auxfunc.R 39.4 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
# number of miliseconds to delay reactions to changes in the UI
# used to delay output from sliders
MILLIS = 1000
dmattek's avatar
dmattek committed
20

21
22
23
# if true, additional output printed to R console
DEB = T

24
# font sizes in pts for plots in the manuscript
Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
25
26
27
28
29
30
# PLOTFONTBASE = 8
# PLOTFONTAXISTEXT = 8
# PLOTFONTAXISTITLE = 8
# PLOTFONTFACETSTRIP = 10
# PLOTFONTLEGEND = 8

dmattek's avatar
dmattek committed
31
# font sizes in pts for screen display
Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
32
33
34
35
36
37
38
39
40
41
42
43
44
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
45
46

# default number of facets in plots
Maciej Dobrzynski's avatar
Maciej Dobrzynski committed
47
PLOTNFACETDEFAULT = 3
48

dmattek's avatar
dmattek committed
49
# internal column names
dmattek's avatar
dmattek committed
50
COLRT   = 'time'
dmattek's avatar
dmattek committed
51
52
53
54
55
56
57
58
59
60
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
61
COLCL = 'cl'
dmattek's avatar
dmattek committed
62
63
64
65
66
67

# file names
FCSVOUTLIERS = 'outliers.csv'
FCSVTCCLEAN  = 'tCoursesSelected_clean.csv'
FPDFTCMEAN   = "tCoursesMeans.pdf"
FPDFTCSINGLE = "tCourses.pdf"
68
FPDFTCPSD    = 'tCoursesPsd.pdf'
dmattek's avatar
dmattek committed
69
70
71
72
FPDFBOXAUC   = 'boxplotAUC.pdf'
FPDFBOXTP    = 'boxplotTP.pdf'
FPDFSCATTER  = 'scatter.pdf'

dmattek's avatar
dmattek committed
73
# Colour definitions ----
dmattek's avatar
dmattek committed
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
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"
)

99
# list of palettes for the heatmap
dmattek's avatar
dmattek committed
100
l.col.pal = list(
dmattek's avatar
dmattek committed
101
102
103
104
  "Spectral" = 'Spectral',
  "Red-Yellow-Green" = 'RdYlGn',
  "Red-Yellow-Blue" = 'RdYlBu',
  "Greys" = "Greys",
dmattek's avatar
dmattek committed
105
106
107
  "Reds" = "Reds",
  "Oranges" = "Oranges",
  "Greens" = "Greens",
dmattek's avatar
dmattek committed
108
  "Blues" = "Blues"
dmattek's avatar
dmattek committed
109
110
)

111
112
113
114
115
116
117
118
119
120
# 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
121
122
123
124
125
126
127
128
129
130
131
# list of palettes for the dendrogram
l.col.pal.dend.2 = list(
  "Colorblind 10" = 'Color Blind',
  "Tableau 10" = 'Tableau 10',
  "Tableau 20" = 'Tableau 20',
  "Classic 10" = "Classic 10",
  "Classic 20" = "Classic 20",
  "Traffic 9" = 'Traffic',
  "Seattle Grays 5" = 'Seattle Grays'
)

dmattek's avatar
dmattek committed
132
# Help text ----
dmattek's avatar
dmattek committed
133
helpText.server = c(
dmattek's avatar
dmattek committed
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
  alDataFormat =  paste0(
    "<p>Switch between long and wide formats of input data. ",
    "TCI accepts CSV or compressed CSV files (gz or bz2).</p>",
    "<p><b>Long format</b> - a row is a single data point and consecutive time series are arranged vertically. ",
    "Data file should contain at least 3 columns separated with a comma:</p>",
    "<li>Identifier of a time series</li>",
    "<li>Time points</li>",
    "<li>A time-varying variable</li>",
    "<br>",
    "<p><b>Wide format</b> - a row is a time series with columns as time points.",
    "At least 3 columns shuold be present:</p>",
    "<li>First two columns in wide format should contain grouping and track IDs</li>",
    "<li>A column with a time point. Headers of columns with time points need to be numeric</li>"
  ),
  inDataGen1 =   paste0(
    "Generate 3 groups with 20 random synthetic time series. ",
    "Every time series contains 101 time points. ",
    "Track IDs are unique across entire dataset."
  ),
  chBtrajRem =   paste0(
    "Load CSV file with a column of track IDs for removal. ",
    "IDs should correspond to those used for plotting."
  ),
  chBstim =      paste0(
    "Load CSV file with stimulation pattern. Should contain 5 columns: ",
    "grouping, start and end time points of stimulation, start and end of y-position, dummy column with ID."
  ),
  chBtrajInter = paste0(
    "Interpolate missing measurements indicated with NAs in the data file. ",
    "In addition, interpolate a row that is completely missing from the data. ",
    "The interval of the time column must be provided to know which rows are missing."
  ),
  chBtrackUni =  paste0(
    "If the track ID in the uploaded dataset is unique only within a group (e.g. an experimental condition), ",
    "make it unique by prepending other columns to the track ID (typically a grouping column)."
  ),
dmattek's avatar
dmattek committed
170
171
172
173
174
175
176
177
178
  chBgroup    = "Select columns to group data according to treatment, condition, etc.",
  inSelMath   = "Select math operation to perform on a single or two measurement columns,",
  chBtimeTrim = "Trim time for further processing.",
  chBnorm     = "Divide measurements by the mean/median or calculate z-score with respect to selected time span.",
  rBnormMeth  = "Fold-change or z-score with respect to selected time span.",
  slNormRtMinMax = "Normalise with respect to this time span.",
  chBnormRobust  = "Calculate fold-change and z-score using the median and Median Absolute Deviation, instead of the mean and standard deviation.",
  chBnormGroup   = "Normalise to mean/median of selected time calculated globally, per group, or for individual time series.",
  downloadDataClean = "Download all time series after modifications in this panel.",
179
180
  alertNAsPresent              = "NAs present in the measurement column. Consider interpolation.",
  alertNAsPresentLong2WideConv = "Missing rows. Consider interpolation.",
dmattek's avatar
dmattek committed
181
  alertWideMissesNumericTime = "Non-numeric headers of time columns. Data in wide format should have numeric column headers corresponding to time points.",
dmattek's avatar
dmattek committed
182
  alertWideTooFewColumns     = "Insufficient columns. Data in wide format should contain at least 3 columns: grouping, track ID, and a single time point."
183
184
)

dmattek's avatar
dmattek committed
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
# 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')
dmattek's avatar
dmattek committed
210
211
212
213
214
LOCcalcTrajCI = function(in.dt,
                         in.col.meas,
                         in.col.by = NULL,
                         in.type = c('normal', 'boot'),
                         ...) {
dmattek's avatar
dmattek committed
215
216
217
  in.type = match.arg(in.type)
  
  if (in.type %like% 'normal')
dmattek's avatar
dmattek committed
218
219
220
221
222
    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)
dmattek's avatar
dmattek committed
223
224
}

225

226
227
228
229
230
231
232
233
234
#' Calculate standard error of the mean
#'
#' @param x Vector
#' @param na.rm Remove NAs; default = FALSE
#'
#' @return A scalar with the result
#' @export
#'
#' @examples
dmattek's avatar
dmattek committed
235
236
LOCstderr = function(x, na.rm = FALSE) {
  if (na.rm)
237
238
    x = na.omit(x)
  
dmattek's avatar
dmattek committed
239
  return(sqrt(var(x) / length(x)))
240
241
}

242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
#' 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,
dmattek's avatar
dmattek committed
258
259
260
261
262
263
264
                       in.col.meas,
                       in.col.id,
                       in.col.by,
                       in.method = "pgram",
                       in.return.period = TRUE,
                       in.time.btwPoints = 1,
                       ...) {
265
  require(data.table)
266
  # Method "ar" returns $spec as matrix whereas "pgram" returns a vector, custom function to homogenze output format
dmattek's avatar
dmattek committed
267
268
  mySpectrum <- function(x, ...) {
    args_spec <- list(x = x, plot = FALSE)
269
270
271
272
273
274
    inargs <- list(...)
    args_spec[names(inargs)] <- inargs
    out <- do.call(spectrum, args_spec)
    out$spec <- as.vector(out$spec)
    return(out)
  }
dmattek's avatar
dmattek committed
275
  if (!in.method %in% c("pgram", "ar")) {
276
277
    stop('Method should be one of: c("pgram", "ar"')
  }
dmattek's avatar
dmattek committed
278
279
  dt_spec <-
    in.dt[, (mySpectrum(get(in.col.meas), plot = FALSE, method = in.method)[c("freq", "spec")]), by = in.col.id]
280
281
282
  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)
dmattek's avatar
dmattek committed
283
284
285
286
  dt_agg <-
    dt_spec[, .(spec = mean(spec)), by = c(in.col.by, "freq")]
  if (in.return.period) {
    dt_agg[, period := 1 / freq]
287
288
289
    dt_agg[, freq := NULL]
    # Adjust period unit to go from frame unit  to time unit
    dt_agg[, period := period * in.time.btwPoints]
290
  } else {
dmattek's avatar
dmattek committed
291
    dt_agg[, freq := freq * (1 / in.time.btwPoints)]
292
    setnames(dt_agg, "freq", "frequency")
293
294
295
296
297
  }
  return(dt_agg)
}


298
#' Generate synthetic CellProfiler output with single-cell time series
dmattek's avatar
dmattek committed
299
300
301
302
303
304
305
306
307
308
309
310
#'
#' @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
311
312
313
314
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
364
LOCgenTraj <-
  function(in.ntpts = 60,
           in.ntracks = 10,
           in.nfov = 6,
           in.nwells = 1,
           in.addna = NULL,
           in.addout = NULL) {
    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
    }
    
    # add outliers for testing
    if (!is.null(in.addout)) {
      locTabLen = length(x.rand.1)
      x.rand.1[round(runif(in.addout) * locTabLen)] = 5
      x.rand.2[round(runif(in.addout) * locTabLen)] = 5
    }
    
    x.arg = rep(seq(1, in.ntpts), in.ntracks * in.nfov)
    
    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)
    )
    
    return(dt.nuc)
dmattek's avatar
dmattek committed
365
  }
dmattek's avatar
dmattek committed
366

dmattek's avatar
dmattek committed
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
LOCgenTraj2 <-
  function(n_perGroup = 20,
           sd_noise = 0.01,
           sampleFreq = 0.2,
           endTime = 50)
  {
    # Function definition ----------------------------------
    sim_expodecay_lagged_stim <-
      function (n,
                noise,
                interval.stim = 5,
                lambda = 0.2,
                freq = 0.2,
                end = 40)
      {
        require(data.table)
        tvec <- seq(0, end, by = freq)
        stim_time <- seq(interval.stim, end, interval.stim)
        stim_time_matrix <-
          matrix(stim_time, nrow = length(stim_time),
                 ncol = n)
        noise_matrix <- abs(replicate(n, rnorm(
          n = length(stim_time),
          mean = 0,
          sd = noise
        )))
        stim_time_matrix <- stim_time_matrix + noise_matrix
        trajs <- matrix(0, nrow = length(tvec), ncol = n)
        for (col in 1:ncol(stim_time_matrix)) {
          for (row in 1:nrow(stim_time_matrix)) {
            index <- which(tvec >= stim_time_matrix[row, col])[1]
            trajs[index, col] <- 1
          }
400
        }
dmattek's avatar
dmattek committed
401
402
403
404
405
406
        decrease_factor <- exp(-lambda * freq)
        for (col in 1:ncol(trajs)) {
          for (row in 2:nrow(trajs)) {
            if (trajs[row, col] != 1) {
              trajs[row, col] <- trajs[row - 1, col] * decrease_factor
            }
407
408
          }
        }
dmattek's avatar
dmattek committed
409
410
411
412
413
        trajs <- as.data.table(trajs)
        trajs <- cbind(seq(0, end, by = freq), trajs)
        colnames(trajs)[1] <- "Time"
        trajs <- melt(trajs, id.vars = "Time")
        return(trajs)
414
      }
dmattek's avatar
dmattek committed
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
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
    
    
    # Dataset creation -----------------------------------------------
    dt1 <-
      sim_expodecay_lagged_stim(
        n = n_perGroup,
        noise = 0.75,
        interval.stim = 10,
        lambda = 0.4,
        freq = sampleFreq,
        end = endTime
      )
    dt2 <-
      sim_expodecay_lagged_stim(
        n = n_perGroup,
        noise = 0.75,
        interval.stim = 10,
        lambda = 0.1,
        freq = sampleFreq,
        end = endTime
      )
    dt3 <-
      sim_expodecay_lagged_stim(
        n = n_perGroup,
        noise = 0.75,
        interval.stim = 10,
        lambda = 0.4,
        freq = sampleFreq,
        end = endTime
      )
    dt3[, value := value / 3]
    
    dt1[, Group := "fastDecay"]
    dt2[, Group := "slowDecay"]
    dt3[, Group := "lowAmplitude"]
    
    dt <- rbindlist(list(dt1, dt2, dt3))
    dt[, ID := sprintf("%s_%02d", Group, as.integer(gsub('[A-Z]', '', variable)))]
    dt[, variable := NULL]
    dt[, Group := as.factor(Group)]
    
    dt[, value := value + runif(1, -0.1, 0.1), by = .(Group, ID)]
    noise_vec <- rnorm(n = nrow(dt), mean = 0, sd = sd_noise)
    dt[, value := value + noise_vec]
    
    setnames(dt, "value", "Meas")
    setcolorder(dt, c("Group", "ID", "Time", "Meas"))
    
    return(dt)
  }
465

dmattek's avatar
dmattek committed
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
#' 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,
dmattek's avatar
dmattek committed
488
489
490
491
492
493
494
                       in.meas.col,
                       in.rt.col = COLRT,
                       in.rt.min = 10,
                       in.rt.max = 20,
                       in.by.cols = NULL,
                       in.robust = TRUE,
                       in.type = 'z.score') {
dmattek's avatar
dmattek committed
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
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
  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)
}


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
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
# Cluster validation ----

#Customize factoextra functions to accept dissimilarity matrix from start. Otherwise can't use distance functions that are not in base R, like DTW.
# Inherit and adapt hcut function to take input from UI, used for fviz_clust

LOChcut <-
function(x,
         k = 2,
         isdiss = inherits(x, "dist"),
         hc_func = "hclust",
         hc_method = "average",
         hc_metric = "euclidean") {

    if (!inherits(x, "dist")) {
    stop("x must be a distance matrix")
  }
  return(
    factoextra::hcut(
      x = x,
      k = k,
      isdiss = TRUE,
      hc_func = hc_func,
      hc_method = hc_method,
      hc_metric = hc_metric
    )
  )
}

# Modified from factoextra::fviz_nbclust
# Allow (actually enforce) x to be a distance matrix; no GAP statistics for compatibility

LOCnbclust <-
  function (x,
            FUNcluster = LOChcut,
            method = c("silhouette", "wss"),
            k.max = 10,
            verbose = FALSE,
            barfill = "steelblue",
            barcolor = "steelblue",
            linecolor = "steelblue",
            print.summary = TRUE,
            ...)
  {
    set.seed(123)
    
    if (k.max < 2)
      stop("k.max must bet > = 2")
    
    method = match.arg(method)
    
    if (!inherits(x, c("dist")))
      stop("x should be an object of class dist")
    
    else if (is.null(FUNcluster))
      stop(
        "The argument FUNcluster is required. ",
        "Possible values are kmeans, pam, hcut, clara, ..."
      )
    
    else if (method %in% c("silhouette", "wss")) {
      diss <- x  # x IS ENFORCED TO BE A DISSIMILARITY MATRIX
      
      v <- rep(0, k.max)
      
      if (method == "silhouette") {
        loc.mainlab = "Optimal number of clusters from silhouette analysis"
        loc.ylab <- "Average silhouette width"
        for (i in 2:k.max) {
          clust <- FUNcluster(x, i, ...)
          v[i] <-
            factoextra:::.get_ave_sil_width(diss, clust$cluster)
        }
      }
      else if (method == "wss") {
        loc.mainlab = "Optimal number of clusters from within cluster sum of squares"
        
        loc.ylab <- "Total within cluster sum of squares"
        
        for (i in 1:k.max) {
          clust <- FUNcluster(x, i, ...)
          v[i] <- factoextra:::.get_withinSS(diss, clust$cluster)
        }
      }
      
      df <- data.frame(clusters = as.factor(1:k.max), y = v)
      
      p <- ggpubr::ggline(
        df,
        x = "clusters",
        y = "y",
        group = 1,
        color = linecolor,
        ylab = loc.ylab,
        xlab = "Number of clusters",
        main = loc.mainlab
      )
      
      if (method == "silhouette")
        p <- p + geom_vline(xintercept = which.max(v),
                            linetype = 2,
                            color = linecolor)
      return(p)
    }
  }
dmattek's avatar
Added:    
dmattek committed
640

641
# Clustering ----
dmattek's avatar
dmattek committed
642
643
644
645
646
647
648
649
650

# 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
651
652
  cat(file = stderr(), 'getDataCl \n')
  
dmattek's avatar
dmattek committed
653
  loc.clAssign = dendextend::cutree(in.dend, in.k, order_clusters_as_data = TRUE, )
dmattek's avatar
dmattek committed
654
655
656
657
  #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
658
659
660
  loc.dt.clAssign = as.data.table(loc.clAssign, keep.rownames = T)
  setnames(loc.dt.clAssign, c(COLID, COLCL))
  
dmattek's avatar
dmattek committed
661
  
662
663
  #cat('===============\ndataCl:\n')
  #print(loc.dt.cl)
dmattek's avatar
dmattek committed
664
  return(loc.dt.clAssign)
dmattek's avatar
Added:    
dmattek committed
665
666
}

dmattek's avatar
dmattek committed
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685

# 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)
  
686
687
  #cat('===============\ndataCl:\n')
  #print(loc.dt.cl)
dmattek's avatar
dmattek committed
688
689
690
691
692
  return(loc.dt.cl)
}



dmattek's avatar
Added:    
dmattek committed
693
694
695
696
697
698
699
# 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(
dmattek's avatar
dmattek committed
700
701
702
703
704
    data.table(
      cl.no = dendextend::cutree(in.dend, k = in.k, order_clusters_as_data = TRUE),
      cl.col = loc.col_labels
    )
  ))
dmattek's avatar
Added:    
dmattek committed
705
706
}

dmattek's avatar
dmattek committed
707
# Custom plotting functions ----
dmattek's avatar
dmattek committed
708

dmattek's avatar
dmattek committed
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723

#' 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,
dmattek's avatar
dmattek committed
724
725
726
727
                          in.font.axis.text = 12,
                          in.font.axis.title = 12,
                          in.font.strip = 14,
                          in.font.legend = 12) {
dmattek's avatar
dmattek committed
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
  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"),
dmattek's avatar
dmattek committed
743
744
      legend.key.width = unit(2, "lines")
    )
dmattek's avatar
dmattek committed
745
746
747
748
  
  return(loc.theme)
}

dmattek's avatar
dmattek committed
749
750
# Build Function to Return Element Text Object
# From: https://stackoverflow.com/a/36979201/1898713
dmattek's avatar
dmattek committed
751
752
753
754
755
LOCrotatedAxisElementText = function(angle,
                                     position = 'x',
                                     size = 12) {
  angle     = angle[1]
  
dmattek's avatar
dmattek committed
756
  position  = position[1]
dmattek's avatar
dmattek committed
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
  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
  element_text(
    size = size,
    angle = angle,
    vjust = vjust,
    hjust = hjust
  )
dmattek's avatar
dmattek committed
777
778
}

779
# Plot individual time series
dmattek's avatar
dmattek committed
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
820
LOCplotTraj = 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,
                       # 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!)
                       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
                       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
                       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
                       aux.label2 = NULL,
                       aux.label3 = NULL,
                       stat.arg = c('', 'mean', 'CI', 'SE')) {
dmattek's avatar
Added:    
dmattek committed
821
822
  # match arguments for stat plotting
  loc.stat = match.arg(stat.arg, several.ok = TRUE)
dmattek's avatar
dmattek committed
823
  
dmattek's avatar
Added:    
dmattek committed
824
825
  
  # aux.label12 are required for plotting XY positions in the tooltip of the interactive (plotly) graph
dmattek's avatar
dmattek committed
826
  p.tmp = ggplot(dt.arg,
dmattek's avatar
dmattek committed
827
828
829
830
831
832
                 aes_string(
                   x = x.arg,
                   y = y.arg,
                   group = group.arg,
                   label = group.arg
                 ))
833
834
835
836
  #,
  #                          label  = aux.label1,
  #                          label2 = aux.label2,
  #                          label3 = aux.label3))
dmattek's avatar
dmattek committed
837
  
dmattek's avatar
dmattek committed
838
839
  if (is.null(line.col.arg)) {
    p.tmp = p.tmp +
dmattek's avatar
dmattek committed
840
841
      geom_line(alpha = 0.25,
                size = 0.25)
dmattek's avatar
dmattek committed
842
843
  }
  else {
dmattek's avatar
dmattek committed
844
845
846
847
848
849
850
851
852
853
854
855
856
    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
dmattek committed
857
  }
dmattek's avatar
dmattek committed
858
  
dmattek's avatar
Mod:    
dmattek committed
859
860
861
862
863
864
865
866
  # 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
867
868
    # adjust facet.color.arg to plot
    
dmattek's avatar
Mod:    
dmattek committed
869
    p.tmp = p.tmp +
dmattek's avatar
dmattek committed
870
871
872
873
874
875
      geom_hline(
        data = loc.dt.cl,
        colour = facet.color.arg,
        yintercept = loc.y.max,
        size = 4
      ) +
dmattek's avatar
Mod:    
dmattek committed
876
877
878
      scale_colour_manual(values = facet.color.arg,
                          name = '')
  }
dmattek's avatar
dmattek committed
879
  
dmattek's avatar
Added:    
dmattek committed
880
  if ('mean' %in% loc.stat)
dmattek's avatar
dmattek committed
881
    p.tmp = p.tmp +
dmattek's avatar
dmattek committed
882
883
    stat_summary(
      aes_string(y = y.arg, group = 1),
dmattek's avatar
dmattek committed
884
      fun.y = mean,
dmattek's avatar
dmattek committed
885
      na.rm = T,
dmattek's avatar
Added:    
dmattek committed
886
      colour = 'red',
dmattek's avatar
dmattek committed
887
888
889
890
      linetype = 'solid',
      size = 1,
      geom = "line",
      group = 1
dmattek's avatar
Added:    
dmattek committed
891
    )
dmattek's avatar
dmattek committed
892
  
dmattek's avatar
Added:    
dmattek committed
893
  if ('CI' %in% loc.stat)
dmattek's avatar
dmattek committed
894
    p.tmp = p.tmp +
dmattek's avatar
Added:    
dmattek committed
895
896
897
    stat_summary(
      aes_string(y = y.arg, group = 1),
      fun.data = mean_cl_normal,
dmattek's avatar
dmattek committed
898
      na.rm = T,
dmattek's avatar
Added:    
dmattek committed
899
      colour = 'red',
dmattek's avatar
Mod:    
dmattek committed
900
      alpha = 0.25,
dmattek's avatar
Added:    
dmattek committed
901
902
903
904
905
      geom = "ribbon",
      group = 1
    )
  
  if ('SE' %in% loc.stat)
dmattek's avatar
dmattek committed
906
    p.tmp = p.tmp +
dmattek's avatar
Added:    
dmattek committed
907
908
909
    stat_summary(
      aes_string(y = y.arg, group = 1),
      fun.data = mean_se,
dmattek's avatar
dmattek committed
910
      na.rm = T,
dmattek's avatar
Added:    
dmattek committed
911
      colour = 'red',
dmattek's avatar
Mod:    
dmattek committed
912
      alpha = 0.25,
dmattek's avatar
Added:    
dmattek committed
913
914
915
916
917
918
      geom = "ribbon",
      group = 1
    )
  
  
  
dmattek's avatar
dmattek committed
919
  p.tmp = p.tmp +
dmattek's avatar
dmattek committed
920
921
922
    facet_wrap(as.formula(paste("~", facet.arg)),
               ncol = facet.ncol.arg,
               scales = "free_x")
dmattek's avatar
dmattek committed
923
  
924
925
926
  # 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
927
928
929
930
931
932
933
934
935
936
937
938
939
  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],
        group = 'group'
      ),
      colour = rhg_cols[[3]],
      size = stim.bar.width.arg
    )
dmattek's avatar
dmattek committed
940
941
  }
  
dmattek's avatar
dmattek committed
942
  p.tmp = p.tmp + coord_cartesian(xlim = xlim.arg, ylim = ylim.arg)
dmattek's avatar
dmattek committed
943
  
dmattek's avatar
dmattek committed
944
  p.tmp = p.tmp +
dmattek's avatar
dmattek committed
945
946
947
    xlab(paste0(xlab.arg, "\n")) +
    ylab(paste0("\n", ylab.arg)) +
    ggtitle(plotlab.arg) +
dmattek's avatar
dmattek committed
948
949
950
951
952
953
954
    LOCggplotTheme(
      in.font.base = PLOTFONTBASE,
      in.font.axis.text = PLOTFONTAXISTEXT,
      in.font.axis.title = PLOTFONTAXISTITLE,
      in.font.strip = PLOTFONTFACETSTRIP,
      in.font.legend = PLOTFONTLEGEND
    ) +
955
    theme(legend.position = "top")
dmattek's avatar
dmattek committed
956
  
dmattek's avatar
Mod:    
dmattek committed
957
  return(p.tmp)
dmattek's avatar
dmattek committed
958
959
}

960
# Plot average time series with CI together in one facet
dmattek's avatar
dmattek committed
961
962
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
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,
                             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
                             ribbon.lohi.arg = c('Lower', 'Upper'),
                             # column names containing lower and upper bound for plotting the ribbon, e.g. for CI; set to NULL to avoid plotting the ribbon
                             ribbon.fill.arg = 'grey50',
                             ribbon.alpha.arg = 0.5,
                             xlab.arg = NULL,
                             ylab.arg = NULL,
                             plotlab.arg = NULL) {
989
990
991
  p.tmp = ggplot(dt.arg, aes_string(x = x.arg, group = group.arg))
  
  if (!is.null(ribbon.lohi.arg))
dmattek's avatar
dmattek committed
992
993
994
995
996
997
    p.tmp = p.tmp +
      geom_ribbon(
        aes_string(ymin = ribbon.lohi.arg[1], ymax = ribbon.lohi.arg[2]),
        fill = ribbon.fill.arg,
        alpha = ribbon.alpha.arg
      )
998
999
  
  p.tmp = p.tmp + geom_line(aes_string(y = y.arg, colour = group.arg))
1000
  
dmattek's avatar
dmattek committed
1001
  
1002
1003
1004
  # 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
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
  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
    )
1018
  }
dmattek's avatar
dmattek committed
1019
  
dmattek's avatar
dmattek committed
1020
  p.tmp = p.tmp + coord_cartesian(xlim = xlim.arg, ylim = ylim.arg)
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
  
  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)
1038
1039
}

1040
# Plot average power spectrum density per facet
dmattek's avatar
dmattek committed
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
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,
                       ylab.arg = y.arg,
                       facet.color.arg = NULL) {
majpark21's avatar
majpark21 committed
1052
  require(ggplot2)
dmattek's avatar
dmattek committed
1053
1054
1055
1056
  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)
    )))
majpark21's avatar
majpark21 committed
1057
  }
dmattek's avatar
dmattek committed
1058
  p.tmp <- ggplot(dt.arg, aes_string(x = x.arg, y = y.arg)) +
majpark21's avatar
majpark21 committed
1059
    geom_line() +
dmattek's avatar
dmattek committed
1060
1061
1062
    geom_rug(sides = "b",
             alpha = 1,
             color = "lightblue") +
majpark21's avatar
majpark21 committed
1063
1064
    facet_wrap(group.arg) +
    labs(x = xlab.arg, y = ylab.arg)
1065
  
1066
1067
1068
1069
1070
1071
1072
  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
    
1073
    p.tmp = p.tmp +
dmattek's avatar
dmattek committed
1074
1075
1076
1077
1078
1079
      geom_hline(
        data = loc.dt.cl,
        colour = facet.color.arg,
        yintercept = loc.y.max,
        size = 4
      ) +
1080
1081
      scale_colour_manual(values = facet.color.arg,
                          name = '')
1082
1083
  }
  
majpark21's avatar
majpark21 committed
1084
1085
  return(p.tmp)
}
1086

dmattek's avatar
dmattek committed
1087
1088
1089
#' Plot a scatter plot with an optional linear regression
#'
#' @param dt.arg input of data.table with 2 columns with x and y coordinates
dmattek's avatar
dmattek committed
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
#' @param facet.arg
#' @param facet.ncol.arg
#' @param xlab.arg
#' @param ylab.arg
#' @param plotlab.arg
#' @param alpha.arg
#' @param trend.arg
#' @param ci.arg

LOCggplotScat = function(dt.arg,
                         facet.arg = NULL,
                         facet.ncol.arg = 2,
                         xlab.arg = NULL,
                         ylab.arg = NULL,
                         plotlab.arg = NULL,
                         alpha.arg = 1,
                         trend.arg = T,
                         ci.arg = 0.95) {
dmattek's avatar
dmattek committed
1108
  p.tmp = ggplot(dt.arg, aes(x = x, y = y, label = id)) +
dmattek's avatar
dmattek committed
1109
    geom_point(alpha = alpha.arg)
dmattek's avatar
dmattek committed
1110
  
dmattek's avatar
dmattek committed
1111
  if (trend.arg) {
dmattek's avatar
dmattek committed
1112
1113
    p.tmp = p.tmp +
      stat_smooth(
dmattek's avatar
dmattek committed
1114
        method = "lm",
dmattek's avatar
dmattek committed
1115
        fullrange = FALSE,
dmattek's avatar
dmattek committed
1116
        level = ci.arg,
dmattek's avatar
dmattek committed
1117
1118
1119
        colour = 'blue'
      )
  }
dmattek's avatar
dmattek committed
1120
  
dmattek's avatar
dmattek committed
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
  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 +
dmattek's avatar
dmattek committed
1141
1142
1143
1144
1145
1146
1147
    LOCggplotTheme(
      in.font.base = PLOTFONTBASE,
      in.font.axis.text = PLOTFONTAXISTEXT,
      in.font.axis.title = PLOTFONTAXISTITLE,
      in.font.strip = PLOTFONTFACETSTRIP,
      in.font.legend = PLOTFONTLEGEND
    ) +
1148
    theme(legend.position = "none")
dmattek's avatar
dmattek committed
1149
  
dmattek's avatar
dmattek committed
1150
1151
  return(p.tmp)
}
dmattek's avatar
dmattek committed
1152

1153

dmattek's avatar
dmattek committed
1154
LOCplotHeatmap <- function(data.arg,
dmattek's avatar
dmattek committed
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
                           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,
                           breaks.arg = NULL,
                           title.arg = 'Clustering') {
1169
1170
  loc.n.colbreaks = 99
  
dmattek's avatar
Mod:    
dmattek committed
1171
1172
  if (palette.rev.arg)
    my_palette <-
dmattek's avatar
dmattek committed
1173
      rev(colorRampPalette(brewer.pal(9, palette.arg))(n = loc.n.colbreaks))
dmattek's avatar
Mod:    
dmattek committed
1174
1175
  else
    my_palette <-
dmattek's avatar
dmattek committed
1176
      colorRampPalette(brewer.pal(9, palette.arg))(n = loc.n.colbreaks)
dmattek's avatar
Mod:    
dmattek committed
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
  
  
  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
1212
1213
    main = title.arg,
    symbreaks = FALSE,
1214
    symkey = FALSE,
dmattek's avatar
dmattek committed
1215
1216
1217
1218
    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
1219
1220
1221
1222
  )
  
  return(loc.p)
}