ', loc.s.pos.x, loc.s.pos.y, '\n')
546 547
    
    if (!is.na(loc.s.pos.x) & !is.na(loc.s.pos.y))
dmattek's avatar
Added:  
dmattek committed
548 549 550 551
      locPos = TRUE
    else
      locPos = FALSE
    
552 553 554 555
    
    # Find column names with ObjectNumber
    # This is different from TrackObject_Label and is handy to keep
    # because labels on segmented images are typically ObjectNumber
556 557 558 559 560 561
    loc.s.objnum = names(loc.dt)[grep('(O|o)bject(N|n)umber', names(loc.dt))[1]]
    #cat('data4trajPlot::loc.s.objnum ', loc.s.objnum, '\n')
    if (is.na(loc.s.objnum)) {
      locObjNum = FALSE
    }
    else {
dmattek's avatar
dmattek committed
562
      loc.s.objnum = loc.s.objnum[1]
563
      locObjNum = TRUE
dmattek's avatar
dmattek committed
564
    }
565 566
    
    
567 568
    # if dataset contains column mid.in with trajectory filtering status,
    # then, include it in plotting
569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610
    if (sum(names(loc.dt) %in% 'mid.in') > 0)
      locMidIn = TRUE
    else
      locMidIn = FALSE
    
    ## Build expression for selecting columns from loc.dt
    # Core columns
    s.colexpr = paste0('.(y = ', loc.s.y,
                       ', id = trackObjectsLabelUni', 
                       ', group = ', loc.s.gr,
                       ', realtime = ', loc.s.rt)
    
    # account for the presence of 'mid.in' column in uploaded data
    if(locMidIn)
      s.colexpr = paste0(s.colexpr, 
                         ', mid.in = mid.in')
    
    # include position x,y columns in uploaded data
    if(locPos)
      s.colexpr = paste0(s.colexpr, 
                         ', pos.x = ', loc.s.pos.x,
                         ', pos.y = ', loc.s.pos.y)
    
    # include ObjectNumber column
    if(locObjNum)
      s.colexpr = paste0(s.colexpr, 
                         ', obj.num = ', loc.s.objnum)
    
    # close bracket, finish the expression
    s.colexpr = paste0(s.colexpr, ')')
    
    # create final dt for output based on columns selected above
    loc.out = loc.dt[, eval(parse(text = s.colexpr))]
    
    
    # if track selection ON
    if (locButHighlight){
      # add a 3rd level with status of track selection
      # to a column with trajectory filtering status in the uploaded file
      if(locMidIn)
        loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', mid.in)]
      else
dmattek's avatar
Mod:  
dmattek committed
611
        # add a column with status of track selection
612
        loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', 'NOT SEL')]
613
    }
614
      
dmattek's avatar
dmattek committed
615

616
    ## Interpolate missing data and NA data points
617
    # From: https://stackoverflow.com/questions/28073752/r-how-to-add-rows-for-missing-values-for-unique-group-sequences
618 619 620
    # Tracks are interpolated only within first and last time points of every cell id
    # Datasets can have different realtime frequency (e.g. every 1', 2', etc),
    # or the frame number metadata can be missing, as is the case for tCourseSelected files that already have realtime column.
621
    # Therefore, we cannot rely on that info to get time frequency; user provides this number!
622
    
623 624
    setkey(loc.out, group, id, realtime)

625 626
    if (input$chBtrajInter) {
      # here we fill missing data with NA's
627
      loc.out = loc.out[setkey(loc.out[, .(seq(min(realtime, na.rm = T), max(realtime, na.rm = T), input$inSelTimeFreq)), by = .(group, id)], group, id, V1)]
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
      
      # x-check: print all rows with NA's
      print('Rows with NAs:')
      print(loc.out[rowSums(is.na(loc.out)) > 0, ])
      
      # NA's may be already present in the dataset'.
      # Interpolate (linear) them with na.interpolate as well
      if(locPos)
        s.cols = c('y', 'pos.x', 'pos.y')
      else
        s.cols = c('y')
      
      loc.out[, (s.cols) := lapply(.SD, na.interpolation), by = id, .SDcols = s.cols]
      
      
      # !!! Current issue with interpolation:
      # The column mid.in is not taken into account.
      # If a trajectory is selected in the UI,
      # the mid.in column is added (if it doesn't already exist in the dataset),
      # and for the interpolated point, it will still be NA. Not really an issue.
      #
      # Also, think about the current option of having mid.in column in the uploaded dataset.
      # Keep it? Expand it?
      # Create a UI filed for selecting the column with mid.in data.
      # What to do with that column during interpolation (see above)
      
    }    
dmattek's avatar
Mod:  
dmattek committed
655
    
656
    ## Trim x-axis (time)
dmattek's avatar
dmattek committed
657 658 659
    if(input$chBtimeTrim) {
      loc.out = loc.out[realtime >= input$slTimeTrim[[1]] & realtime <= input$slTimeTrim[[2]] ]
    }
dmattek's avatar
dmattek committed
660
    
661
    ## Normalization
662
    # F-n tca::normTraj adds additional column with .norm suffix
dmattek's avatar
dmattek committed
663
    if (input$chBnorm) {
664
      loc.out = tca::normTraj(
dmattek's avatar
dmattek committed
665 666 667 668 669 670 671 672 673 674
        in.dt = loc.out,
        in.meas.col = 'y',
        in.rt.col = 'realtime',
        in.rt.min = input$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
675 676
      # Column with normalized data is renamed to the original name
      # Further code assumes column name y produced by data4trajPlot
dmattek's avatar
dmattek committed
677 678 679 680
      loc.out[, y := NULL]
      setnames(loc.out, 'y.norm', 'y')
    }
    
dmattek's avatar
dmattek committed
681 682 683 684 685 686
    ##### 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
687 688 689 690 691 692 693 694 695 696
    # 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$chBoutliers) {
      loc.out[, y.sc := scale(y)]  
697 698
      loc.tmp = loc.out[ y.sc < quantile(y.sc, (1 - input$slOutliersPerc * 0.01)*0.5, na.rm = T) | 
                           y.sc > quantile(y.sc, 1 - (1 - input$slOutliersPerc * 0.01)*0.5, na.rm = T)]
dmattek's avatar
dmattek committed
699 700 701 702 703
      loc.out = loc.out[!(id %in% unique(loc.tmp$id))]
      loc.out[, y.sc := NULL]
    }
    
    cat('Ncells trim = ', length(unique(loc.out$id)), '\n')
dmattek's avatar
Mod:  
dmattek committed
704
    
dmattek's avatar
dmattek committed
705
    return(loc.out)
dmattek's avatar
dmattek committed
706 707
  })
  
dmattek's avatar
dmattek committed
708 709 710 711 712 713 714 715 716 717 718 719 720
  
  
  # 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)
    
dmattek's avatar
Added:  
dmattek committed
721
    #print(loc.dt)
dmattek's avatar
dmattek committed
722
    loc.out = dcast(loc.dt, id ~ realtime, value.var = 'y')
dmattek's avatar
Added:  
dmattek committed
723
    #print(loc.out)
dmattek's avatar
dmattek committed
724 725
    loc.rownames = loc.out$id
    
dmattek's avatar
Mod:  
dmattek committed
726
    
dmattek's avatar
dmattek committed
727 728
    loc.out = as.matrix(loc.out[, -1])
    rownames(loc.out) = loc.rownames
dmattek's avatar
Added:  
dmattek committed
729
    
730 731
    # This might be removed entirely because all NA treatment happens in data4trajPlot
    # Clustering should work with NAs present. These might result from data itself or from missing time point rows that were turned into NAs when dcast-ing from long format.
dmattek's avatar
Added:  
dmattek committed
732 733 734 735
    # Remove NA's
    # na.interpolation from package imputeTS works with multidimensional data
    # but imputation is performed for each column independently
    # The matrix for clustering contains time series in rows, hence transposing it twice
736
    # loc.out = t(na.interpolation(t(loc.out)))
dmattek's avatar
Added:  
dmattek committed
737
    
dmattek's avatar
dmattek committed
738
    return(loc.out)
dmattek's avatar
Mod:  
dmattek committed
739
  }) 
dmattek's avatar
dmattek committed
740
  
dmattek's avatar
dmattek committed
741
  
dmattek's avatar
Added:  
dmattek committed
742 743 744 745 746 747 748 749 750
  # download data as prepared for plotting
  # after all modification
  output$downloadDataClean <- downloadHandler(
    filename = 'tCoursesSelected_clean.csv',
    content = function(file) {
      write.csv(data4trajPlot(), file, row.names = FALSE)
    }
  )
  
751 752
  ###### Trajectory plotting
  callModule(modTrajRibbonPlot, 'modTrajRibbon', 
753 754
             in.data = data4trajPlot,
             in.fname = function() return( "tCoursesMeans.pdf"))
dmattek's avatar
dmattek committed
755
  
756
  ###### Trajectory plotting
757 758 759
  callModule(modTrajPlot, 'modTrajPlot', 
             in.data = data4trajPlot, 
             in.fname = function() {return( "tCourses.pdf")})
760 761 762
  
  ## UI for selecting trajectories
  # The output data table of data4trajPlot is modified based on inSelHighlight field
dmattek's avatar
dmattek committed
763 764
  output$varSelHighlight = renderUI({
    cat(file = stderr(), 'UI varSelHighlight\n')
dmattek's avatar
dmattek committed
765
    
dmattek's avatar
dmattek committed
766 767 768
    locBut = input$chBhighlightTraj
    if (!locBut)
      return(NULL)
dmattek's avatar
dmattek committed
769
    
dmattek's avatar
dmattek committed
770
    loc.v = getDataTrackObjLabUni()
dmattek's avatar
dmattek committed
771
    if (!is.null(loc.v)) {
772
      selectInput(
dmattek's avatar
dmattek committed
773 774 775
        'inSelHighlight',
        'Select one or more rajectories:',
        loc.v,
776
        width = '100%',
dmattek's avatar
dmattek committed
777
        multiple = TRUE
778
      )
dmattek's avatar
dmattek committed
779 780 781
    }
  })
  
782
  ###### AUC calculation and plotting
783
  callModule(modAUCplot, 'tabAUC', data4trajPlot, in.fname = function() return('boxplotAUC.pdf'))
dmattek's avatar
Added:  
dmattek committed
784
  
dmattek's avatar
Added:  
dmattek committed
785
  ###### Box-plot
786
  callModule(tabBoxPlot, 'tabBoxPlot', data4trajPlot, in.fname = function() return('boxplotTP.pdf'))
dmattek's avatar
dmattek committed
787
  
dmattek's avatar
dmattek committed
788
  ###### Scatter plot
789
  callModule(tabScatterPlot, 'tabScatter', data4trajPlot, in.fname = function() return('scatter.pdf'))
dmattek's avatar
dmattek committed
790
  
dmattek's avatar
dmattek committed
791
  ##### Hierarchical clustering
dmattek's avatar
Added:  
dmattek committed
792
  callModule(clustHier, 'tabClHier', data4clust, data4trajPlot)
dmattek's avatar
dmattek committed
793 794
  
  ##### Sparse hierarchical clustering using sparcl
dmattek's avatar
Added:  
dmattek committed
795
  callModule(clustHierSpar, 'tabClHierSpar', data4clust, data4trajPlot)
dmattek's avatar
dmattek committed
796

dmattek's avatar
Mod:  
dmattek committed
797
  
dmattek's avatar
dmattek committed
798
})