server.R 25.3 KB
Newer Older
dmattek's avatar
dmattek committed
1
#
dmattek's avatar
dmattek committed
2 3 4 5
# Time Course Inspector: Shiny app for plotting time series data
# Author: Maciej Dobrzynski
#
# This is the server logic for a Shiny web application.
dmattek's avatar
dmattek committed
6 7 8 9 10 11
#

library(shiny)
library(shinyjs) #http://deanattali.com/shinyjs/
library(data.table)
library(ggplot2)
dmattek's avatar
dmattek committed
12
library(gplots) # for heatmap.2
dmattek's avatar
dmattek committed
13
library(plotly)
dmattek's avatar
dmattek committed
14 15
library(d3heatmap) # for interactive heatmap
library(dendextend) # for color_branches
16
library(colorspace) # for palettes (used to colour dendrogram)
dmattek's avatar
dmattek committed
17
library(RColorBrewer)
dmattek's avatar
dmattek committed
18
library(sparcl) # sparse hierarchical and k-means
dmattek's avatar
dmattek committed
19
library(scales) # for percentages on y scale
dmattek's avatar
dmattek committed
20 21
library(dtw) # for dynamic time warping
library(imputeTS) # for interpolating NAs
dmattek's avatar
dmattek committed
22

23
# Global parameters ----
dmattek's avatar
dmattek committed
24
# change to increase the limit of the upload file size
dmattek's avatar
dmattek committed
25
options(shiny.maxRequestSize = 200 * 1024 ^ 2)
dmattek's avatar
dmattek committed
26

dmattek's avatar
dmattek committed
27
# Server logic ----
28
shinyServer(function(input, output, session) {
29
  useShinyjs()
dmattek's avatar
dmattek committed
30
  
31
  # This is only set at session start
dmattek's avatar
dmattek committed
32
  # We use this as a way to determine which input was
33 34
  # clicked in the dataInBoth reactive
  counter <- reactiveValues(
dmattek's avatar
dmattek committed
35 36 37
    # The value of actionButton is the number of times the button is pressed
    dataGen1        = isolate(input$inDataGen1),
    dataLoadNuc     = isolate(input$inButLoadNuc),
38 39
    dataLoadTrajRem = isolate(input$inButLoadTrajRem),
    dataLoadStim    = isolate(input$inButLoadStim)
dmattek's avatar
dmattek committed
40
  )
dmattek's avatar
dmattek committed
41 42 43 44 45 46 47 48 49

  nCellsCounter <- reactiveValues(
    nCellsOrig = 0,
    nCellsAfterOutlierTrim = 0
  )
    
  myReactVals = reactiveValues(
    outlierIDs = NULL
  )
dmattek's avatar
dmattek committed
50
  
dmattek's avatar
dmattek committed
51
  # UI-side-panel-data-load ----
dmattek's avatar
dmattek committed
52
  
dmattek's avatar
dmattek committed
53
  # Generate random dataset
54 55 56
  dataGen1 <- eventReactive(input$inDataGen1, {
    cat("dataGen1\n")
    
dmattek's avatar
dmattek committed
57
    return(LOCgenTraj(in.nwells = 3, in.addout = 3))
58 59
  })
  
dmattek's avatar
dmattek committed
60
  # Load main data file
61 62 63 64 65 66 67 68 69 70 71 72 73
  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
74 75 76 77
  # This button will reset the inFileLoad
  observeEvent(input$butReset, {
    reset("inFileLoadNuc")  # reset is a shinyjs function
  })
78

dmattek's avatar
dmattek committed
79
  # Load data with trajectories to remove
80 81 82 83 84 85 86 87 88 89 90 91
  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
92
  
dmattek's avatar
dmattek committed
93
  # Load data with stimulation pattern
94 95 96 97 98 99 100 101 102 103 104 105 106 107
  dataLoadStim <- eventReactive(input$inButLoadStim, {
    cat(file = stderr(), "dataLoadStim\n")
    locFilePath = input$inFileLoadStim$datapath
    
    counter$dataLoadStim <- input$inButLoadStim - 1
    
    if (is.null(locFilePath) || locFilePath == '')
      return(NULL)
    else {
      return(fread(locFilePath))
    }
  })
  
    
dmattek's avatar
dmattek committed
108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
  # 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")
  })

127 128 129
  # UI for loading csv with stimulation pattern
  output$uiFileLoadStim = renderUI({
    cat(file = stderr(), 'UI uiFileLoadStim\n')
dmattek's avatar
dmattek committed
130
    
131 132 133 134 135 136 137 138 139 140
    if(input$chBstim) 
      fileInput(
        'inFileLoadStim',
        'Select data file (e.g. stim.csv) and press "Load Data"',
        accept = c('text/csv', 'text/comma-separated-values,text/plain')
      )
  })
  
  output$uiButLoadStim = renderUI({
    cat(file = stderr(), 'UI uiButLoadStim\n')
dmattek's avatar
dmattek committed
141
    
142 143
    if(input$chBstim)
      actionButton("inButLoadStim", "Load Data")
dmattek's avatar
dmattek committed
144 145
  })
  
146

dmattek's avatar
dmattek committed
147
  
dmattek's avatar
dmattek committed
148
  # UI-side-panel-column-selection ----
dmattek's avatar
dmattek committed
149 150 151
  output$varSelTrackLabel = renderUI({
    cat(file = stderr(), 'UI varSelTrackLabel\n')
    locCols = getDataNucCols()
152
    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
153 154 155
    
    selectInput(
      'inSelTrackLabel',
dmattek's avatar
dmattek committed
156
      'Select Track Label (e.g. objNuc_TrackObjects_Label):',
dmattek's avatar
dmattek committed
157 158 159 160 161 162 163 164 165
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  output$varSelTime = renderUI({
    cat(file = stderr(), 'UI varSelTime\n')
    locCols = getDataNucCols()
166
    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
167 168 169
    
    selectInput(
      'inSelTime',
dmattek's avatar
dmattek committed
170
      'Select time column (e.g. Metadata_T, RealTime):',
dmattek's avatar
dmattek committed
171 172 173 174 175
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
176 177 178 179

  output$varSelTimeFreq = renderUI({
    cat(file = stderr(), 'UI varSelTimeFreq\n')
    
180 181 182 183 184 185 186 187 188 189
    if (input$chBtrajInter) {
      numericInput(
        'inSelTimeFreq',
        'Provide time frequency:',
        min = 1,
        step = 1,
        width = '100%',
        value = 1
      )
    }
190
  })
dmattek's avatar
dmattek committed
191
  
dmattek's avatar
dmattek committed
192
  # This is the main field to select plot facet grouping
dmattek's avatar
dmattek committed
193
  # It's typically a column with the entire experimental description,
dmattek's avatar
dmattek committed
194 195
  # e.g.1 Stim_All_Ch or Stim_All_S.
  # e.g.2 a combination of 3 columns called Stimulation_...
dmattek's avatar
dmattek committed
196 197 198
  output$varSelGroup = renderUI({
    cat(file = stderr(), 'UI varSelGroup\n')
    
dmattek's avatar
dmattek committed
199 200 201 202 203
    if (input$chBgroup) {
      
      locCols = getDataNucCols()
      
      if (!is.null(locCols)) {
204 205 206
        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
207 208 209 210 211 212 213 214
        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
215 216 217 218 219 220 221
      }
    }
  })
  
  output$varSelSite = renderUI({
    cat(file = stderr(), 'UI varSelSite\n')
    
222
    if (input$chBtrackUni) {
dmattek's avatar
dmattek committed
223
      locCols = getDataNucCols()
224
      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
dmattek committed
225 226 227 228 229 230 231 232 233
      
      selectInput(
        'inSelSite',
        'Select FOV (e.g. Metadata_Site or Metadata_Series):',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
dmattek's avatar
dmattek committed
234 235 236 237 238 239 240 241
  })
  
  
  output$varSelMeas1 = renderUI({
    cat(file = stderr(), 'UI varSelMeas1\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols)) {
242
      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
243

dmattek's avatar
dmattek committed
244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260
      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 / '))) {
261
      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
262

dmattek's avatar
dmattek committed
263 264 265 266 267 268 269 270 271 272
      selectInput(
        'inSelMeas2',
        'Select 2nd measurement',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
  })
  
dmattek's avatar
dmattek committed
273
  # UI-side-panel-trim x-axis (time) ----
dmattek's avatar
dmattek committed
274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296
  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
297
  
dmattek's avatar
dmattek committed
298
  # UI-side-panel-normalization ----
dmattek's avatar
dmattek committed
299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327
  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
328 329
        value = c(locRTmin, 0.1 * locRTmax), 
        step = 1
dmattek's avatar
dmattek committed
330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348
      )
    }
  })
  
  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
349
                   label = 'Normalisation grouping',
350
                   choices = list('Entire dataset' = 'none', 'Per facet' = 'group', 'Per trajectory' = 'id'))
dmattek's avatar
dmattek committed
351 352 353 354
    }
  })
  
  
dmattek's avatar
dmattek committed
355
  # UI-main-tab-remove-outliers ----
dmattek's avatar
dmattek committed
356 357 358 359
  output$uiSlOutliers = renderUI({
    cat(file = stderr(), 'UI uiSlOutliers\n')
    
    if (input$chBoutliers) {
dmattek's avatar
Mod:  
dmattek committed
360
      
dmattek's avatar
dmattek committed
361 362 363 364 365
      sliderInput(
        'slOutliersPerc',
        label = 'Percentage of middle data',
        min = 90,
        max = 100,
dmattek's avatar
dmattek committed
366
        value = 99.5, 
dmattek's avatar
dmattek committed
367 368
        step = 0.1
      )
dmattek's avatar
dmattek committed
369
      
dmattek's avatar
Mod:  
dmattek committed
370
      
dmattek's avatar
dmattek committed
371 372 373
    }
  })
  
dmattek's avatar
dmattek committed
374 375 376 377 378 379 380 381 382 383 384 385 386 387
  output$uiTxtOutliers = renderUI({
    cat(file = stderr(), 'UI uiTxtOutliers\n')
    
    if (input$chBoutliers) {
      htmlOutput(
        'txtOutliersPerc'
      )
    }
  })
  
  output$txtOutliersPerc <- renderText({ 
    sprintf('<b>#tracks: %d <br>#outliers: %d</b>', 
            nCellsCounter[['nCellsOrig']], 
            nCellsCounter[['nCellsOrig']] - nCellsCounter[['nCellsAfterOutlierTrim']])  })
dmattek's avatar
dmattek committed
388
  
dmattek's avatar
dmattek committed
389

dmattek's avatar
dmattek committed
390
  # Processing-data ----
dmattek's avatar
dmattek committed
391
  
392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439
  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
440
  getDataNucCols <- reactive({
441 442 443 444 445 446 447 448 449 450 451
    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
452
    cat(file = stderr(), 'dataMod\n')
453 454
    loc.dt = dataInBoth()
    
dmattek's avatar
dmattek committed
455
    if (is.null(loc.dt))
456 457
      return(NULL)
    
458
    if (input$chBtrackUni) {
dmattek's avatar
dmattek committed
459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477
      loc.types = lapply(loc.dt, class)
      if(loc.types[[input$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$inSelTrackLabel)),
                                               sep = "_")]
      }
dmattek's avatar
dmattek committed
478
    } else {
dmattek's avatar
dmattek committed
479
      loc.dt[, trackObjectsLabelUni := get(input$inSelTrackLabel)]
dmattek's avatar
dmattek committed
480 481
    }
    
dmattek's avatar
dmattek committed
482
    
dmattek's avatar
dmattek committed
483 484 485 486 487 488
    # 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
489
      loc.dt = loc.dt[!(trackObjectsLabelUni %in% loc.dt.rem[[1]])]
dmattek's avatar
dmattek committed
490 491
    }
    
492 493 494
    return(loc.dt)
  })
  
dmattek's avatar
dmattek committed
495 496 497 498 499
  # 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()
500
    
dmattek's avatar
dmattek committed
501 502 503 504
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt$trackObjectsLabelUni))
505 506
  })
  
dmattek's avatar
Mod:  
dmattek committed
507
  
dmattek's avatar
dmattek committed
508 509 510
  # 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
511 512 513
  getDataTpts <- reactive({
    cat(file = stderr(), 'getDataTpts\n')
    loc.dt = dataMod()
514
    
dmattek's avatar
dmattek committed
515 516 517 518
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt[[input$inSelTime]]))
519 520
  })
  
dmattek's avatar
dmattek committed
521
  
522 523 524
  
  # prepare data for plotting time courses
  # returns dt with these columns:
dmattek's avatar
dmattek committed
525
  #    realtime - selected from input
dmattek's avatar
dmattek committed
526
  #    y        - measurement selected from input
dmattek's avatar
dmattek committed
527
  #               (can be a single column or result of an operation on two cols)
528 529
  #    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
530 531
  #    group    - grouping variable for facetting from input
  #    mid.in   - column with trajectory selection status from the input file or
532 533 534 535
  #               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
536
  data4trajPlot <- reactive({
dmattek's avatar
dmattek committed
537
    cat(file = stderr(), 'data4trajPlot\n')
538 539
    
    loc.dt = dataMod()
dmattek's avatar
dmattek committed
540
    if (is.null(loc.dt))
541 542
      return(NULL)
    
543
    # create expression for 'y' column based on measurements and math operations selected in UI
dmattek's avatar
dmattek committed
544
    if (input$inSelMath == '')
545 546 547 548 549 550
      loc.s.y = input$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)
    
551
    # create expression for 'group' column
552 553
    # creates a merged column based on other columns from input
    # used for grouping of plot facets
dmattek's avatar
dmattek committed
554 555 556 557 558 559 560 561 562 563 564
    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')"
    }
565
    
dmattek's avatar
dmattek committed
566 567

    # column name with time
568 569
    loc.s.rt = input$inSelTime
    
dmattek's avatar
dmattek committed
570 571
    # Assign tracks selected for highlighting in UI
    loc.tracks.highlight = input$inSelHighlight
572
    locButHighlight = input$chBhighlightTraj
dmattek's avatar
dmattek committed
573
    
dmattek's avatar
dmattek committed
574 575
    
    # Find column names with position
576
    loc.s.pos.x = names(loc.dt)[grep('(L|l)ocation.*X|(P|p)os.x|(P|p)osx', names(loc.dt))[1]]
577
    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
dmattek committed
578
    
579
    cat('Position columns: ', loc.s.pos.x, loc.s.pos.y, '\n')
580 581
    
    if (!is.na(loc.s.pos.x) & !is.na(loc.s.pos.y))
dmattek's avatar
dmattek committed
582 583 584 585
      locPos = TRUE
    else
      locPos = FALSE
    
586 587 588 589
    
    # Find column names with ObjectNumber
    # This is different from TrackObject_Label and is handy to keep
    # because labels on segmented images are typically ObjectNumber
590 591 592 593 594 595
    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
596
      loc.s.objnum = loc.s.objnum[1]
597
      locObjNum = TRUE
dmattek's avatar
dmattek committed
598
    }
599 600
    
    
601 602
    # if dataset contains column mid.in with trajectory filtering status,
    # then, include it in plotting
603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644
    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
645
        # add a column with status of track selection
646
        loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', 'NOT SEL')]
647
    }
648
      
dmattek's avatar
dmattek committed
649

650
    ## Interpolate missing data and NA data points
651
    # From: https://stackoverflow.com/questions/28073752/r-how-to-add-rows-for-missing-values-for-unique-group-sequences
652 653 654
    # 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.
655
    # Therefore, we cannot rely on that info to get time frequency; user provides this number!
656
    
657 658
    setkey(loc.out, group, id, realtime)

659 660
    if (input$chBtrajInter) {
      # here we fill missing data with NA's
dmattek's avatar
dmattek committed
661
      loc.out = loc.out[setkeyv(loc.out[, .(seq(min(get(COLRT), na.rm = T), max(get(COLRT), na.rm = T), input$inSelTimeFreq)), by = c(COLGR, COLID)], c(COLGR, COLID, 'V1'))]
662 663 664 665 666 667 668 669
      
      # 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)
dmattek's avatar
dmattek committed
670
        s.cols = c(COLY, COLPOSX, COLPOSY)
671
      else
dmattek's avatar
dmattek committed
672
        s.cols = c(COLY)
673
      
dmattek's avatar
dmattek committed
674
      loc.out[, (s.cols) := lapply(.SD, na.interpolation), by = c(COLID), .SDcols = s.cols]
675 676 677 678 679 680 681 682 683 684 685 686 687 688
      
      
      # !!! 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
689
    
690
    ## Trim x-axis (time)
dmattek's avatar
dmattek committed
691
    if(input$chBtimeTrim) {
dmattek's avatar
dmattek committed
692
      loc.out = loc.out[get(COLRT) >= input$slTimeTrim[[1]] & get(COLRT) <= input$slTimeTrim[[2]] ]
dmattek's avatar
dmattek committed
693
    }
dmattek's avatar
dmattek committed
694
    
695
    ## Normalization
dmattek's avatar
dmattek committed
696
    # F-n normTraj adds additional column with .norm suffix
dmattek's avatar
dmattek committed
697
    if (input$chBnorm) {
dmattek's avatar
dmattek committed
698
      loc.out = LOCnormTraj(
dmattek's avatar
dmattek committed
699
        in.dt = loc.out,
dmattek's avatar
dmattek committed
700 701
        in.meas.col = COLY,
        in.rt.col = COLRT,
dmattek's avatar
dmattek committed
702 703 704 705 706 707 708
        in.rt.min = input$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
709 710
      # Column with normalized data is renamed to the original name
      # Further code assumes column name y produced by data4trajPlot
dmattek's avatar
dmattek committed
711 712
      loc.out[, get(COLY) := NULL]
      setnames(loc.out, 'y.norm', COLY)
dmattek's avatar
dmattek committed
713 714 715
    }
    
    return(loc.out)
dmattek's avatar
dmattek committed
716 717
  })
  
dmattek's avatar
dmattek committed
718 719 720 721 722 723 724 725
  
  # prepare data for clustering
  # return a matrix with:
  # cells as columns
  # time points as rows
  data4clust <- reactive({
    cat(file = stderr(), 'data4clust\n')
    
dmattek's avatar
dmattek committed
726
    loc.dt = data4trajPlotNoOut()
dmattek's avatar
dmattek committed
727 728 729
    if (is.null(loc.dt))
      return(NULL)
    
dmattek's avatar
dmattek committed
730
    #print(loc.dt)
dmattek's avatar
dmattek committed
731
    loc.out = dcast(loc.dt, id ~ realtime, value.var = 'y')
dmattek's avatar
dmattek committed
732
    #print(loc.out)
dmattek's avatar
dmattek committed
733 734
    loc.rownames = loc.out$id
    
dmattek's avatar
Mod:  
dmattek committed
735
    
dmattek's avatar
dmattek committed
736 737
    loc.out = as.matrix(loc.out[, -1])
    rownames(loc.out) = loc.rownames
dmattek's avatar
dmattek committed
738
    
739 740
    # 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
dmattek committed
741 742 743 744
    # 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
745
    # loc.out = t(na.interpolation(t(loc.out)))
dmattek's avatar
dmattek committed
746
    
dmattek's avatar
dmattek committed
747
    return(loc.out)
dmattek's avatar
Mod:  
dmattek committed
748
  }) 
749
  
dmattek's avatar
dmattek committed
750
  
751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766
  # prepare data with stimulation pattern
  # this dataset is displayed underneath of trajectory plot (modules/trajPlot.R) as geom_segment
  data4stimPlot <- reactive({
    cat(file = stderr(), 'data4stimPlot\n')
    
    if (input$chBstim) {
      cat(file = stderr(), 'data4stimPlot: stim not NULL\n')
      
      loc.dt.stim = dataLoadStim()
      return(loc.dt.stim)
    } else {
      cat(file = stderr(), 'data4stimPlot: stim is NULL\n')
      return(NULL)
    }
  })
  
dmattek's avatar
dmattek committed
767 768 769
  # download data as prepared for plotting
  # after all modification
  output$downloadDataClean <- downloadHandler(
dmattek's avatar
dmattek committed
770
    filename = FCSVTCCLEAN,
dmattek's avatar
dmattek committed
771
    content = function(file) {
dmattek's avatar
dmattek committed
772
      write.csv(data4trajPlotNoOut(), file, row.names = FALSE)
dmattek's avatar
dmattek committed
773 774 775
    }
  )
  
dmattek's avatar
dmattek committed
776 777 778
  # Plotting-trajectories ----

  # UI for selecting trajectories
779
  # The output data table of data4trajPlot is modified based on inSelHighlight field
dmattek's avatar
dmattek committed
780 781
  output$varSelHighlight = renderUI({
    cat(file = stderr(), 'UI varSelHighlight\n')
dmattek's avatar
dmattek committed
782
    
dmattek's avatar
dmattek committed
783 784 785
    locBut = input$chBhighlightTraj
    if (!locBut)
      return(NULL)
dmattek's avatar
dmattek committed
786
    
dmattek's avatar
dmattek committed
787
    loc.v = getDataTrackObjLabUni()
dmattek's avatar
dmattek committed
788
    if (!is.null(loc.v)) {
789
      selectInput(
dmattek's avatar
dmattek committed
790 791 792
        'inSelHighlight',
        'Select one or more rajectories:',
        loc.v,
793
        width = '100%',
dmattek's avatar
dmattek committed
794
        multiple = TRUE
795
      )
dmattek's avatar
dmattek committed
796 797 798
    }
  })
  
dmattek's avatar
dmattek committed
799 800 801
  # Taking out outliers 
  data4trajPlotNoOut = callModule(modSelOutliers, 'returnOutlierIDs', data4trajPlot)
  
dmattek's avatar
dmattek committed
802 803
  # Trajectory plotting - ribbon
  callModule(modTrajRibbonPlot, 'modTrajRibbon', 
dmattek's avatar
dmattek committed
804
             in.data = data4trajPlotNoOut,
dmattek's avatar
dmattek committed
805
             in.data.stim = data4stimPlot,
dmattek's avatar
dmattek committed
806
             in.fname = function() return(FPDFTCMEAN))
dmattek's avatar
dmattek committed
807
  
dmattek's avatar
dmattek committed
808
  # Trajectory plotting - individual
dmattek's avatar
dmattek committed
809
  callModule(modTrajPlot, 'modTrajPlot', 
dmattek's avatar
dmattek committed
810
             in.data = data4trajPlotNoOut, 
dmattek's avatar
dmattek committed
811
             in.data.stim = data4stimPlot,
dmattek's avatar
dmattek committed
812
             in.fname = function() {return(FPDFTCSINGLE)})
dmattek's avatar
dmattek committed
813 814 815
  
  
  # Tabs ----
816
  ###### AUC calculation and plotting
dmattek's avatar
dmattek committed
817
  callModule(modAUCplot, 'tabAUC', data4trajPlotNoOut, in.fname = function() return(FPDFBOXAUC))
dmattek's avatar
dmattek committed
818
  
dmattek's avatar
dmattek committed
819
  ###### Box-plot
dmattek's avatar
dmattek committed
820
  callModule(tabBoxPlot, 'tabBoxPlot', data4trajPlotNoOut, in.fname = function() return(FPDFBOXTP))
dmattek's avatar
dmattek committed
821
  
dmattek's avatar
dmattek committed
822
  ###### Scatter plot
dmattek's avatar
dmattek committed
823
  callModule(tabScatterPlot, 'tabScatter', data4trajPlotNoOut, in.fname = function() return(FPDFSCATTER))
dmattek's avatar
dmattek committed
824
  
dmattek's avatar
dmattek committed
825
  ##### Hierarchical clustering
dmattek's avatar
dmattek committed
826
  callModule(clustHier, 'tabClHier', data4clust, data4trajPlotNoOut, data4stimPlot)
dmattek's avatar
dmattek committed
827 828
  
  ##### Sparse hierarchical clustering using sparcl
dmattek's avatar
dmattek committed
829
  callModule(clustHierSpar, 'tabClHierSpar', data4clust, data4trajPlotNoOut, data4stimPlot)
dmattek's avatar
dmattek committed
830

dmattek's avatar
Mod:  
dmattek committed
831
  
dmattek's avatar
dmattek committed
832
})