auxfunc.R 32.1 KB
Newer Older
dmattek's avatar
dmattek committed
1
2
3
4
#
# Time Course Inspector: Shiny app for plotting time series data
# Author: Maciej Dobrzynski
#
dmattek's avatar
dmattek committed
5
# Auxilary functions & definitions of global constants
dmattek's avatar
dmattek committed
6
7
8
#


Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
9
10
11
12
13
library(ggplot2)
library(RColorBrewer)
library(gplots) # for heatmap.2
library(grid) # for modifying grob
library(Hmisc) # for CI calculation
dmattek's avatar
dmattek committed
14

15
16

# Global parameters ----
17
18
19
20

# if true, additional output printed to R console
DEB = T

21
# font sizes in pts for plots in the manuscript
Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
22
23
24
25
26
27
# PLOTFONTBASE = 8
# PLOTFONTAXISTEXT = 8
# PLOTFONTAXISTITLE = 8
# PLOTFONTFACETSTRIP = 10
# PLOTFONTLEGEND = 8

dmattek's avatar
dmattek committed
28
# font sizes in pts for screen display
Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
29
30
31
32
33
34
35
36
37
38
39
40
41
PLOTFONTBASE = 16
PLOTFONTAXISTEXT = 16
PLOTFONTAXISTITLE = 16
PLOTFONTFACETSTRIP = 20
PLOTFONTLEGEND = 16

# height (in pixels) of ribbon and single traj. plots
PLOTRIBBONHEIGHT = 500 # in pixels
PLOTTRAJHEIGHT = 500 # in pixels
PLOTPSDHEIGHT = 500 # in pixels
PLOTBOXHEIGHT = 500 # in pixels
PLOTSCATTERHEIGHT = 500 # in pixels
PLOTWIDTH = 85 # in percent
42
43

# default number of facets in plots
Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
44
PLOTNFACETDEFAULT = 3
45

dmattek's avatar
dmattek committed
46
47
48
49
50
51
52
53
54
55
56
57
# internal column names
COLRT   = 'realtime'
COLY    = 'y'
COLID   = 'id'
COLIDUNI = 'trackObjectsLabelUni'
COLGR   = 'group'
COLIN   = 'mid.in'
COLOBJN = 'obj.num'
COLPOSX = 'pos.x'
COLPOSY = 'pos.y'
COLIDX = 'IDX'
COLIDXDIFF = 'IDXdiff'
dmattek's avatar
dmattek committed
58
COLCL = 'cl'
dmattek's avatar
dmattek committed
59
60
61
62
63
64

# file names
FCSVOUTLIERS = 'outliers.csv'
FCSVTCCLEAN  = 'tCoursesSelected_clean.csv'
FPDFTCMEAN   = "tCoursesMeans.pdf"
FPDFTCSINGLE = "tCourses.pdf"
65
FPDFTCPSD    = 'tCoursesPsd.pdf'
dmattek's avatar
dmattek committed
66
67
68
69
FPDFBOXAUC   = 'boxplotAUC.pdf'
FPDFBOXTP    = 'boxplotTP.pdf'
FPDFSCATTER  = 'scatter.pdf'

dmattek's avatar
dmattek committed
70
# Colour definitions ----
dmattek's avatar
dmattek committed
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
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"
)

96
# list of palettes for the heatmap
dmattek's avatar
dmattek committed
97
98
99
100
101
102
103
104
105
106
l.col.pal = list(
  "White-Orange-Red" = 'OrRd',
  "Yellow-Orange-Red" = 'YlOrRd',
  "Reds" = "Reds",
  "Oranges" = "Oranges",
  "Greens" = "Greens",
  "Blues" = "Blues",
  "Spectral" = 'Spectral'
)

107
108
109
110
111
112
113
114
115
116
# 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
dmattek committed
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
# Clustering algorithms ----

s.cl.linkage = c("ward.D",
                 "ward.D2",
                 "single",
                 "complete",
                 "average",
                 "mcquitty",
                 "centroid")

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

s.cl.diss = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "DTW")
s.cl.spar.diss = c("squared.distance","absolute.value")


# Help text ----
dmattek's avatar
Added:    
dmattek committed
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
# 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")
    )
  )
}

160
help.text.short = c(
dmattek's avatar
dmattek committed
161
  'Load CSV file with a column of track IDs for removal. IDs should correspond to those used for plotting.',
162
  'If the track ID is unique only within a group, make it unique globally by combining with the grouping column.',
dmattek's avatar
dmattek committed
163
164
  'Interpolate missing time points and pre-existing NAs. The interval of the time column must be provided!',
  'Load CSV file with 5 columns: grouping, start and end tpts of stimulation, start and end of y-position, dummy column with ID.',
165
166
167
  'Select columns to group data according to treatment, condition, etc.',
  'Select math operation to perform on a single or two columns,',
  'Select range of time for further processing.',
dmattek's avatar
dmattek committed
168
  'Normalise time series to a selected region.',
dmattek's avatar
dmattek committed
169
170
  'Download time series after modification in this section.',
  'Long format: a row is a single data point. Wide format: a row contains entire time series with columns as time points.'
171
172
)

dmattek's avatar
dmattek committed
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
# Functions for data processing ----
#' Calculate the mean and CI around time series
#'
#' @param in.dt Data table in long format
#' @param in.col.meas Name of the column with the measurement
#' @param in.col.by Column names for grouping (default NULL - no grouping). Typically, you want to use at least a column with time.
#' @param in.type Choice of normal approximation or boot-strapping
#' @param ... Other params passed to smean.cl.normal and smean.cl.boot; these include \code{conf.int} for the confidence level, \code{B} for the number of boot-strapping iterations.
#'
#' @return Datatable with columns: Mean, lower and upper CI, and grouping columns if provided.
#' @export
#' @import data.table
#' @import Hmisc
#'
#' @examples
#'
#'
#' # generate synthetic time series; 100 time points long, with 10 randomly placed NAs
#' dt.tmp = genTraj(100, 10, 6, 3, in.addna = 10)
#'
#' # calculate single stats from all time points
#' calcTrajCI(dt.tmp, 'objCyto_Intensity_MeanIntensity_imErkCor')
#'
#' # calculate the mean and CI along the time course
#' calcTrajCI(dt.tmp, 'objCyto_Intensity_MeanIntensity_imErkCor', 'Metadata_RealTime')
LOCcalcTrajCI = function(in.dt, in.col.meas, in.col.by = NULL, in.type = c('normal', 'boot'), ...) {
  in.type = match.arg(in.type)
  
  if (in.type %like% 'normal')
    loc.dt = in.dt[, as.list(smean.cl.normal(get(in.col.meas), ...)), by = in.col.by] else
      loc.dt = in.dt[, as.list(smean.cl.boot(get(in.col.meas), ...)), by = in.col.by]
    
    return(loc.dt)
}

208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229

#' Calculate the power spectrum density for time-series
#'
#' @param in.dt Data table in long format
#' @param in.col.meas Name of the column with the measurement
#' @param in.col.id Name of the column with the unique series identifier
#' @param in.col.by Column names for grouping (default NULL - no grouping). PSD of individual trajectories will be averaged within a group.
#' @param in.method Name of the method for PSD estimation, must be one of c("pgram", "ar"). Default to "pgram*.
#' @param in.return.period Wheter to return densities though periods (1/frequencies) instead of frequencies.
#' @param ... Other paramters to pass to stats::spectrum()
#'
#' @return Datatable with columns: (frequency or period), spec (the density) and grouping column
#' @export
#' @import data.table
#'
#' @examples
LOCcalcPSD <- function(in.dt,
                    in.col.meas,
                    in.col.id,
                    in.col.by,
                    in.method = "pgram",
                    in.return.period = TRUE,
230
                    in.time.btwPoints = 1,
231
232
                    ...){
  require(data.table)
233
234
235
236
237
238
239
240
241
  # Method "ar" returns $spec as matrix whereas "pgram" returns a vector, custom function to homogenze output format
  mySpectrum <- function(x, ...){
    args_spec <- list(x=x, plot=FALSE)
    inargs <- list(...)
    args_spec[names(inargs)] <- inargs
    out <- do.call(spectrum, args_spec)
    out$spec <- as.vector(out$spec)
    return(out)
  }
242
243
244
  if(!in.method %in% c("pgram", "ar")){
    stop('Method should be one of: c("pgram", "ar"')
  }
245
246
247
248
249
  dt_spec <-  in.dt[, (mySpectrum(get(in.col.meas), plot = FALSE, method = in.method)[c("freq", "spec")]), by = in.col.id]
  dt_group <- in.dt[, .SD[1, get(in.col.by)], by = in.col.id]
  setnames(dt_group, "V1", in.col.by)
  dt_spec <- merge(dt_spec, dt_group, by = in.col.id)
  dt_agg <- dt_spec[, .(spec = mean(spec)), by = c(in.col.by, "freq")]
250
  if(in.return.period){
251
    dt_agg[, period := 1/freq]
252
253
254
    dt_agg[, freq := NULL]
    # Adjust period unit to go from frame unit  to time unit
    dt_agg[, period := period * in.time.btwPoints]
255
  } else {
256
    dt_agg[, freq := freq * (1/in.time.btwPoints)]
257
    setnames(dt_agg, "freq", "frequency")
258
259
260
261
262
  }
  return(dt_agg)
}


dmattek's avatar
dmattek committed
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
#' Generate synthetic CellProfiler output with single cell time series
#'
#'
#'
#' @param in.ntpts Number of time points (default 60)
#' @param in.ntracks Number of tracks per FOV (default 10)
#' @param in.nfov Number of FOV (default 6)
#' @param in.nwells Number of wells (default 1)
#' @param in.addna Number of NAs to add randomly in the data (default NULL)
#'
#' @return Data table with the follwoing columns: Metadata_Site, Metadata_Well, Metadata_RealTime, objCyto_Intensity_MeanIntensity_imErkCor (normal distributed),
#' objNuc_Intensity_MeanIntensity_imErkCor (normal distributed), objNuc_Location_X and objNuc_Location_Y (uniform ditributed), TrackLabel
#' @export
#' @import data.table

dmattek's avatar
dmattek committed
278
LOCgenTraj <- function(in.ntpts = 60, in.ntracks = 10, in.nfov = 6, in.nwells = 1, in.addna = NULL, in.addout = NULL) {
dmattek's avatar
dmattek committed
279
280
281
282
283
284
285
286
287
288
289
  
  x.rand.1 = c(rnorm(in.ntpts * in.ntracks * in.nfov * 1/3, 0.5, 0.1), rnorm(in.ntpts * in.ntracks * in.nfov * 1/3,   1, 0.2), rnorm(in.ntpts * in.ntracks * in.nfov * 1/3,  2, 0.5))
  x.rand.2 = c(rnorm(in.ntpts * in.ntracks * in.nfov * 1/3, 0.25, 0.1), rnorm(in.ntpts * in.ntracks * in.nfov * 1/3, 0.5, 0.2),  rnorm(in.ntpts * in.ntracks * in.nfov * 1/3, 1, 0.2))
  
  # add NA's for testing
  if (!is.null(in.addna)) {
    locTabLen = length(x.rand.1)
    x.rand.1[round(runif(in.addna) * locTabLen)] = NA
    x.rand.2[round(runif(in.addna) * locTabLen)] = NA
  }
  
dmattek's avatar
dmattek committed
290
291
292
  # add outliers for testing
  if (!is.null(in.addout)) {
    locTabLen = length(x.rand.1)
Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
293
294
    x.rand.1[round(runif(in.addout) * locTabLen)] = 5
    x.rand.2[round(runif(in.addout) * locTabLen)] = 5
dmattek's avatar
dmattek committed
295
296
  }
  
dmattek's avatar
dmattek committed
297
298
  x.arg = rep(seq(1, in.ntpts), in.ntracks * in.nfov)
  
Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
299
300
301
302
303
304
305
306
  dt.nuc = data.table(well = rep(LETTERS[1:in.nwells], each = in.ntpts * in.nfov * in.ntracks / in.nwells),
                      group = rep(1:in.nfov, each = in.ntpts * in.ntracks),
                      time = x.arg,
                      y1 = x.rand.1,
                      y2  = x.rand.2,
                      posx = runif(in.ntpts * in.ntracks * in.nfov, min = 0, max = 1),
                      posy = runif(in.ntpts * in.ntracks * in.nfov, min = 0, max = 1),
                      id = rep(1:(in.ntracks*in.nfov), each = in.ntpts))
dmattek's avatar
dmattek committed
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
  
  return(dt.nuc)
}

#' Normalize Trajectory
#'
#' 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.#'
#'
#' @param in.dt Data table in long format
#' @param in.meas.col String with the column name to normalize
#' @param in.rt.col String with the colum name holding time
#' @param in.rt.min Lower bound for time period used for normalization
#' @param in.rt.max Upper bound for time period used for normalization
#' @param in.by.cols String vector with 'by' columns to calculate normalization per group; if NULL, no grouping is done
#' @param in.robust Whether robust measures should be used (median instead of mean, mad instead of sd); default TRUE
#' @param in.type Type of normalization: z.score or mean (i.e. fold change w.r.t. mean); default 'z-score'
#'
#' @return Returns original dt with an additional column with normalized quantity.
#' @export
#' @import data.table

LOCnormTraj = function(in.dt,
                    in.meas.col,
dmattek's avatar
dmattek committed
334
                    in.rt.col = COLRT,
dmattek's avatar
dmattek committed
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
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
                    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)
      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),
                                                                meas.mad = mad(get(in.meas.col), na.rm = TRUE))]
    else
      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),
                                                                meas.mad = sd(get(in.meas.col), na.rm = TRUE))]
    
    loc.dt = cbind(loc.dt, loc.dt.pre.aggr)
  }  else {
    if (in.robust)
      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),
                                                                meas.mad = mad(get(in.meas.col), na.rm = TRUE)), by = in.by.cols]
    else
      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),
                                                                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
Added:    
dmattek committed
381

dmattek's avatar
dmattek committed
382
# Functions for clustering ----
dmattek's avatar
dmattek committed
383
384
385
386
387
388
389
390
391

# 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
392
393
  cat(file = stderr(), 'getDataCl \n')
  
dmattek's avatar
dmattek committed
394
  loc.clAssign = dendextend::cutree(in.dend, in.k, order_clusters_as_data = TRUE, )
dmattek's avatar
dmattek committed
395
396
397
398
  #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
dmattek's avatar
dmattek committed
399
400
401
  loc.dt.clAssign = as.data.table(loc.clAssign, keep.rownames = T)
  setnames(loc.dt.clAssign, c(COLID, COLCL))
  
dmattek's avatar
dmattek committed
402
  
403
404
  #cat('===============\ndataCl:\n')
  #print(loc.dt.cl)
dmattek's avatar
dmattek committed
405
  return(loc.dt.clAssign)
dmattek's avatar
Added:    
dmattek committed
406
407
}

dmattek's avatar
dmattek committed
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426

# 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)
  
427
428
  #cat('===============\ndataCl:\n')
  #print(loc.dt.cl)
dmattek's avatar
dmattek committed
429
430
431
432
433
  return(loc.dt.cl)
}



dmattek's avatar
Added:    
dmattek committed
434
435
436
437
438
439
440
441
442
443
444
445
446
# 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)))
}


dmattek's avatar
dmattek committed
447
# Custom plotting functions ----
dmattek's avatar
dmattek committed
448

dmattek's avatar
dmattek committed
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

#' Custom ggPlot theme based on theme_bw
#'
#' @param in.font.base
#' @param in.font.axis.text
#' @param in.font.axis.title
#' @param in.font.strip
#' @param in.font.legend
#'
#' @return
#' @export
#'
#' @examples
#'
LOCggplotTheme = function(in.font.base = 12,
                       in.font.axis.text = 12,
                       in.font.axis.title = 12,
                       in.font.strip = 14,
                       in.font.legend = 12) {
  loc.theme =
    theme_bw(base_size = in.font.base, base_family = "Helvetica") +
    theme(
      panel.spacing = unit(1, "lines"),
      panel.grid.minor = element_blank(),
      panel.grid.major = element_blank(),
      panel.border = element_blank(),
      axis.line = element_line(color = "black", size = 0.25),
      axis.text = element_text(size = in.font.axis.text),
      axis.title = element_text(size = in.font.axis.title),
      strip.text = element_text(size = in.font.strip, face = "bold"),
      strip.background = element_blank(),
      legend.key = element_blank(),
      legend.text = element_text(size = in.font.legend),
      legend.key.height = unit(1, "lines"),
      legend.key.width = unit(2, "lines"))
  
  return(loc.theme)
}

dmattek's avatar
dmattek committed
488
489
# Build Function to Return Element Text Object
# From: https://stackoverflow.com/a/36979201/1898713
dmattek's avatar
dmattek committed
490
LOCrotatedAxisElementText = function(angle, position='x', size = 12){
dmattek's avatar
dmattek committed
491
492
493
494
495
496
497
498
499
500
  angle     = angle[1]; 
  position  = position[1]
  positions = list(x=0, y=90, top=180, right=270)
  if(!position %in% names(positions))
    stop(sprintf("'position' must be one of [%s]",paste(names(positions),collapse=", ")), call.=FALSE)
  if(!is.numeric(angle))
    stop("'angle' must be numeric",call.=FALSE)
  rads = (-angle - positions[[ position ]])*pi/180
  hjust = round((1 - sin(rads)))/2
  vjust = round((1 + cos(rads)))/2
Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
501
  element_text(size = size, angle = angle, vjust = vjust, hjust = hjust)
dmattek's avatar
dmattek committed
502
503
}

504
505
# Plot individual time series
LOCplotTraj = function(dt.arg, # input data table
dmattek's avatar
Mod:    
dmattek committed
506
507
508
509
510
511
512
513
514
515
516
                        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!)
517
518
                        x.stim.arg = c('tstart', 'tend'), # column names in stimulation dt with x and xend parameters
                        y.stim.arg = c('ystart', 'yend'), # column names in stimulation dt with y and yend parameters
dmattek's avatar
dmattek committed
519
520
521
522
                        tfreq.arg = 1, # unused
                        xlim.arg = NULL, # limits of x-axis; for visualisation only, not trimmimng data
                        ylim.arg = NULL, # limits of y-axis; for visualisation only, not trimmimng data
                        stim.bar.width.arg = 0.5, # width of the stimulation line; plotted under time series
dmattek's avatar
Mod:    
dmattek committed
523
                        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
524
                        aux.label2 = NULL,
525
                        aux.label3 = NULL,
dmattek's avatar
Added:    
dmattek committed
526
527
528
529
530
                        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
531
532
  
  # aux.label12 are required for plotting XY positions in the tooltip of the interactive (plotly) graph
dmattek's avatar
dmattek committed
533
534
  p.tmp = ggplot(dt.arg,
                 aes_string(x = x.arg,
dmattek's avatar
dmattek committed
535
                            y = y.arg,
dmattek's avatar
Added:    
dmattek committed
536
                            group = group.arg,
537
538
539
540
541
                            label = group.arg))
  #,
  #                          label  = aux.label1,
  #                          label2 = aux.label2,
  #                          label3 = aux.label3))
dmattek's avatar
dmattek committed
542
  
dmattek's avatar
dmattek committed
543
544
545
546
547
548
549
550
551
552
553
554
555
  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
556
557
558
559
560
561
562
563
564
565

  # 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
566
567
    # adjust facet.color.arg to plot
    
dmattek's avatar
Mod:    
dmattek committed
568
569
570
571
572
    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
573
  
dmattek's avatar
Added:    
dmattek committed
574
575
  if ('mean' %in% loc.stat)
    p.tmp = p.tmp + 
dmattek's avatar
dmattek committed
576
577
    stat_summary(
      aes_string(y = y.arg, group = 1),
dmattek's avatar
dmattek committed
578
579
      fun.y = mean, 
      na.rm = T,
dmattek's avatar
Added:    
dmattek committed
580
      colour = 'red',
dmattek's avatar
dmattek committed
581
582
583
584
      linetype = 'solid',
      size = 1,
      geom = "line",
      group = 1
dmattek's avatar
Added:    
dmattek committed
585
586
587
588
589
590
591
    )

  if ('CI' %in% loc.stat)
    p.tmp = p.tmp + 
    stat_summary(
      aes_string(y = y.arg, group = 1),
      fun.data = mean_cl_normal,
dmattek's avatar
dmattek committed
592
      na.rm = T,
dmattek's avatar
Added:    
dmattek committed
593
      colour = 'red',
dmattek's avatar
Mod:    
dmattek committed
594
      alpha = 0.25,
dmattek's avatar
Added:    
dmattek committed
595
596
597
598
599
600
601
602
603
      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,
dmattek's avatar
dmattek committed
604
      na.rm = T,
dmattek's avatar
Added:    
dmattek committed
605
      colour = 'red',
dmattek's avatar
Mod:    
dmattek committed
606
      alpha = 0.25,
dmattek's avatar
Added:    
dmattek committed
607
608
609
610
611
612
613
      geom = "ribbon",
      group = 1
    )
  
  
  
  p.tmp = p.tmp + 
dmattek's avatar
dmattek committed
614
615
616
    facet_wrap(as.formula(paste("~", facet.arg)),
               ncol = facet.ncol.arg,
               scales = "free_x")
617
618
619
620

  # plot stimulation bars underneath time series
  # dt.stim.arg is read separately and should contain 4 columns with
  # xy positions of beginning and end of the bar
dmattek's avatar
dmattek committed
621
622
  if(!is.null(dt.stim.arg)) {
    p.tmp = p.tmp + geom_segment(data = dt.stim.arg,
623
624
625
626
627
                                 aes_string(x = x.stim.arg[1],
                                            xend = x.stim.arg[2],
                                            y = y.stim.arg[1],
                                            yend = y.stim.arg[2],
                                            group = 'group'),
dmattek's avatar
dmattek committed
628
                                 colour = rhg_cols[[3]],
629
                                 size = stim.bar.width.arg) 
dmattek's avatar
dmattek committed
630
631
  }
  
dmattek's avatar
dmattek committed
632
  p.tmp = p.tmp + coord_cartesian(xlim = xlim.arg, ylim = ylim.arg)
dmattek's avatar
dmattek committed
633
  
dmattek's avatar
dmattek committed
634
635
636
637
  p.tmp = p.tmp + 
    xlab(paste0(xlab.arg, "\n")) +
    ylab(paste0("\n", ylab.arg)) +
    ggtitle(plotlab.arg) +
638
639
640
641
642
    LOCggplotTheme(in.font.base = PLOTFONTBASE, 
                   in.font.axis.text = PLOTFONTAXISTEXT, 
                   in.font.axis.title = PLOTFONTAXISTITLE, 
                   in.font.strip = PLOTFONTFACETSTRIP, 
                   in.font.legend = PLOTFONTLEGEND) + 
643
    theme(legend.position = "top")
dmattek's avatar
dmattek committed
644
  
dmattek's avatar
Mod:    
dmattek committed
645
  return(p.tmp)
dmattek's avatar
dmattek committed
646
647
}

648
649
650
651
652
653
654
655
656
657
# Plot average time series with CI together in one facet
LOCplotTrajRibbon = function(dt.arg, # input data table
                          x.arg, # string with column name for x-axis
                          y.arg, # string with column name for y-axis
                          group.arg = NULL, # string with column name for grouping time series (here, it's a column corresponding to grouping by condition)
                          col.arg = NULL, # colour pallette for individual time series
                          dt.stim.arg = NULL, # data table with stimulation pattern
                          x.stim.arg = c('tstart', 'tend'), # column names in stimulation dt with x and xend parameters
                          y.stim.arg = c('ystart', 'yend'), # column names in stimulation dt with y and yend parameters
                          stim.bar.width.arg = 0.5,
dmattek's avatar
dmattek committed
658
659
                          xlim.arg = NULL, # limits of x-axis; for visualisation only, not trimmimng data
                          ylim.arg = NULL, # limits of y-axis; for visualisation only, not trimmimng data
660
661
662
663
664
665
666
667
668
669
670
671
672
                          ribbon.lohi.arg = c('Lower', 'Upper'),
                          ribbon.fill.arg = 'grey50',
                          ribbon.alpha.arg = 0.5,
                          xlab.arg = NULL,
                          ylab.arg = NULL,
                          plotlab.arg = NULL) {
  
  p.tmp = ggplot(dt.arg, aes_string(x = x.arg, group = group.arg)) +
    geom_ribbon(aes_string(ymin = ribbon.lohi.arg[1], ymax = ribbon.lohi.arg[2]),
                fill = ribbon.fill.arg,
                alpha = ribbon.alpha.arg) +
    geom_line(aes_string(y = y.arg, colour = group.arg))
  
dmattek's avatar
dmattek committed
673

674
675
676
677
678
679
680
681
682
683
684
685
686
687
  # plot stimulation bars underneath time series
  # dt.stim.arg is read separately and should contain 4 columns with
  # xy positions of beginning and end of the bar
  if(!is.null(dt.stim.arg)) {
    p.tmp = p.tmp + geom_segment(data = dt.stim.arg,
                                 aes_string(x = x.stim.arg[1],
                                     xend = x.stim.arg[2],
                                     y = y.stim.arg[1],
                                     yend = y.stim.arg[2]),
                                 colour = rhg_cols[[3]],
                                 size = stim.bar.width.arg,
                                 group = 1) 
  }

dmattek's avatar
dmattek committed
688
  p.tmp = p.tmp + coord_cartesian(xlim = xlim.arg, ylim = ylim.arg)
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
  
  if (is.null(col.arg)) {
    p.tmp = p.tmp +
      scale_color_discrete(name = '')
  } else {
    p.tmp = p.tmp +
      scale_colour_manual(values = col.arg, name = '')
  }
  
  if (!is.null(plotlab.arg))
    p.tmp = p.tmp + ggtitle(plotlab.arg)
  
  p.tmp = p.tmp +
    xlab(xlab.arg) +
    ylab(ylab.arg)
  
  return(p.tmp)
706
707
}

708
# Plot average power spectrum density per facet
majpark21's avatar
majpark21 committed
709
710
711
712
713
LOCplotPSD <- function(dt.arg, # input data table
                    x.arg, # string with column name for x-axis
                    y.arg, # string with column name for y-axis
                    group.arg=NULL, # string with column name for grouping time series (here, it's a column corresponding to grouping by condition)
                    xlab.arg = x.arg,
714
                    ylab.arg = y.arg,
715
                    facet.color.arg = NULL){
majpark21's avatar
majpark21 committed
716
717
718
719
720
721
  require(ggplot2)
  if(length(setdiff(c(x.arg, y.arg, group.arg), colnames(dt.arg))) > 0){
    stop(paste("Missing columns in dt.arg: ", setdiff(c(x.arg, y.arg, group.arg), colnames(dt.arg))))
  }
  p.tmp <- ggplot(dt.arg, aes_string(x=x.arg, y=y.arg)) +
    geom_line() +
722
    geom_rug(sides="b", alpha = 1, color = "lightblue") +
majpark21's avatar
majpark21 committed
723
724
    facet_wrap(group.arg) +
    labs(x = xlab.arg, y = ylab.arg)
725
  
726
727
728
729
730
731
732
733
  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', group.arg)
    
    # adjust facet.color.arg to plot
    
734
    p.tmp = p.tmp +
735
736
737
      geom_hline(data = loc.dt.cl, colour = facet.color.arg, yintercept = loc.y.max, size = 4) +
      scale_colour_manual(values = facet.color.arg,
                          name = '')
738
739
  }
  
majpark21's avatar
majpark21 committed
740
741
  return(p.tmp)
}
742

dmattek's avatar
dmattek committed
743
744
745
746
747
748
749
750
# 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)

dmattek's avatar
dmattek committed
751
LOCggplotScat = function(dt.arg,
dmattek's avatar
dmattek committed
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
                        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(
dmattek's avatar
dmattek committed
775
776
777
        # method = function(formula, data, weights = weight)
        #   rlm(formula, data, weights = weight, method = 'MM'),
        method = "lm",
dmattek's avatar
dmattek committed
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
        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 +
820
821
822
823
824
    LOCggplotTheme(in.font.base = PLOTFONTBASE, 
                   in.font.axis.text = PLOTFONTAXISTEXT, 
                   in.font.axis.title = PLOTFONTAXISTITLE, 
                   in.font.strip = PLOTFONTFACETSTRIP, 
                   in.font.legend = PLOTFONTLEGEND) + 
825
826
    theme(legend.position = "none")

dmattek's avatar
dmattek committed
827
828
829
830
831
832
  # 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
833

834

dmattek's avatar
dmattek committed
835
LOCplotHeatmap <- function(data.arg,
dmattek's avatar
Mod:    
dmattek committed
836
837
838
839
840
841
842
843
844
845
846
847
                          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,
848
                          breaks.arg = NULL,
dmattek's avatar
Mod:    
dmattek committed
849
850
                          title.arg = 'Clustering') {
  
851
852
  loc.n.colbreaks = 99
  
dmattek's avatar
Mod:    
dmattek committed
853
854
  if (palette.rev.arg)
    my_palette <-
855
    rev(colorRampPalette(brewer.pal(9, palette.arg))(n = loc.n.colbreaks))
dmattek's avatar
Mod:    
dmattek committed
856
857
  else
    my_palette <-
858
    colorRampPalette(brewer.pal(9, palette.arg))(n = loc.n.colbreaks)
dmattek's avatar
Mod:    
dmattek committed
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
  
  
  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
894
895
    main = title.arg,
    symbreaks = FALSE,
896
897
    symkey = FALSE,
    breaks = if (is.null(breaks.arg)) NULL else seq(breaks.arg[1], breaks.arg[2], length.out = loc.n.colbreaks+1)
dmattek's avatar
Mod:    
dmattek committed
898
899
900
901
  )
  
  return(loc.p)
}