server.R 37.6 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
dmattek's avatar
Mod:  
dmattek committed
18
library(colorspace) # for palettes (ised 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
dmattek committed
22

23
# increase file upload limit
dmattek's avatar
Added:  
dmattek committed
24
options(shiny.maxRequestSize = 80 * 1024 ^ 2)
dmattek's avatar
dmattek committed
25

dmattek's avatar
dmattek committed
26
shinyServer(function(input, output, session) {
27
  useShinyjs()
dmattek's avatar
dmattek committed
28
  
29 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$inDataGen1),
    dataLoadNuc  = isolate(input$inButLoadNuc)
    #dataLoadStim = isolate(input$inButLoadStim)
dmattek's avatar
dmattek committed
37 38
  )
  
dmattek's avatar
dmattek committed
39 40 41
  ####
  ## UI for side panel
  
dmattek's avatar
dmattek committed
42
  # FILE LOAD
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69
  # 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
70 71 72 73 74 75 76
  # This button will reset the inFileLoad
  observeEvent(input$butReset, {
    reset("inFileLoadNuc")  # reset is a shinyjs function
    #    reset("inFileStimLoad")  # reset is a shinyjs function
    
  })
  
dmattek's avatar
dmattek committed
77 78
  
  # COLUMN SELECTION
dmattek's avatar
dmattek committed
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
  output$varSelTrackLabel = renderUI({
    cat(file = stderr(), 'UI varSelTrackLabel\n')
    locCols = getDataNucCols()
    locColSel = locCols[locCols %like% 'rack'][1] # index 1 at the end in case more matches; select 1st
    
    selectInput(
      'inSelTrackLabel',
      'Select Track Label (e.g. objNuc_Track_ObjectsLabel):',
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  output$varSelTime = renderUI({
    cat(file = stderr(), 'UI varSelTime\n')
    locCols = getDataNucCols()
    locColSel = locCols[locCols %like% 'RealTime'][1] # index 1 at the end in case more matches; select 1st
    
    cat(locColSel, '\n')
    selectInput(
      'inSelTime',
      'Select time column (e.g. RealTime):',
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  # This is main field to select plot facet grouping
  # It's typically a column with the entire experimental description,
  # e.g. in Yannick's case it's Stim_All_Ch or Stim_All_S.
  # In Coralie's case it's a combination of 3 columns called Stimulation_...
  output$varSelGroup = renderUI({
    cat(file = stderr(), 'UI varSelGroup\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols)) {
      locColSel = locCols[locCols %like% 'ite']
      if (length(locColSel) == 0)
        locColSel = locCols[locCols %like% 'eries'][1] # index 1 at the end in case more matches; select 1st
      else if (length(locColSel) > 1) {
        locColSel = locColSel[1]
      }
      #    cat('UI varSelGroup::locColSel ', locColSel, '\n')
      selectInput(
        'inSelGroup',
        'Select one or more facet groupings (e.g. Site, Well, Channel):',
        locCols,
        width = '100%',
        selected = locColSel,
        multiple = TRUE
      )
    }
    
  })
  
  output$varSelSite = renderUI({
    cat(file = stderr(), 'UI varSelSite\n')
    
dmattek's avatar
Added:  
dmattek committed
139 140 141 142 143 144 145 146 147 148 149 150
    if (!input$chBtrackUni) {
      locCols = getDataNucCols()
      locColSel = locCols[locCols %like% 'ite'][1] # index 1 at the end in case more matches; select 1st
      
      selectInput(
        'inSelSite',
        'Select FOV (e.g. Metadata_Site or Metadata_Series):',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
dmattek's avatar
dmattek committed
151 152 153 154 155 156 157 158 159 160
  })
  
  
  
  
  output$varSelMeas1 = renderUI({
    cat(file = stderr(), 'UI varSelMeas1\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols)) {
dmattek's avatar
dmattek committed
161 162
      locColSel = locCols[locCols %like% 'objCyto_Intensity_MeanIntensity_imErkCor.*' |
                            locCols %like% 'Ratio'][1] # index 1 at the end in case more matches; select 1st
dmattek's avatar
dmattek committed
163

dmattek's avatar
dmattek committed
164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181
      selectInput(
        'inSelMeas1',
        'Select 1st measurement:',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
  })
  
  
  output$varSelMeas2 = renderUI({
    cat(file = stderr(), 'UI varSelMeas2\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols) &&
        !(input$inSelMath %in% c('', '1 / '))) {
      locColSel = locCols[locCols %like% 'objNuc_Intensity_MeanIntensity_imErkCor.*'][1] # index 1 at the end in case more matches; select 1st
dmattek's avatar
dmattek committed
182

dmattek's avatar
dmattek committed
183 184 185 186 187 188 189 190 191 192
      selectInput(
        'inSelMeas2',
        'Select 2nd measurement',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
  })
  
dmattek's avatar
dmattek committed
193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
  # 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
217
  
dmattek's avatar
dmattek committed
218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248
  # 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
249 250
        value = c(locRTmin, 0.1 * locRTmax), 
        step = 1
dmattek's avatar
dmattek committed
251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
      )
    }
  })
  
  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
270 271
                   label = 'Normalisation grouping',
                   choices = list('Entire dataset' = 'none', 'Per facet' = 'group', 'Per trajectory (Korean way)' = 'id'))
dmattek's avatar
dmattek committed
272 273 274 275
    }
  })
  
  
dmattek's avatar
dmattek committed
276 277 278 279 280 281
  # UI for removing outliers
  
  output$uiSlOutliers = renderUI({
    cat(file = stderr(), 'UI uiSlOutliers\n')
    
    if (input$chBoutliers) {
dmattek's avatar
Mod:  
dmattek committed
282
      
dmattek's avatar
dmattek committed
283 284 285 286 287 288 289 290
      sliderInput(
        'slOutliersPerc',
        label = 'Percentage of middle data',
        min = 90,
        max = 100,
        value = 99, 
        step = 0.1
      )
dmattek's avatar
dmattek committed
291
      
dmattek's avatar
Mod:  
dmattek committed
292
      
dmattek's avatar
dmattek committed
293 294 295
    }
  })
  
dmattek's avatar
dmattek committed
296 297 298 299 300 301 302 303 304
  output$uiTxtOutliers = renderUI({
    if (input$chBoutliers) {
      
      p("Total tracks")
      
    }
    
  })
  
dmattek's avatar
dmattek committed
305
  
dmattek's avatar
dmattek committed
306 307 308 309 310 311 312 313 314
  ####
  ## data processing
  
  # generate random dataset 1
  dataGen1 <- eventReactive(input$inDataGen1, {
    cat("dataGen1\n")
    
    return(userDataGen())
  })
315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363
  
  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
364
  getDataNucCols <- reactive({
365 366 367 368 369 370 371 372 373 374 375
    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
376
    cat(file = stderr(), 'dataMod\n')
377 378
    loc.dt = dataInBoth()
    
dmattek's avatar
dmattek committed
379
    if (is.null(loc.dt))
380 381
      return(NULL)
    
dmattek's avatar
Added:  
dmattek committed
382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401
    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$inSelTrackLabel)),
                                               sep = "_")]
      }
dmattek's avatar
Added:  
dmattek committed
402
    } else {
dmattek's avatar
Added:  
dmattek committed
403
      loc.dt[, trackObjectsLabelUni := get(input$inSelTrackLabel)]
dmattek's avatar
Added:  
dmattek committed
404 405
    }
    
dmattek's avatar
dmattek committed
406
    
407 408 409
    return(loc.dt)
  })
  
dmattek's avatar
dmattek committed
410 411 412 413 414
  # 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()
415
    
dmattek's avatar
dmattek committed
416 417 418 419
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt$trackObjectsLabelUni))
420 421
  })
  
dmattek's avatar
dmattek committed
422
  # return all unique track object labels (created in dataMod)
dmattek's avatar
dmattek committed
423
  # This will be used to display in UI for trajectory highlighting
dmattek's avatar
dmattek committed
424 425 426 427 428 429 430 431 432
  getDataTrackObjLabUni_afterTrim <- reactive({
    cat(file = stderr(), 'getDataTrackObjLabUni_afterTrim\n')
    loc.dt = data4trajPlot()
    
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt$id))
  })
dmattek's avatar
Mod:  
dmattek committed
433
  
dmattek's avatar
dmattek committed
434 435 436
  # 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
437 438 439
  getDataTpts <- reactive({
    cat(file = stderr(), 'getDataTpts\n')
    loc.dt = dataMod()
440
    
dmattek's avatar
dmattek committed
441 442 443 444
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt[[input$inSelTime]]))
445 446
  })
  
dmattek's avatar
dmattek committed
447 448 449 450 451 452 453 454 455 456 457 458 459
  # return dt with cell IDs and their corresponding condition name
  # The condition is the column defined by facet groupings
  getDataCond <- reactive({
    cat(file = stderr(), 'getDataCond\n')
    loc.dt = data4trajPlot()
    
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt[, .(id, group)]))
    
  })
  
460 461 462
  
  # prepare data for plotting time courses
  # returns dt with these columns:
dmattek's avatar
dmattek committed
463
  #    realtime - selected from input
dmattek's avatar
dmattek committed
464
  #    y        - measurement selected from input
dmattek's avatar
dmattek committed
465 466 467 468 469
  #               (can be a single column or result of an operation on two cols)
  #    id       - trackObjectsLabelUni (created in dataMod)
  #    group    - grouping variable for facetting from input
  #    mid.in   - column with trajectory selection status from the input file or
  #               highlight status from UI
470
  data4trajPlot <- reactive({
dmattek's avatar
dmattek committed
471
    cat(file = stderr(), 'data4trajPlot\n')
472 473
    
    loc.dt = dataMod()
dmattek's avatar
dmattek committed
474
    if (is.null(loc.dt))
475 476 477
      return(NULL)
    
    
dmattek's avatar
dmattek committed
478
    if (input$inSelMath == '')
479 480 481 482 483 484 485 486 487
      loc.s.y = input$inSelMeas1
    else if (input$inSelMath == '1 / ')
      loc.s.y = paste0(input$inSelMath, input$inSelMeas1)
    else
      loc.s.y = paste0(input$inSelMeas1, input$inSelMath, input$inSelMeas2)
    
    # create expression for parsing
    # creates a merged column based on other columns from input
    # used for grouping of plot facets
dmattek's avatar
dmattek committed
488 489 490 491
    if(length(input$inSelGroup) == 0)
      return(NULL)
    loc.s.gr = sprintf("paste(%s, sep=';')",
                       paste(input$inSelGroup, sep = '', collapse = ','))
492 493 494
    
    loc.s.rt = input$inSelTime
    
dmattek's avatar
dmattek committed
495 496 497 498
    # Assign tracks selected for highlighting in UI
    loc.tracks.highlight = input$inSelHighlight
    locBut = input$chBhighlightTraj
    
dmattek's avatar
Added:  
dmattek committed
499 500
    
    # Find column names with position
dmattek's avatar
Mod:  
dmattek committed
501 502
    loc.s.pos.x = names(loc.dt)[names(loc.dt) %like% c('.*ocation.*X') | names(loc.dt) %like% c('.*os.x')]
    loc.s.pos.y = names(loc.dt)[names(loc.dt) %like% c('.*ocation.*Y') | names(loc.dt) %like% c('.*os.y')]
dmattek's avatar
Added:  
dmattek committed
503 504 505 506 507 508
    
    if (length(loc.s.pos.x) == 1 & length(loc.s.pos.y) == 1)
      locPos = TRUE
    else
      locPos = FALSE
    
509 510 511
    # if dataset contains column mid.in with trajectory filtering status,
    # then, include it in plotting
    if (sum(names(loc.dt) %in% 'mid.in') > 0) {
dmattek's avatar
Added:  
dmattek committed
512 513 514 515 516 517
      if (locPos) # position columns present
        loc.out = loc.dt[, .(
          y = eval(parse(text = loc.s.y)),
          id = trackObjectsLabelUni,
          group = eval(parse(text = loc.s.gr)),
          realtime = eval(parse(text = loc.s.rt)),
dmattek's avatar
Mod:  
dmattek committed
518 519
          pos.x = get(loc.s.pos.x),
          pos.y = get(loc.s.pos.y),
dmattek's avatar
Added:  
dmattek committed
520
          mid.in = mid.in
dmattek's avatar
Mod:  
dmattek committed
521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538
        )] else
          loc.out = loc.dt[, .(
            y = eval(parse(text = loc.s.y)),
            id = trackObjectsLabelUni,
            group = eval(parse(text = loc.s.gr)),
            realtime = eval(parse(text = loc.s.rt)),
            mid.in = mid.in
          )]
        
        
        
        
        # add 3rd level with status of track selection
        # to a column with trajectory filtering status
        if (locBut) {
          loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', mid.in)]
        }
        
539
    } else {
dmattek's avatar
Added:  
dmattek committed
540 541 542 543 544
      if (locPos) # position columns present
        loc.out = loc.dt[, .(
          y = eval(parse(text = loc.s.y)),
          id = trackObjectsLabelUni,
          group = eval(parse(text = loc.s.gr)),
dmattek's avatar
Mod:  
dmattek committed
545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
          realtime = eval(parse(text = loc.s.rt)),
          pos.x = get(loc.s.pos.x),
          pos.y = get(loc.s.pos.y)
        )] else
          loc.out = loc.dt[, .(
            y = eval(parse(text = loc.s.y)),
            id = trackObjectsLabelUni,
            group = eval(parse(text = loc.s.gr)),
            realtime = eval(parse(text = loc.s.rt))
          )]
        
        
        # add a column with status of track selection
        if (locBut) {
          loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', 'NOT SEL')]
        }
561
    }
562
    
dmattek's avatar
Added:  
dmattek committed
563 564
    # add XY location if present in the dataset
    
dmattek's avatar
dmattek committed
565 566
    # remove NAs
    loc.out = loc.out[complete.cases(loc.out)]
dmattek's avatar
Mod:  
dmattek committed
567
    
dmattek's avatar
dmattek committed
568 569 570 571
    # Trim x-axis (time)
    if(input$chBtimeTrim) {
      loc.out = loc.out[realtime >= input$slTimeTrim[[1]] & realtime <= input$slTimeTrim[[2]] ]
    }
dmattek's avatar
dmattek committed
572 573
    
    # Normalization
dmattek's avatar
dmattek committed
574
    # F-n myNorm adds additional column with .norm suffix
dmattek's avatar
dmattek committed
575 576 577 578 579 580 581 582 583 584 585 586
    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
587 588
      # Column with normalized data is renamed to the original name
      # Further code assumes column name y produced by data4trajPlot
dmattek's avatar
dmattek committed
589 590 591 592
      loc.out[, y := NULL]
      setnames(loc.out, 'y.norm', 'y')
    }
    
dmattek's avatar
dmattek committed
593 594 595 596 597 598
    ##### 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
599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615
    # 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
616
    
dmattek's avatar
dmattek committed
617
    return(loc.out)
dmattek's avatar
dmattek committed
618 619
  })
  
dmattek's avatar
dmattek committed
620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635
  
  
  # prepare data for clustering
  # return a matrix with:
  # cells as columns
  # time points as rows
  data4clust <- reactive({
    cat(file = stderr(), 'data4clust\n')
    
    loc.dt = data4trajPlot()
    if (is.null(loc.dt))
      return(NULL)
    
    loc.out = dcast(loc.dt, id ~ realtime, value.var = 'y')
    loc.rownames = loc.out$id
    
dmattek's avatar
Mod:  
dmattek committed
636
    
dmattek's avatar
dmattek committed
637 638 639
    loc.out = as.matrix(loc.out[, -1])
    rownames(loc.out) = loc.rownames
    return(loc.out)
dmattek's avatar
Mod:  
dmattek committed
640
  }) 
dmattek's avatar
dmattek committed
641
  
dmattek's avatar
dmattek committed
642 643
  
  # get cell IDs with cluster assignments depending on dendrogram cut
dmattek's avatar
dmattek committed
644 645
  getDataCl = function(in.dend, in.k, in.ids) {
    cat(file = stderr(), 'getDataCl \n')
dmattek's avatar
Mod:  
dmattek committed
646
    
dmattek's avatar
dmattek committed
647
    loc.dt.cl = data.table(id = in.ids,
dmattek's avatar
dmattek committed
648 649 650
                           cl = cutree(as.dendrogram(in.dend), k = in.k))
  }
  
dmattek's avatar
dmattek committed
651 652
  ####
  ## UI for trajectory plot
dmattek's avatar
dmattek committed
653
  
dmattek's avatar
dmattek committed
654 655
  output$varSelHighlight = renderUI({
    cat(file = stderr(), 'UI varSelHighlight\n')
dmattek's avatar
dmattek committed
656
    
dmattek's avatar
dmattek committed
657 658 659
    locBut = input$chBhighlightTraj
    if (!locBut)
      return(NULL)
dmattek's avatar
dmattek committed
660
    
dmattek's avatar
dmattek committed
661
    loc.v = getDataTrackObjLabUni()
dmattek's avatar
dmattek committed
662
    if (!is.null(loc.v)) {
663
      selectInput(
dmattek's avatar
dmattek committed
664 665 666
        'inSelHighlight',
        'Select one or more rajectories:',
        loc.v,
667
        width = '100%',
dmattek's avatar
dmattek committed
668
        multiple = TRUE
669
      )
dmattek's avatar
dmattek committed
670 671 672
    }
  })
  
dmattek's avatar
Mod:  
dmattek committed
673
  callModule(modTrajPlot, 'modTrajPlot', data4trajPlot)
dmattek's avatar
dmattek committed
674
  
dmattek's avatar
Added:  
dmattek committed
675 676
  ###### Box-plot
  callModule(tabBoxPlot, 'tabBoxPlot', data4trajPlot)
dmattek's avatar
dmattek committed
677
  
dmattek's avatar
dmattek committed
678 679
  
  
dmattek's avatar
dmattek committed
680 681 682
  ###### Scatter plot
  callModule(tabScatterPlot, 'tabScatter', data4trajPlot)
  
dmattek's avatar
dmattek committed
683 684 685 686 687 688 689 690 691 692 693
  ##### Hierarchical clustering
  
  output$uiPlotHierClSel = renderUI({
    if(input$chBPlotHierClSel) {
      selectInput('inPlotHierClSel', 'Select clusters to display', 
                  choices = seq(1, input$inPlotHierNclust, 1),
                  multiple = TRUE, 
                  selected = 1)
    }
  })
  
dmattek's avatar
Mod:  
dmattek committed
694 695
  # perform hierarchical clustering and return dendrogram coloured according to cutree
  # branch coloring performed using dendextend package
dmattek's avatar
dmattek committed
696
  userFitDendHier <- reactive({
dmattek's avatar
Mod:  
dmattek committed
697 698
    cat(file = stderr(), 'userFitDendHier \n')

dmattek's avatar
dmattek committed
699 700
    dm.t = data4clust()
    if (is.null(dm.t)) {
dmattek's avatar
Mod:  
dmattek committed
701
      return(NULL)
dmattek's avatar
dmattek committed
702 703 704 705 706 707 708
    }
    
    cl.dist = dist(dm.t, method = s.cl.diss[as.numeric(input$selectPlotHierDiss)])
    cl.hc = hclust(cl.dist, method = s.cl.linkage[as.numeric(input$selectPlotHierLinkage)])
    cl.lev = rev(row.names(dm.t))
    
    dend <- as.dendrogram(cl.hc)
dmattek's avatar
Mod:  
dmattek committed
709 710 711
    dend <- color_branches(dend, 
                           col = rainbow_hcl, # make sure that n here equals max in the input$inPlotHierNclust slider
                           k = input$inPlotHierNclust)
dmattek's avatar
dmattek committed
712 713 714 715
    
    return(dend)
  })
  
dmattek's avatar
Mod:  
dmattek committed
716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742
  # prepares a table with cluster numbers in 1st column and colour assignments in 2nd column
  # the number of rows is determined by dendrogram cut
  getClCol <- function(in.dend, in.k) {
    
    loc.col_labels <- get_leaves_branches_col(in.dend)
    loc.col_labels <- loc.col_labels[order(order.dendrogram(in.dend))]
    
    return(unique(
      data.table(cl.no = dendextend::cutree(in.dend, k = in.k, order_clusters_as_data = TRUE),
                 cl.col = loc.col_labels)))
  }
  
  # returns table prepared with f-n getClCol
  # for hierarchical clustering
  getClColHier <- reactive({
    cat(file = stderr(), 'getClColHier \n')
    
    loc.dend = userFitDendHier()
    if (is.null(loc.dend))
      return(NULL)
    
    return(getClCol(loc.dend, input$inPlotHierNclust))
  })
  

  
    # Function instead of reactive as per:
dmattek's avatar
dmattek committed
743 744 745 746 747 748 749 750 751 752 753 754
  # http://stackoverflow.com/questions/26764481/downloading-png-from-shiny-r
  # This function is used to plot and to downoad a pdf
  plotHier <- function() {
    
    loc.dm = data4clust()
    if (is.null(loc.dm))
      return(NULL)
    
    loc.dend <- userFitDendHier()
    if (is.null(loc.dend))
      return(NULL)
    
dmattek's avatar
Mod:  
dmattek committed
755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771
    loc.p = myPlotHeatmap(loc.dm,
                  loc.dend, 
                  palette.arg = input$selectPlotHierPalette, 
                  palette.rev.arg = input$inPlotHierRevPalette, 
                  dend.show.arg = input$selectPlotHierDend, 
                  key.show.arg = input$selectPlotHierKey, 
                  margin.x.arg = input$inPlotHierMarginX, 
                  margin.y.arg = input$inPlotHierMarginY, 
                  nacol.arg = input$inPlotHierNAcolor, 
                  font.row.arg = input$inPlotHierFontX, 
                  font.col.arg = input$inPlotHierFontY, 
                  title.arg = paste(
                    "Distance measure: ",
                    s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                    "\nLinkage method: ",
                    s.cl.linkage[as.numeric(input$selectPlotHierLinkage)]
                  ))
dmattek's avatar
dmattek committed
772 773 774 775 776
    
    return(loc.p)
  }
  
  
dmattek's avatar
Mod:  
dmattek committed
777 778 779 780 781
  # prepare data for plotting trajectories per cluster
  # outputs dt as data4trajPlot but with an additional column 'cl' that holds cluster numbers
  # additionally some clusters are omitted according to manual selection
  data4trajPlotCl <- reactive({
    cat(file = stderr(), 'data4trajPlotCl: in\n')
dmattek's avatar
dmattek committed
782
    
dmattek's avatar
Mod:  
dmattek committed
783
    loc.dt = data4trajPlot()
dmattek's avatar
dmattek committed
784 785
    
    if (is.null(loc.dt)) {
dmattek's avatar
Mod:  
dmattek committed
786
      cat(file = stderr(), 'data4trajPlotCl: dt is NULL\n')
dmattek's avatar
dmattek committed
787 788 789
      return(NULL)
    }
    
dmattek's avatar
Mod:  
dmattek committed
790
    cat(file = stderr(), 'data4trajPlotCl: dt not NULL\n')
dmattek's avatar
dmattek committed
791 792
    
    # get cellIDs with cluster assignments based on dendrogram cut
dmattek's avatar
dmattek committed
793
    loc.dt.cl = getDataCl(userFitDendHier(), input$inPlotHierNclust, getDataTrackObjLabUni_afterTrim())
dmattek's avatar
dmattek committed
794 795 796
    loc.dt = merge(loc.dt, loc.dt.cl, by = 'id')
    
    # display only selected clusters
dmattek's avatar
Mod:  
dmattek committed
797 798
    if(input$chBPlotHierClSel)
      loc.dt = loc.dt[cl %in% input$inPlotHierClSel]
dmattek's avatar
dmattek committed
799
    
dmattek's avatar
Mod:  
dmattek committed
800 801
    return(loc.dt)    
  })
dmattek's avatar
dmattek committed
802
  
dmattek's avatar
Mod:  
dmattek committed
803 804 805 806 807
  callModule(modTrajPlot, 'modPlotHierTraj', 
             in.data = data4trajPlotCl, 
             in.facet = 'cl',  
             in.facet.color = getClColHier,
             in.fname = paste0('clust_hierch_tCourses_',
dmattek's avatar
Mod:  
dmattek committed
808 809 810
                                                                            s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                                                                            '_',
                                                                            s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.pdf'))
dmattek's avatar
dmattek committed
811
  
dmattek's avatar
Added:  
dmattek committed
812
  # download a list of cellIDs with cluster assignments
dmattek's avatar
dmattek committed
813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839
  output$downCellCl <- downloadHandler(
    filename = function() {
      paste0('clust_hierch_data_',
             s.cl.diss[as.numeric(input$selectPlotHierDiss)],
             '_',
             s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.csv')
    },
    
    content = function(file) {
      write.csv(x = getDataCl(userFitDendHier(), input$inPlotHierNclust, getDataTrackObjLabUni_afterTrim()), file = file, row.names = FALSE)
    }
  )
  
  output$downCellClSpar <- downloadHandler(
    filename = function() {
      paste0('clust_hierchSpar_data_',
             s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
             '_',
             s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.csv')
    },
    
    content = function(file) {
      write.csv(x = getDataCl(userFitDendHierSpar(), input$inPlotHierSparNclust, getDataTrackObjLabUni_afterTrim()), file = file, row.names = FALSE)
    }
  )
  
  
dmattek's avatar
Mod:  
dmattek committed
840 841 842 843 844 845
  # callModule(downCellCl, 'downDataHier', paste0('clust_hierch_data_',
  #                                               s.cl.diss[as.numeric(input$selectPlotHierDiss)],
  #                                               '_',
  #                                               s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.csv'),
  #            getDataCl(userFitDendHier, input$inPlotHierNclust, getDataTrackObjLabUni_afterTrim))
  # 
dmattek's avatar
Added:  
dmattek committed
846
  
dmattek's avatar
Mod:  
dmattek committed
847 848 849 850 851 852 853 854 855 856 857
  output$downloadDataClean <- downloadHandler(
    filename = 'tCoursesSelected_clean.csv',
    content = function(file) {
      write.csv(data4trajPlot(), file, row.names = FALSE)
    }
  )
  
  
  # prepare data for barplot with distribution of items per condition  
  data4clDistPlot <- reactive({
    cat(file = stderr(), 'data4clDistPlot: in\n')
dmattek's avatar
dmattek committed
858 859
    
    # get cell IDs with cluster assignments depending on dendrogram cut
dmattek's avatar
Mod:  
dmattek committed
860
    loc.dend <- userFitDendHier()
dmattek's avatar
dmattek committed
861 862 863 864 865 866 867 868 869
    if (is.null(loc.dend)) {
      cat(file = stderr(), 'plotClDist: loc.dend is NULL\n')
      return(NULL)
    }
    
    loc.dt.cl = data.table(id = getDataTrackObjLabUni_afterTrim(),
                           cl = cutree(as.dendrogram(loc.dend), k = input$inPlotHierNclust))
    
    
dmattek's avatar
dmattek committed
870
    # get cellIDs with condition name
dmattek's avatar
Mod:  
dmattek committed
871
    loc.dt.gr = getDataCond()
dmattek's avatar
dmattek committed
872 873 874 875 876 877 878 879
    if (is.null(loc.dt.gr)) {
      cat(file = stderr(), 'plotClDist: loc.dt.gr is NULL\n')
      return(NULL)
    }
    
    loc.dt = merge(loc.dt.cl, loc.dt.gr, by = 'id')
    
    # display only selected clusters
dmattek's avatar
Mod:  
dmattek committed
880 881
    if(input$chBPlotHierClSel)
      loc.dt = loc.dt[cl %in% input$inPlotHierClSel]
dmattek's avatar
dmattek committed
882 883 884
    
    loc.dt.aggr = loc.dt[, .(nCells = .N), by = .(group, cl)]
    
dmattek's avatar
Mod:  
dmattek committed
885
    return(loc.dt.aggr)
dmattek's avatar
dmattek committed
886
    
dmattek's avatar
Mod:  
dmattek committed
887
  })
dmattek's avatar
dmattek committed
888
  
dmattek's avatar
Mod:  
dmattek committed
889

dmattek's avatar
dmattek committed
890 891 892 893 894 895 896 897 898 899 900 901 902
  #  Hierarchical - display heatmap
  getPlotHierHeatMapHeight <- function() {
    return (input$inPlotHierHeatMapHeight)
  }
  
  output$outPlotHier <- renderPlot({
    locBut = input$butPlotHierHeatMap
    
    if (locBut == 0) {
      cat(file = stderr(), 'outPlotHier: Go button not pressed\n')
      
      return(NULL)
    }
dmattek's avatar
Mod:  
dmattek committed
903

dmattek's avatar
dmattek committed
904 905 906 907
    plotHier()
  }, height = getPlotHierHeatMapHeight)
  
  
dmattek's avatar
dmattek committed
908 909 910 911
  #  Hierarchical - Heat Map - download pdf
  callModule(downPlot, "downPlotHier",       paste0('clust_hierch_heatMap_',
                                                    s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                                                    '_',
dmattek's avatar
Mod:  
dmattek committed
912
                                                    s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.png'), plotHier)
dmattek's avatar
dmattek committed
913

dmattek's avatar
Mod:  
dmattek committed
914 915 916 917
  callModule(modClDistPlot, 'hierClDistPlot', 
             in.data = data4clDistPlot,
             in.cols = getClColHier,
             in.fname = paste0('clust_hierch_clDist_',
dmattek's avatar
Mod:  
dmattek committed
918 919 920 921
                    s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                    '_',
                    s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.pdf'))
  
dmattek's avatar
dmattek committed
922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961
  
  ##### Sparse hierarchical clustering using sparcl
  
  # UI for advanced options
  output$uiPlotHierSparNperms = renderUI({
    if (input$inHierSparAdv)
      sliderInput(
        'inPlotHierSparNperms',
        'Number of permutations',
        min = 1,
        max = 20,
        value = 1,
        step = 1,
        ticks = TRUE
      )
  })
  
  # UI for advanced options
  output$uiPlotHierSparNiter = renderUI({
    if (input$inHierSparAdv)
      sliderInput(
        'inPlotHierSparNiter',
        'Number of iterations',
        min = 1,
        max = 50,
        value = 1,
        step = 1,
        ticks = TRUE
      )
  })
  
  output$uiPlotHierSparClSel = renderUI({
    if(input$chBPlotHierSparClSel) {
      selectInput('inPlotHierSparClSel', 'Select clusters to display', 
                  choices = seq(1, input$inPlotHierSparNclust, 1),
                  multiple = TRUE, 
                  selected = 1)
    }
  })
  
dmattek's avatar
Mod:  
dmattek committed
962
  
dmattek's avatar
dmattek committed
963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999
  getPlotHierSparHeatMapHeight <- function() {
    return (input$inPlotHierSparHeatMapHeight)
  }
  
  userFitHierSpar <- reactive({
    dm.t = data4clust()
    if (is.null(dm.t)) {
      return()
    }
    
    #cat('rownames: ', rownames(dm.t), '\n')
    
    perm.out <- HierarchicalSparseCluster.permute(
      dm.t,
      wbounds = NULL,
      nperms = ifelse(input$inHierSparAdv, input$inPlotHierSparNperms, 1),
      dissimilarity = s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)]
    )
    
    sparsehc <- HierarchicalSparseCluster(
      dists = perm.out$dists,
      wbound = perm.out$bestw,
      niter = ifelse(input$inHierSparAdv, input$inPlotHierSparNiter, 1),
      method = s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)],
      dissimilarity = s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)]
    )
    return(sparsehc)
  })
  
  
  userFitDendHierSpar <- reactive({
    sparsehc = userFitHierSpar()
    if (is.null(sparsehc)) {
      return()
    }
    
    dend <- as.dendrogram(sparsehc$hc)
dmattek's avatar
Mod:  
dmattek committed
1000 1001 1002
    dend <- color_branches(dend, 
                           col = rainbow_hcl,
                           k = input$inPlotHierSparNclust)
dmattek's avatar
dmattek committed
1003 1004 1005
    
    return(dend)
  })
dmattek's avatar
Mod:  
dmattek committed
1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018

  # returns table prepared with f-n getClCol
  # for sparse hierarchical clustering
  getClColHierSpar <- reactive({
    cat(file = stderr(), 'getClColHierSpar \n')
    
    loc.dend = userFitDendHierSpar()
    if (is.null(loc.dend))
      return(NULL)
    
    return(getClCol(loc.dend, input$inPlotHierNclust))
  })
  
dmattek's avatar
dmattek committed
1019 1020 1021 1022 1023 1024
  
  # Function instead of reactive as per:
  # http://stackoverflow.com/questions/26764481/downloading-png-from-shiny-r
  # This function is used to plot and to downoad a pdf
  plotHierSpar <- function() {
    
dmattek's avatar
Mod:  
dmattek committed
1025 1026
    loc.dm = data4clust()
    if (is.null(loc.dm)) {
dmattek's avatar
dmattek committed
1027 1028 1029 1030
      return()
    }
    
    sparsehc <- userFitHierSpar()
dmattek's avatar
Mod:  
dmattek committed
1031 1032
    loc.dend <- userFitDendHierSpar()

dmattek's avatar
dmattek committed
1033 1034 1035 1036 1037
    loc.colnames = paste0(ifelse(sparsehc$ws == 0, "",
                                 ifelse(
                                   sparsehc$ws <= 0.1,
                                   "* ",
                                   ifelse(sparsehc$ws <= 0.5, "** ", "*** ")
dmattek's avatar
Mod:  
dmattek committed
1038
                                 )),  colnames(loc.dm))
dmattek's avatar
dmattek committed
1039 1040 1041 1042 1043 1044 1045 1046 1047
    
    loc.colcol   = ifelse(sparsehc$ws == 0,
                          "black",
                          ifelse(
                            sparsehc$ws <= 0.1,
                            "blue",
                            ifelse(sparsehc$ws <= 0.5, "green", "red")
                          ))
    
dmattek's avatar
Mod:  
dmattek committed
1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066
    loc.p = myPlotHeatmap(loc.dm,
                  loc.dend, 
                  palette.arg = input$selectPlotHierSparPalette, 
                  palette.rev.arg = input$inPlotHierSparRevPalette, 
                  dend.show.arg = input$selectPlotHierSparDend, 
                  key.show.arg = input$selectPlotHierSparKey, 
                  margin.x.arg = input$inPlotHierSparMarginX, 
                  margin.y.arg = input$inPlotHierSparMarginY, 
                  nacol.arg = input$inPlotHierSparNAcolor, 
                  colCol.arg = loc.colcol,
                  labCol.arg = loc.colnames,
                  font.row.arg = input$inPlotHierSparFontX, 
                  font.col.arg = input$inPlotHierSparFontY, 
                  title.arg = paste(
                    "Distance measure: ",
                    s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
                    "\nLinkage method: ",
                    s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)]
                  ))
dmattek's avatar
dmattek committed
1067 1068 1069 1070
    
    return(loc.p)
  }
  
dmattek's avatar
Mod:  
dmattek committed
1071 1072 1073 1074 1075 1076 1077
  # prepare data for plotting trajectories per cluster
  # outputs dt as data4trajPlot but with an additional column 'cl' that holds cluster numbers
  # additionally some clusters are omitted according to manual selection
  data4trajPlotClSpar <- reactive({
    cat(file = stderr(), 'data4trajPlotClSpar: in\n')
    
    loc.dt = data4trajPlot()
dmattek's avatar
dmattek committed
1078 1079
    
    if (is.null(loc.dt)) {
dmattek's avatar
Mod:  
dmattek committed
1080
      cat(file = stderr(), 'data4trajPlotClSpar: dt is NULL\n')
dmattek's avatar
dmattek committed
1081 1082 1083
      return(NULL)
    }
    
dmattek's avatar
Mod:  
dmattek committed
1084
    cat(file = stderr(), 'data4trajPlotClSpar: dt not NULL\n')
dmattek's avatar
dmattek committed
1085 1086
    
    # get cellIDs with cluster assignments based on dendrogram cut
dmattek's avatar
Mod:  
dmattek committed
1087
    loc.dt.cl = getDataCl(userFitDendHierSpar(), input$inPlotHierSparNclust, getDataTrackObjLabUni_afterTrim())
dmattek's avatar
dmattek committed
1088 1089
    loc.dt = merge(loc.dt, loc.dt.cl, by = 'id')
    
dmattek's avatar
Mod:  
dmattek committed
1090 1091 1092
    # display only selected clusters
    if(input$chBPlotHierSparClSel)
      loc.dt = loc.dt[cl %in% input$inPlotHierSparClSel]
dmattek's avatar
dmattek committed
1093
    
dmattek's avatar
Mod:  
dmattek committed
1094 1095
    return(loc.dt)    
  })
dmattek's avatar
dmattek committed
1096
  
dmattek's avatar
Mod:  
dmattek committed
1097 1098 1099 1100 1101
  callModule(modTrajPlot, 'modPlotHierSparTraj', 
             in.data = data4trajPlotClSpar, 
             in.facet = 'cl', 
             in.facet.color = getClColHierSpar,
             paste0('clust_hierchSparse_tCourses_',
dmattek's avatar
Mod:  
dmattek committed
1102 1103 1104 1105
                                                                                   s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
                                                                                   '_',
                                                                                   s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.pdf'))

dmattek's avatar
dmattek committed
1106
  
dmattek's avatar
Mod:  
dmattek committed
1107 1108 1109 1110
  
  # prepare data for barplot with distribution of items per condition  
  data4clSparDistPlot <- reactive({
    cat(file = stderr(), 'data4clSparDistPlot: in\n')
dmattek's avatar
dmattek committed
1111 1112
    
    # get cell IDs with cluster assignments depending on dendrogram cut
dmattek's avatar
Mod:  
dmattek committed
1113 1114 1115
    loc.dend <- userFitHierSpar()
    if (is.null(loc.dend)) {
      cat(file = stderr(), 'plotClSparDist: loc.dend is NULL\n')
dmattek's avatar
dmattek committed
1116 1117 1118 1119
      return(NULL)
    }
    
    loc.dt.cl = data.table(id = getDataTrackObjLabUni_afterTrim(),
dmattek's avatar
Mod:  
dmattek committed
1120
                           cl = cutree(as.dendrogram(loc.dend$hc), k = input$inPlotHierSparNclust))
dmattek's avatar
dmattek committed
1121 1122
    
    
dmattek's avatar
Mod:  
dmattek committed
1123 1124
    # get cellIDs with condition name
    loc.dt.gr = getDataCond()
dmattek's avatar
dmattek committed
1125
    if (is.null(loc.dt.gr)) {
dmattek's avatar
Mod:  
dmattek committed
1126
      cat(file = stderr(), 'plotClSparDist: loc.dt.gr is NULL\n')
dmattek's avatar
dmattek committed
1127 1128 1129 1130 1131
      return(NULL)
    }
    
    loc.dt = merge(loc.dt.cl, loc.dt.gr, by = 'id')
    
dmattek's avatar
Mod:  
dmattek committed
1132 1133 1134
    # display only selected clusters
    if(input$chBPlotHierSparClSel)
      loc.dt = loc.dt[cl %in% input$inPlotHierSparClSel]
dmattek's avatar
dmattek committed
1135 1136 1137
    
    loc.dt.aggr = loc.dt[, .(nCells = .N), by = .(group, cl)]
    
dmattek's avatar
Mod:  
dmattek committed
1138
    return(loc.dt.aggr)
dmattek's avatar
dmattek committed
1139
    
dmattek's avatar
Mod:  
dmattek committed
1140 1141
  })
  
dmattek's avatar
Mod:  
dmattek committed
1142 1143 1144 1145
  callModule(modClDistPlot, 'hierClSparDistPlot', 
             in.data = data4clSparDistPlot,
             in.cols = getClColHierSpar,
             in.fname = paste0('clust_hierchSparse_clDist_',
dmattek's avatar
Mod:  
dmattek committed
1146 1147 1148 1149
                    s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
                    '_',
                    s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.pdf'))
  
dmattek's avatar
dmattek committed
1150

dmattek's avatar
Mod:  
dmattek committed
1151
  
dmattek's avatar
dmattek committed
1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165
  # Sparse Hierarchical - display heatmap
  output$outPlotHierSpar <- renderPlot({
    locBut = input$butPlotHierSparHeatMap
    
    if (locBut == 0) {
      cat(file = stderr(), 'outPlotHierSpar: Go button not pressed\n')
      
      return(NULL)
    }
    
    plotHierSpar()
  }, height = getPlotHierSparHeatMapHeight)
  
  # Sparse Hierarchical - Heat Map - download pdf
dmattek's avatar
dmattek committed
1166 1167 1168
  callModule(downPlot, "downPlotHierSparHM",       paste0('clust_hierchSparse_heatMap_',
                                                          s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
                                                          '_',
dmattek's avatar
Mod:  
dmattek committed
1169
                                                          s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.pdf'), plotHierSpar)
dmattek's avatar
dmattek committed
1170
  
dmattek's avatar
dmattek committed
1171

dmattek's avatar
dmattek committed
1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229
  # Sparse Hierarchical clustering (sparcl) interactive version
  output$plotHierSparInt <- renderD3heatmap({
    dm.t = data4clust()
    if (is.null(dm.t)) {
      return()
    }
    
    sparsehc <- userFitHierSpar()
    
    dend <- as.dendrogram(sparsehc$hc)
    dend <- color_branches(dend, k = input$inPlotHierSparNclust)
    
    if (input$inPlotHierSparRevPalette)
      my_palette <-
      rev(colorRampPalette(brewer.pal(9, input$selectPlotHierSparPalette))(n = 99))
    else
      my_palette <-
      colorRampPalette(brewer.pal(9, input$selectPlotHierSparPalette))(n = 99)
    
    
    col_labels <- get_leaves_branches_col(dend)
    col_labels <- col_labels[order(order.dendrogram(dend))]
    
    if (input$selectPlotHierSparDend == 1)
      assign("var.tmp", dend)
    else
      assign("var.tmp", FALSE)
    
    
    loc.colnames = paste0(colnames(dm.t), ifelse(sparsehc$ws == 0, "",
                                                 ifelse(
                                                   sparsehc$ws <= 0.1,
                                                   " *",
                                                   ifelse(sparsehc$ws <= 0.5, " **", " ***")
                                                 )))
    
    d3heatmap(
      dm.t,
      Rowv = var.tmp,
      dendrogram = ifelse(input$selectPlotHierSparDend == 1, "row", 'none'),
      trace = "none",
      revC = FALSE,
      na.rm = FALSE,
      margins = c(
        input$inPlotHierSparMarginX * 10,
        input$inPlotHierSparMarginY * 10
      ),
      colors = my_palette,
      na.col = grey(input$inPlotHierSparNAcolor),
      cexRow = input$inPlotHierSparFontY,
      cexCol = input$inPlotHierSparFontX,
      xaxis_height = input$inPlotHierSparMarginX * 10,
      yaxis_width = input$inPlotHierSparMarginY * 10,
      show_grid = TRUE,
      #labRow = rownames(dm.t),
      labCol = loc.colnames
    )
  })
dmattek's avatar
Mod:  
dmattek committed
1230 1231
  
  #callModule(clustBay, 'TabClustBay', data4clust)
1232
  
dmattek's avatar
dmattek committed
1233
})