server.R 22.9 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 20 21
library(RColorBrewer)
library(sparcl) # sparse hierarchical and k-means
library(scales) # for percentages on y scale
dmattek's avatar
Added:  
dmattek committed
22 23
library(dtw) # for dynamic time warping
library(imputeTS) # for interpolating NAs
dmattek's avatar
dmattek committed
24

25
# increase file upload limit
dmattek's avatar
Added:  
dmattek committed
26
options(shiny.maxRequestSize = 200 * 1024 ^ 2)
dmattek's avatar
dmattek committed
27

dmattek's avatar
dmattek committed
28
shinyServer(function(input, output, session) {
29
  useShinyjs()
dmattek's avatar
dmattek committed
30
  
31 32 33 34 35 36
  # 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$inDataGen1),
dmattek's avatar
Added:  
dmattek committed
37 38
    dataLoadNuc  = isolate(input$inButLoadNuc),
    dataLoadTrajRem = isolate(input$inButLoadTrajRem)
39
    #dataLoadStim = isolate(input$inButLoadStim)
dmattek's avatar
dmattek committed
40 41
  )
  
dmattek's avatar
dmattek committed
42 43 44
  ####
  ## UI for side panel
  
dmattek's avatar
dmattek committed
45
  # FILE LOAD
46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
  # 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
73 74 75 76 77 78 79
  # 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
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
  # 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
113 114
  
  # COLUMN SELECTION
dmattek's avatar
dmattek committed
115 116 117
  output$varSelTrackLabel = renderUI({
    cat(file = stderr(), 'UI varSelTrackLabel\n')
    locCols = getDataNucCols()
118
    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
119 120 121
    
    selectInput(
      'inSelTrackLabel',
dmattek's avatar
dmattek committed
122
      'Select Track Label (e.g. objNuc_TrackObjects_Label):',
dmattek's avatar
dmattek committed
123 124 125 126 127 128 129 130 131
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  output$varSelTime = renderUI({
    cat(file = stderr(), 'UI varSelTime\n')
    locCols = getDataNucCols()
132
    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
133 134 135 136
    
    cat(locColSel, '\n')
    selectInput(
      'inSelTime',
dmattek's avatar
dmattek committed
137
      'Select time column (e.g. Metadata_T, RealTime):',
dmattek's avatar
dmattek committed
138 139 140 141 142 143 144 145 146 147 148 149 150
      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')
    
dmattek's avatar
dmattek committed
151 152 153 154 155
    if (input$chBgroup) {
      
      locCols = getDataNucCols()
      
      if (!is.null(locCols)) {
156 157 158
        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
159 160 161 162 163 164 165 166
        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
167 168 169 170 171 172 173
      }
    }
  })
  
  output$varSelSite = renderUI({
    cat(file = stderr(), 'UI varSelSite\n')
    
dmattek's avatar
Added:  
dmattek committed
174 175
    if (!input$chBtrackUni) {
      locCols = getDataNucCols()
176
      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
177 178 179 180 181 182 183 184 185
      
      selectInput(
        'inSelSite',
        'Select FOV (e.g. Metadata_Site or Metadata_Series):',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
dmattek's avatar
dmattek committed
186 187 188 189 190 191 192 193 194 195
  })
  
  
  
  
  output$varSelMeas1 = renderUI({
    cat(file = stderr(), 'UI varSelMeas1\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols)) {
196
      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
197

dmattek's avatar
dmattek committed
198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214
      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 / '))) {
215
      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
216

dmattek's avatar
dmattek committed
217 218 219 220 221 222 223 224 225 226
      selectInput(
        'inSelMeas2',
        'Select 2nd measurement',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
  })
  
dmattek's avatar
dmattek committed
227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250
  # 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
251
  
dmattek's avatar
dmattek committed
252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282
  # 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
283 284
        value = c(locRTmin, 0.1 * locRTmax), 
        step = 1
dmattek's avatar
dmattek committed
285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303
      )
    }
  })
  
  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
304 305
                   label = 'Normalisation grouping',
                   choices = list('Entire dataset' = 'none', 'Per facet' = 'group', 'Per trajectory (Korean way)' = 'id'))
dmattek's avatar
dmattek committed
306 307 308 309
    }
  })
  
  
dmattek's avatar
dmattek committed
310 311 312 313 314
  # UI for removing outliers
  output$uiSlOutliers = renderUI({
    cat(file = stderr(), 'UI uiSlOutliers\n')
    
    if (input$chBoutliers) {
dmattek's avatar
Mod:  
dmattek committed
315
      
dmattek's avatar
dmattek committed
316 317 318 319 320
      sliderInput(
        'slOutliersPerc',
        label = 'Percentage of middle data',
        min = 90,
        max = 100,
dmattek's avatar
Fixed:  
dmattek committed
321
        value = 99.5, 
dmattek's avatar
dmattek committed
322 323
        step = 0.1
      )
dmattek's avatar
dmattek committed
324
      
dmattek's avatar
Mod:  
dmattek committed
325
      
dmattek's avatar
dmattek committed
326 327 328
    }
  })
  
dmattek's avatar
dmattek committed
329 330 331 332 333 334 335 336 337
  output$uiTxtOutliers = renderUI({
    if (input$chBoutliers) {
      
      p("Total tracks")
      
    }
    
  })
  
dmattek's avatar
dmattek committed
338
  
dmattek's avatar
dmattek committed
339 340 341 342 343 344 345 346 347
  ####
  ## data processing
  
  # generate random dataset 1
  dataGen1 <- eventReactive(input$inDataGen1, {
    cat("dataGen1\n")
    
    return(userDataGen())
  })
348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396
  
  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
397
  getDataNucCols <- reactive({
398 399 400 401 402 403 404 405 406 407 408
    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
409
    cat(file = stderr(), 'dataMod\n')
410 411
    loc.dt = dataInBoth()
    
dmattek's avatar
dmattek committed
412
    if (is.null(loc.dt))
413 414
      return(NULL)
    
dmattek's avatar
Added:  
dmattek committed
415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434
    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$inSelTrackLabel)),
                                               sep = "_")]
      }
dmattek's avatar
Added:  
dmattek committed
435
    } else {
dmattek's avatar
Added:  
dmattek committed
436
      loc.dt[, trackObjectsLabelUni := get(input$inSelTrackLabel)]
dmattek's avatar
Added:  
dmattek committed
437 438
    }
    
dmattek's avatar
dmattek committed
439
    
dmattek's avatar
Added:  
dmattek committed
440 441 442 443 444 445
    # 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
446 447
      
      loc.dt = loc.dt[!(trackObjectsLabelUni %in% loc.dt.rem[[1]])]
dmattek's avatar
Added:  
dmattek committed
448 449
    }
    
450 451 452
    return(loc.dt)
  })
  
dmattek's avatar
dmattek committed
453 454 455 456 457
  # 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()
458
    
dmattek's avatar
dmattek committed
459 460 461 462
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt$trackObjectsLabelUni))
463 464
  })
  
dmattek's avatar
Mod:  
dmattek committed
465
  
dmattek's avatar
dmattek committed
466 467 468
  # 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
469 470 471
  getDataTpts <- reactive({
    cat(file = stderr(), 'getDataTpts\n')
    loc.dt = dataMod()
472
    
dmattek's avatar
dmattek committed
473 474 475 476
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt[[input$inSelTime]]))
477 478
  })
  
dmattek's avatar
dmattek committed
479
  
480 481 482
  
  # prepare data for plotting time courses
  # returns dt with these columns:
dmattek's avatar
dmattek committed
483
  #    realtime - selected from input
dmattek's avatar
dmattek committed
484
  #    y        - measurement selected from input
dmattek's avatar
dmattek committed
485
  #               (can be a single column or result of an operation on two cols)
486 487
  #    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
488 489
  #    group    - grouping variable for facetting from input
  #    mid.in   - column with trajectory selection status from the input file or
490 491 492 493
  #               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
494
  data4trajPlot <- reactive({
dmattek's avatar
dmattek committed
495
    cat(file = stderr(), 'data4trajPlot\n')
496 497
    
    loc.dt = dataMod()
dmattek's avatar
dmattek committed
498
    if (is.null(loc.dt))
499 500
      return(NULL)
    
501
    # create expression for 'y' column based on measurements and math operations selected in UI
dmattek's avatar
dmattek committed
502
    if (input$inSelMath == '')
503 504 505 506 507 508
      loc.s.y = input$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)
    
509
    # create expression for 'group' column
510 511
    # creates a merged column based on other columns from input
    # used for grouping of plot facets
dmattek's avatar
dmattek committed
512 513 514 515 516 517 518 519 520 521 522
    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')"
    }
523
    
dmattek's avatar
dmattek committed
524 525

    # column name with time
526 527
    loc.s.rt = input$inSelTime
    
dmattek's avatar
dmattek committed
528 529
    # Assign tracks selected for highlighting in UI
    loc.tracks.highlight = input$inSelHighlight
530
    locButHighlight = input$chBhighlightTraj
dmattek's avatar
dmattek committed
531
    
dmattek's avatar
Added:  
dmattek committed
532 533
    
    # Find column names with position
534
    loc.s.pos.x = names(loc.dt)[grep('(L|l)ocation.*X|(P|p)os.x|(P|p)osx', names(loc.dt))[1]]
535
    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
536
    
537 538 539
    cat(loc.s.pos.x, loc.s.pos.y, '\n')
    
    if (!is.na(loc.s.pos.x) & !is.na(loc.s.pos.y))
dmattek's avatar
Added:  
dmattek committed
540 541 542 543
      locPos = TRUE
    else
      locPos = FALSE
    
544 545 546 547
    
    # Find column names with ObjectNumber
    # This is different from TrackObject_Label and is handy to keep
    # because labels on segmented images are typically ObjectNumber
548 549 550 551 552 553
    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
554
      loc.s.objnum = loc.s.objnum[1]
555
      locObjNum = TRUE
dmattek's avatar
dmattek committed
556
    }
557 558
    
    
559 560
    # if dataset contains column mid.in with trajectory filtering status,
    # then, include it in plotting
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602
    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
603
        # add a column with status of track selection
604
        loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', 'NOT SEL')]
605
    }
606
      
dmattek's avatar
dmattek committed
607

608 609 610 611 612 613
    ## Interpolate NA's and data points not include
    # From: https://stackoverflow.com/questions/28073752/r-how-to-add-rows-for-missing-values-for-unique-group-sequences
    # Tracks are interpolated only within min and max realtime of every cell id
    setkey(loc.out, group, id, realtime)
    loc.out = loc.out[setkey(loc.out[, .(min(realtime):max(realtime)), by = .(group, id)], group, id, V1)]

dmattek's avatar
dmattek committed
614 615 616 617 618 619 620
    # x-check: print all rows with NA's
    print('Rows with NAs:')
    print(loc.out[rowSums(is.na(loc.out)) > 0, ])
    
    # Merge will create NA's where a realtime is missing.
    # Also, NA's may be already present in the dataset'.
    # Interpolate (linear) them with na.interpolate
621
    if(locPos)
dmattek's avatar
dmattek committed
622
      s.cols = c('y', 'pos.x', 'pos.y')
623
    else
dmattek's avatar
dmattek committed
624
      s.cols = c('y')
625 626
    
    loc.out[, (s.cols) := lapply(.SD, na.interpolation), by = id, .SDcols = s.cols]
dmattek's avatar
dmattek committed
627 628 629 630 631 632 633 634 635 636 637 638
    

    # !!! 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
639
    
640
    ## Trim x-axis (time)
dmattek's avatar
dmattek committed
641 642 643
    if(input$chBtimeTrim) {
      loc.out = loc.out[realtime >= input$slTimeTrim[[1]] & realtime <= input$slTimeTrim[[2]] ]
    }
dmattek's avatar
dmattek committed
644
    
645
    ## Normalization
dmattek's avatar
dmattek committed
646
    # F-n myNorm adds additional column with .norm suffix
dmattek's avatar
dmattek committed
647 648 649 650 651 652 653 654 655 656 657 658
    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
659 660
      # Column with normalized data is renamed to the original name
      # Further code assumes column name y produced by data4trajPlot
dmattek's avatar
dmattek committed
661 662 663 664
      loc.out[, y := NULL]
      setnames(loc.out, 'y.norm', 'y')
    }
    
dmattek's avatar
dmattek committed
665 666 667 668 669 670
    ##### 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
671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687
    # 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
688
    
dmattek's avatar
dmattek committed
689
    return(loc.out)
dmattek's avatar
dmattek committed
690 691
  })
  
dmattek's avatar
dmattek committed
692 693 694 695 696 697 698 699 700 701 702 703 704
  
  
  # 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
705
    #print(loc.dt)
dmattek's avatar
dmattek committed
706
    loc.out = dcast(loc.dt, id ~ realtime, value.var = 'y')
dmattek's avatar
Added:  
dmattek committed
707
    #print(loc.out)
dmattek's avatar
dmattek committed
708 709
    loc.rownames = loc.out$id
    
dmattek's avatar
Mod:  
dmattek committed
710
    
dmattek's avatar
dmattek committed
711 712
    loc.out = as.matrix(loc.out[, -1])
    rownames(loc.out) = loc.rownames
dmattek's avatar
Added:  
dmattek committed
713 714 715 716 717 718 719
    
    # 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
    loc.out = t(na.interpolation(t(loc.out)))
    
dmattek's avatar
dmattek committed
720
    return(loc.out)
dmattek's avatar
Mod:  
dmattek committed
721
  }) 
dmattek's avatar
dmattek committed
722
  
dmattek's avatar
dmattek committed
723
  
dmattek's avatar
Added:  
dmattek committed
724 725 726 727 728 729 730 731 732
  # 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)
    }
  )
  
dmattek's avatar
dmattek committed
733
  
dmattek's avatar
dmattek committed
734 735 736 737
  ####
  ## UI for trajectory plot
  output$varSelHighlight = renderUI({
    cat(file = stderr(), 'UI varSelHighlight\n')
dmattek's avatar
dmattek committed
738
    
dmattek's avatar
dmattek committed
739 740 741
    locBut = input$chBhighlightTraj
    if (!locBut)
      return(NULL)
dmattek's avatar
dmattek committed
742
    
dmattek's avatar
dmattek committed
743
    loc.v = getDataTrackObjLabUni()
dmattek's avatar
dmattek committed
744
    if (!is.null(loc.v)) {
745
      selectInput(
dmattek's avatar
dmattek committed
746 747 748
        'inSelHighlight',
        'Select one or more rajectories:',
        loc.v,
749
        width = '100%',
dmattek's avatar
dmattek committed
750
        multiple = TRUE
751
      )
dmattek's avatar
dmattek committed
752 753 754
    }
  })
  
dmattek's avatar
Added:  
dmattek committed
755
  ###### Trajectory plotting
dmattek's avatar
Mod:  
dmattek committed
756
  callModule(modTrajPlot, 'modTrajPlot', data4trajPlot)
dmattek's avatar
dmattek committed
757
  
dmattek's avatar
Added:  
dmattek committed
758 759 760
  ###### AUC caluclation and plotting
  callModule(modAUCplot, 'tabAUC', data4trajPlot)
  
dmattek's avatar
Added:  
dmattek committed
761 762
  ###### Box-plot
  callModule(tabBoxPlot, 'tabBoxPlot', data4trajPlot)
dmattek's avatar
dmattek committed
763
  
dmattek's avatar
dmattek committed
764 765
  
  
dmattek's avatar
dmattek committed
766 767 768
  ###### Scatter plot
  callModule(tabScatterPlot, 'tabScatter', data4trajPlot)
  
dmattek's avatar
dmattek committed
769
  ##### Hierarchical clustering
dmattek's avatar
Added:  
dmattek committed
770
  callModule(clustHier, 'tabClHier', data4clust, data4trajPlot)
dmattek's avatar
dmattek committed
771 772
  
  ##### Sparse hierarchical clustering using sparcl
dmattek's avatar
Added:  
dmattek committed
773
  callModule(clustHierSpar, 'tabClHierSpar', data4clust, data4trajPlot)
dmattek's avatar
dmattek committed
774

dmattek's avatar
Mod:  
dmattek committed
775
  
dmattek's avatar
dmattek committed
776
})