auxfunc.R 20.8 KB
Newer Older
dmattek's avatar
dmattek committed
1
## Custom plotting
dmattek's avatar
dmattek committed
2
require(ggplot2)
dmattek's avatar
Mod:  
dmattek committed
3 4 5
require(RColorBrewer)
require(gplots) # for heatmap.2
require(grid) # for modifying grob
dmattek's avatar
dmattek committed
6

dmattek's avatar
dmattek committed
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
rhg_cols <- c(
  "#771C19",
  "#AA3929",
  "#E25033",
  "#F27314",
  "#F8A31B",
  "#E2C59F",
  "#B6C5CC",
  "#8E9CA3",
  "#556670",
  "#000000"
)

md_cols <- c(
  "#FFFFFF",
  "#F8A31B",
  "#F27314",
  "#E25033",
  "#AA3929",
  "#FFFFCC",
  "#C2E699",
  "#78C679",
  "#238443"
)

dmattek's avatar
dmattek committed
32 33 34 35 36 37 38 39 40 41 42 43 44
s.cl.linkage = c("ward.D",
                 "ward.D2",
                 "single",
                 "complete",
                 "average",
                 "mcquitty",
                 "centroid")

s.cl.spar.linkage = c("average",
                      "complete", 
                      "single",
                      "centroid")

dmattek's avatar
Added:  
dmattek committed
45
s.cl.diss = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "DTW")
dmattek's avatar
dmattek committed
46 47
s.cl.spar.diss = c("squared.distance","absolute.value")

48
# list of palettes for the heatmap
dmattek's avatar
dmattek committed
49 50 51 52 53 54 55 56 57 58
l.col.pal = list(
  "White-Orange-Red" = 'OrRd',
  "Yellow-Orange-Red" = 'YlOrRd',
  "Reds" = "Reds",
  "Oranges" = "Oranges",
  "Greens" = "Greens",
  "Blues" = "Blues",
  "Spectral" = 'Spectral'
)

59 60 61 62 63 64 65 66 67 68
# list of palettes for the dendrogram
l.col.pal.dend = list(
  "Rainbow" = 'rainbow_hcl',
  "Sequential" = 'sequential_hcl',
  "Heat" = 'heat_hcl',
  "Terrain" = 'terrain_hcl',
  "Diverge HCL" = 'diverge_hcl',
  "Diverge HSV" = 'diverge_hsv'
)

dmattek's avatar
Added:  
dmattek committed
69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102
# Creates a popup with help text
# From: https://gist.github.com/jcheng5/5913297
helpPopup <- function(title, content,
                      placement=c('right', 'top', 'left', 'bottom'),
                      trigger=c('click', 'hover', 'focus', 'manual')) {
  tagList(
    singleton(
      tags$head(
        tags$script("$(function() { $(\"[data-toggle='popover']\").popover(); })")
      )
    ),
    tags$a(
      href = "#", class = "btn btn-mini", `data-toggle` = "popover",
      title = title, `data-content` = content, `data-animation` = TRUE,
      `data-placement` = match.arg(placement, several.ok=TRUE)[1],
      `data-trigger` = match.arg(trigger, several.ok=TRUE)[1],
      #tags$i(class="icon-question-sign")
      # changed based on http://stackoverflow.com/questions/30436013/info-bubble-text-in-a-shiny-interface
      icon("question")
    )
  )
}

help.text = c(
  'Accepts CSV file with a column of cell IDs for removal. 
                   IDs should correspond to those used for plotting. 
  Say, the main data file contains columns Metadata_Site and TrackLabel. 
  These two columns should be then selected in UI to form a unique cell ID, e.g. 001_0001 where former part corresponds to Metadata_Site and the latter to TrackLabel.',
  'Plotting and data processing requires a unique cell ID across entire dataset. A typical dataset from CellProfiler assigns unique cell ID (TrackLabel) within each field of view (Metadata_Site).
                   Therefore, a unique ID is created by concatenating these two columns. If the dataset already contains a unique ID, check this box and select a single column only.'
)


#####
dmattek's avatar
dmattek committed
103
## Functions for clustering 
dmattek's avatar
Added:  
dmattek committed
104

dmattek's avatar
dmattek committed
105 106 107 108 109 110 111 112 113

# Return a dt with cell IDs and corresponding cluster assignments depending on dendrogram cut (in.k)
# This one works wth dist & hclust pair
# For sparse hierarchical clustering use getDataClSpar
# Arguments:
# in.dend  - dendrogram; usually output from as.dendrogram(hclust(distance_matrix))
# in.k - level at which dendrogram should be cut

getDataCl = function(in.dend, in.k) {
dmattek's avatar
Added:  
dmattek committed
114 115
  cat(file = stderr(), 'getDataCl \n')
  
dmattek's avatar
dmattek committed
116 117 118 119 120 121 122 123
  loc.m = dendextend::cutree(in.dend, in.k, order_clusters_as_data = TRUE)
  #print(loc.m)
  
  # The result of cutree containes named vector with names being cell id's
  # THIS WON'T WORK with sparse hierarchical clustering because there, the dendrogram doesn't have original id's
  loc.dt.cl = data.table(id = names(loc.m),
                         cl = loc.m)
  
124 125
  #cat('===============\ndataCl:\n')
  #print(loc.dt.cl)
dmattek's avatar
dmattek committed
126
  return(loc.dt.cl)
dmattek's avatar
Added:  
dmattek committed
127 128
}

dmattek's avatar
dmattek committed
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147

# Return a dt with cell IDs and corresponding cluster assignments depending on dendrogram cut (in.k)
# This one works with sparse hierarchical clustering!
# Arguments:
# in.dend  - dendrogram; usually output from as.dendrogram(hclust(distance_matrix))
# in.k - level at which dendrogram should be cut
# in.id - vector of cell id's

getDataClSpar = function(in.dend, in.k, in.id) {
  cat(file = stderr(), 'getDataClSpar \n')
  
  loc.m = dendextend::cutree(in.dend, in.k, order_clusters_as_data = TRUE)
  #print(loc.m)
  
  # The result of cutree containes named vector with names being cell id's
  # THIS WON'T WORK with sparse hierarchical clustering because there, the dendrogram doesn't have original id's
  loc.dt.cl = data.table(id = in.id,
                         cl = loc.m)
  
148 149
  #cat('===============\ndataCl:\n')
  #print(loc.dt.cl)
dmattek's avatar
dmattek committed
150 151 152 153 154
  return(loc.dt.cl)
}



dmattek's avatar
Added:  
dmattek committed
155 156 157 158 159 160 161 162 163 164 165 166 167 168 169
# 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)))
}


#####
## Common plotting functions
dmattek's avatar
dmattek committed
170

dmattek's avatar
Mod:  
dmattek committed
171 172 173 174 175 176 177 178 179 180 181 182
myGgplotTraj = function(dt.arg, # data table
                        x.arg,  # string with column name for x-axis
                        y.arg, # string with column name for y-axis
                        group.arg, # string with column name for grouping time series (typicaly cell ID)
                        facet.arg, # string with column name for facetting
                        facet.ncol.arg = 2, # default number of facet columns
                        facet.color.arg = NULL, # vector with list of colours for adding colours to facet names (currently a horizontal line on top of the facet is drawn)
                        line.col.arg = NULL, # string with column name for colouring time series (typically when individual time series are selected in UI)
                        xlab.arg = NULL, # string with x-axis label
                        ylab.arg = NULL, # string with y-axis label
                        plotlab.arg = NULL, # string with plot label
                        dt.stim.arg = NULL, # plotting additional dataset; typically to indicate stimulations (not fully implemented yet, not tested!)
dmattek's avatar
dmattek committed
183
                        tfreq.arg = 1,
dmattek's avatar
dmattek committed
184
                        ylim.arg = NULL,
dmattek's avatar
dmattek committed
185
                        stim.bar.height.arg = 0.1,
dmattek's avatar
Added:  
dmattek committed
186
                        stim.bar.width.arg = 0.5,
dmattek's avatar
Mod:  
dmattek committed
187
                        aux.label1 = NULL, # 1st point label; used for interactive plotting; displayed in the tooltip; typically used to display values of column holding x & y coordinates
dmattek's avatar
Added:  
dmattek committed
188
                        aux.label2 = NULL,
189
                        aux.label3 = NULL,
dmattek's avatar
Added:  
dmattek committed
190 191 192 193 194
                        stat.arg = c('', 'mean', 'CI', 'SE')) {
  
  # match arguments for stat plotting
  loc.stat = match.arg(stat.arg, several.ok = TRUE)

dmattek's avatar
Added:  
dmattek committed
195 196
  
  # aux.label12 are required for plotting XY positions in the tooltip of the interactive (plotly) graph
dmattek's avatar
dmattek committed
197 198
  p.tmp = ggplot(dt.arg,
                 aes_string(x = x.arg,
dmattek's avatar
dmattek committed
199
                            y = y.arg,
dmattek's avatar
Added:  
dmattek committed
200 201
                            group = group.arg,
                            label  = aux.label1,
202 203
                            label2 = aux.label2,
                            label3 = aux.label3))
dmattek's avatar
dmattek committed
204
  
dmattek's avatar
dmattek committed
205 206 207 208 209 210 211 212 213 214 215 216 217
  if (is.null(line.col.arg)) {
    p.tmp = p.tmp +
      geom_line(alpha = 0.25, 
                              size = 0.25)
  }
  else {
    p.tmp = p.tmp + 
      geom_line(aes_string(colour = line.col.arg), 
                              alpha = 0.5, 
                              size = 0.5) +
      scale_color_manual(name = '', 
                         values =c("FALSE" = rhg_cols[7], "TRUE" = rhg_cols[3], "SELECTED" = 'green', "NOT SEL" = rhg_cols[7]))
  }
dmattek's avatar
Mod:  
dmattek committed
218 219 220 221 222 223 224 225 226 227

  # this is temporary solution for adding colour according to cluster number
  # use only when plotting traj from clustering!
  # a horizontal line is added at the top of data
  if (!is.null(facet.color.arg)) {

    loc.y.max = max(dt.arg[, c(y.arg), with = FALSE])
    loc.dt.cl = data.table(xx = 1:length(facet.color.arg), yy = loc.y.max)
    setnames(loc.dt.cl, 'xx', facet.arg)
    
dmattek's avatar
Fixed:  
dmattek committed
228 229
    # adjust facet.color.arg to plot
    
dmattek's avatar
Mod:  
dmattek committed
230 231 232 233 234
    p.tmp = p.tmp +
      geom_hline(data = loc.dt.cl, colour = facet.color.arg, yintercept = loc.y.max, size = 4) +
      scale_colour_manual(values = facet.color.arg,
                          name = '')
  }
dmattek's avatar
dmattek committed
235
  
dmattek's avatar
Added:  
dmattek committed
236 237
  if ('mean' %in% loc.stat)
    p.tmp = p.tmp + 
dmattek's avatar
dmattek committed
238 239 240
    stat_summary(
      aes_string(y = y.arg, group = 1),
      fun.y = mean,
dmattek's avatar
Added:  
dmattek committed
241
      colour = 'red',
dmattek's avatar
dmattek committed
242 243 244 245
      linetype = 'solid',
      size = 1,
      geom = "line",
      group = 1
dmattek's avatar
Added:  
dmattek committed
246 247 248 249 250 251 252 253
    )

  if ('CI' %in% loc.stat)
    p.tmp = p.tmp + 
    stat_summary(
      aes_string(y = y.arg, group = 1),
      fun.data = mean_cl_normal,
      colour = 'red',
dmattek's avatar
Mod:  
dmattek committed
254
      alpha = 0.25,
dmattek's avatar
Added:  
dmattek committed
255 256 257 258 259 260 261 262 263 264
      geom = "ribbon",
      group = 1
    )
  
  if ('SE' %in% loc.stat)
    p.tmp = p.tmp + 
    stat_summary(
      aes_string(y = y.arg, group = 1),
      fun.data = mean_se,
      colour = 'red',
dmattek's avatar
Mod:  
dmattek committed
265
      alpha = 0.25,
dmattek's avatar
Added:  
dmattek committed
266 267 268 269 270 271 272
      geom = "ribbon",
      group = 1
    )
  
  
  
  p.tmp = p.tmp + 
dmattek's avatar
dmattek committed
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
    facet_wrap(as.formula(paste("~", facet.arg)),
               ncol = facet.ncol.arg,
               scales = "free_x")
  
  if(!is.null(dt.stim.arg)) {
    p.tmp = p.tmp + geom_segment(data = dt.stim.arg,
                                 aes(x = Stimulation_time - tfreq.arg,
                                     xend = Stimulation_time - tfreq.arg,
                                     y = ylim.arg[1],
                                     yend = ylim.arg[1] + abs(ylim.arg[2] - ylim.arg[1]) * stim.bar.height.arg),
                                 colour = rhg_cols[[3]],
                                 size = stim.bar.width.arg,
                                 group = 1) 
  }
  
dmattek's avatar
dmattek committed
288 289 290
  if (!is.null(ylim.arg)) 
    p.tmp = p.tmp + coord_cartesian(ylim = ylim.arg)
  
dmattek's avatar
dmattek committed
291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312
  p.tmp = p.tmp + 
    xlab(paste0(xlab.arg, "\n")) +
    ylab(paste0("\n", ylab.arg)) +
    ggtitle(plotlab.arg) +
    theme_bw(base_size = 18, base_family = "Helvetica") +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major = element_blank(),
      panel.border = element_blank(),
      axis.line.x = element_line(color = "black", size = 0.25),
      axis.line.y = element_line(color = "black", size = 0.25),
      axis.text.x = element_text(size = 12),
      axis.text.y = element_text(size = 12),
      strip.text.x = element_text(size = 14, face = "bold"),
      strip.text.y = element_text(size = 14, face = "bold"),
      strip.background = element_blank(),
      legend.key = element_blank(),
      legend.key.height = unit(1, "lines"),
      legend.key.width = unit(2, "lines"),
      legend.position = "top"
    )
  
dmattek's avatar
Mod:  
dmattek committed
313 314 315 316
  

  
  return(p.tmp)
dmattek's avatar
dmattek committed
317 318 319 320 321 322
}


userDataGen <- function() {  
  cat(file=stderr(), 'userDataGen: in\n')
  
323
  locNtp = 60
dmattek's avatar
dmattek committed
324
  locNtracks = 10
325
  locNsites = 6
326 327
  locNwells = 1
  
328 329
  x.rand.1 = c(rnorm(locNtp * locNtracks * locNsites * 1/3, 0.5, 0.1), rnorm(locNtp * locNtracks * locNsites * 1/3,   1, 0.2), rnorm(locNtp * locNtracks * locNsites * 1/3,  2, 0.5))
  x.rand.2 = c(rnorm(locNtp * locNtracks * locNsites * 1/3, 0.25, 0.1), rnorm(locNtp * locNtracks * locNsites * 1/3, 0.5, 0.2),  rnorm(locNtp * locNtracks * locNsites * 1/3, 1, 0.2))
dmattek's avatar
dmattek committed
330 331 332 333 334

  # add NA's for testing
  x.rand.1[c(10,20,30)] = NA
  
  #  x.rand.3 = rep(rnorm(locNtracks, 2, 0.5), 1, each = locNtp)
dmattek's avatar
Mod:  
dmattek committed
335
#  x.rand.4 = rep(rnorm(locNtracks, 1, 0.1), 1, each = locNtp)
336
  
dmattek's avatar
Mod:  
dmattek committed
337 338
#  x.arg = rep(seq(0, locNtp-1) / locNtp * 4 * pi, locNtracks * locNsites)
  x.arg = rep(seq(1, locNtp), locNtracks * locNsites)
dmattek's avatar
dmattek committed
339 340 341
  
  dt.nuc = data.table(Metadata_Site = rep(1:locNsites, each = locNtp * locNtracks),
                      Metadata_Well = rep(1:locNwells, each = locNtp * locNsites * locNtracks / locNwells),
342
                      Metadata_RealTime = x.arg,
dmattek's avatar
Mod:  
dmattek committed
343 344 345 346 347
                      objCyto_Intensity_MeanIntensity_imErkCor = x.rand.1,
                      objNuc_Intensity_MeanIntensity_imErkCor  = x.rand.2,
                      objNuc_Location_X = runif(locNtp * locNtracks * locNsites, min = 0, max = 1),
                      objNuc_Location_Y = runif(locNtp * locNtracks * locNsites, min = 0, max = 1),
#                      objCyto_Intensity_MeanIntensity_imErkCor = x.rand.3 + ifelse(x.arg < 4, 0, 1) / x.rand.3,
348
#                      objNuc_Intensity_MeanIntensity_imErkCor  = c(rnorm(locNtp * locNtracks * locNsites * 0.5, .25, 0.1), rnorm(locNtp * locNtracks * locNsites * 0.5, .5, 0.2)),
dmattek's avatar
dmattek committed
349 350 351 352
                      TrackLabel = rep(1:(locNtracks*locNsites), each = locNtp))
  
  return(dt.nuc)
}
dmattek's avatar
dmattek committed
353 354


355 356 357 358 359 360 361
# Fast DTW computation
fastDTW <-function (x)
{
  return(dtw(x, window.type = 'sakoechiba', distance.only = T)$normalizedDistance)
}


dmattek's avatar
dmattek committed
362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385
# Returns original dt with an additional column with normalized quantity.
# The column to be normalised is given by 'in.meas.col'.
# The name of additional column is the same as in.meas.col but with ".norm" suffix added.
# Normalisation is based on part of the trajectory;
# this is defined by in.rt.min and max, and the column with time in.rt.col.
# Additional parameters:
# in.by.cols - character vector with 'by' columns to calculate normalisation per group
#              if NULL, no grouping is done
# in.robust - whether robust measures should be used (median instead of mean, mad instead of sd)
# in.type - type of normalization: z.score or mean (fi.e. old change w.r.t. mean)

myNorm = function(in.dt,
                  in.meas.col,
                  in.rt.col = 'RealTime',
                  in.rt.min = 10,
                  in.rt.max = 20,
                  in.by.cols = NULL,
                  in.robust = TRUE,
                  in.type = 'z.score') {
  loc.dt <-
    copy(in.dt) # copy so as not to alter original dt object w intermediate assignments
  
  if (is.null(in.by.cols)) {
    if (in.robust)
dmattek's avatar
Fixed:  
dmattek committed
386 387
      loc.dt.pre.aggr = loc.dt[get(in.rt.col) >= in.rt.min &
                                 get(in.rt.col) <= in.rt.max, .(meas.md = median(get(in.meas.col), na.rm = TRUE),
dmattek's avatar
dmattek committed
388 389
                                                               meas.mad = mad(get(in.meas.col), na.rm = TRUE))]
    else
dmattek's avatar
Fixed:  
dmattek committed
390 391
      loc.dt.pre.aggr = loc.dt[get(in.rt.col) >= in.rt.min &
                                 get(in.rt.col) <= in.rt.max, .(meas.md = mean(get(in.meas.col), na.rm = TRUE),
dmattek's avatar
dmattek committed
392 393 394 395 396
                                                               meas.mad = sd(get(in.meas.col), na.rm = TRUE))]
    
    loc.dt = cbind(loc.dt, loc.dt.pre.aggr)
  }  else {
    if (in.robust)
dmattek's avatar
Fixed:  
dmattek committed
397 398
      loc.dt.pre.aggr = loc.dt[get(in.rt.col) >= in.rt.min &
                                 get(in.rt.col) <= in.rt.max, .(meas.md = median(get(in.meas.col), na.rm = TRUE),
dmattek's avatar
dmattek committed
399 400
                                                               meas.mad = mad(get(in.meas.col), na.rm = TRUE)), by = in.by.cols]
    else
dmattek's avatar
Fixed:  
dmattek committed
401 402
      loc.dt.pre.aggr = loc.dt[get(in.rt.col) >= in.rt.min &
                                 get(in.rt.col) <= in.rt.max, .(meas.md = mean(get(in.meas.col), na.rm = TRUE),
dmattek's avatar
dmattek committed
403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418
                                                               meas.mad = sd(get(in.meas.col), na.rm = TRUE)), by = in.by.cols]
    
    loc.dt = merge(loc.dt, loc.dt.pre.aggr, by = in.by.cols)
  }
  
  
  if (in.type == 'z.score') {
    loc.dt[, meas.norm := (get(in.meas.col) - meas.md) / meas.mad]
  } else {
    loc.dt[, meas.norm := (get(in.meas.col) / meas.md)]
  }
  
  setnames(loc.dt, 'meas.norm', paste0(in.meas.col, '.norm'))
  
  loc.dt[, c('meas.md', 'meas.mad') := NULL]
  return(loc.dt)
dmattek's avatar
dmattek committed
419 420
}

dmattek's avatar
dmattek committed
421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519
# Plots a scatter plot with marginal histograms
# Points are connected by a line (grouping by cellID)
#
# Assumes an input of data.table with
# x, y - columns with x and y coordinates
# id - a unique point identifier (here corresponds to cellID)
# mid - a (0,1) column by which points are coloured (here corresponds to whether cells are within bounds)

myGgplotScat = function(dt.arg,
                        band.arg = NULL,
                        facet.arg = NULL,
                        facet.ncol.arg = 2,
                        xlab.arg = NULL,
                        ylab.arg = NULL,
                        plotlab.arg = NULL,
                        alpha.arg = 1,
                        group.col.arg = NULL) {
  p.tmp = ggplot(dt.arg, aes(x = x, y = y))
  
  if (is.null(group.col.arg)) {
    p.tmp = p.tmp +
      geom_point(alpha = alpha.arg, aes(group = id))
  } else {
    p.tmp = p.tmp +
      geom_point(aes(colour = as.factor(get(group.col.arg)), group = id), alpha = alpha.arg) +
      geom_path(aes(colour = as.factor(get(group.col.arg)), group = id), alpha = alpha.arg) +
      scale_color_manual(name = group.col.arg, values =c("FALSE" = rhg_cols[7], "TRUE" = rhg_cols[3], "SELECTED" = 'green'))
  }
  
  if (is.null(band.arg))
    p.tmp = p.tmp +
      stat_smooth(
        method = function(formula, data, weights = weight)
          rlm(formula, data, weights = weight, method = 'MM'),
        fullrange = FALSE,
        level = 0.95,
        colour = 'blue'
      )
  else {
    p.tmp = p.tmp +
      geom_abline(slope = band.arg$a, intercept = band.arg$b) +
      geom_abline(
        slope = band.arg$a,
        intercept =  band.arg$b + abs(band.arg$b)*band.arg$width,
        linetype = 'dashed'
      ) +
      geom_abline(
        slope = band.arg$a,
        intercept = band.arg$b - abs(band.arg$b)*band.arg$width,
        linetype = 'dashed'
      )
  }
  
  if (!is.null(facet.arg)) {
    p.tmp = p.tmp +
      facet_wrap(as.formula(paste("~", facet.arg)),
                 ncol = facet.ncol.arg)
    
  }
  
  
  if (!is.null(xlab.arg))
    p.tmp = p.tmp +
      xlab(paste0(xlab.arg, "\n"))
  
  if (!is.null(ylab.arg))
    p.tmp = p.tmp +
      ylab(paste0("\n", ylab.arg))
  
  if (!is.null(plotlab.arg))
    p.tmp = p.tmp +
      ggtitle(paste0(plotlab.arg, "\n"))
  
  
  
  p.tmp = p.tmp +
    theme_bw(base_size = 18, base_family = "Helvetica") +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major = element_blank(),
      axis.line.x = element_line(color = "black", size = 0.25),
      axis.line.y = element_line(color = "black", size = 0.25),
      axis.text.x = element_text(size = 12),
      axis.text.y = element_text(size = 12),
      strip.text.x = element_text(size = 14, face = "bold"),
      strip.text.y = element_text(size = 14, face = "bold"),
      strip.background = element_blank(),
      legend.key = element_blank(),
      legend.key.height = unit(1, "lines"),
      legend.key.width = unit(2, "lines"),
      legend.position = "none"
    )
  
  # Marginal distributions don;t work with plotly...
  # if (is.null(facet.arg))
  #   ggExtra::ggMarginal(p.scat, type = "histogram",  bins = 100)
  # else
  return(p.tmp)
}
dmattek's avatar
dmattek committed
520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535

myGgplotTheme = theme_bw(base_size = 18, base_family = "Helvetica") +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    axis.line.x = element_line(color = "black", size = 0.25),
    axis.line.y = element_line(color = "black", size = 0.25),
    axis.text.x = element_text(size = 12, angle = 45, hjust = 1),
    axis.text.y = element_text(size = 12),
    strip.text.x = element_text(size = 14, face = "bold"),
    strip.text.y = element_text(size = 14, face = "bold"),
    strip.background = element_blank(),
    legend.key = element_blank(),
    legend.key.height = unit(1, "lines"),
    legend.key.width = unit(2, "lines"),
    legend.position = "right"
dmattek's avatar
Mod:  
dmattek committed
536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594
  )


myPlotHeatmap <- function(data.arg,
                          dend.arg,
                          palette.arg,
                          palette.rev.arg = TRUE,
                          dend.show.arg = TRUE,
                          key.show.arg = TRUE,
                          margin.x.arg = 5,
                          margin.y.arg = 20,
                          nacol.arg = 0.5,
                          colCol.arg = NULL,
                          labCol.arg = NULL,
                          font.row.arg = 1,
                          font.col.arg = 1,
                          title.arg = 'Clustering') {
  
  if (palette.rev.arg)
    my_palette <-
    rev(colorRampPalette(brewer.pal(9, palette.arg))(n = 99))
  else
    my_palette <-
    colorRampPalette(brewer.pal(9, palette.arg))(n = 99)
  
  
  col_labels <- get_leaves_branches_col(dend.arg)
  col_labels <- col_labels[order(order.dendrogram(dend.arg))]
  
  if (dend.show.arg) {
    assign("var.tmp.1", dend.arg)
    var.tmp.2 = "row"
  } else {
    assign("var.tmp.1", FALSE)
    var.tmp.2 = "none"
  }
  
  loc.p = heatmap.2(
    data.arg,
    Colv = "NA",
    Rowv = var.tmp.1,
    srtCol = 90,
    dendrogram = var.tmp.2,
    trace = "none",
    key = key.show.arg,
    margins = c(margin.x.arg, margin.y.arg),
    col = my_palette,
    na.col = grey(nacol.arg),
    denscol = "black",
    density.info = "density",
    RowSideColors = col_labels,
    colRow = col_labels,
    colCol = colCol.arg,
    labCol = labCol.arg,
    #      sepcolor = grey(input$inPlotHierGridColor),
    #      colsep = 1:ncol(loc.dm),
    #      rowsep = 1:nrow(loc.dm),
    cexRow = font.row.arg,
    cexCol = font.col.arg,
dmattek's avatar
dmattek committed
595 596 597
    main = title.arg,
    symbreaks = FALSE,
    symkey = FALSE
dmattek's avatar
Mod:  
dmattek committed
598 599 600 601
  )
  
  return(loc.p)
}