auxfunc.R 31.8 KB
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#
# Time Course Inspector: Shiny app for plotting time series data
# Author: Maciej Dobrzynski
#
# These are auxilary functions
#


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require(ggplot2)
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require(RColorBrewer)
require(gplots) # for heatmap.2
require(grid) # for modifying grob
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require(Hmisc) # for CI calculation
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# Global parameters ----
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# if true, additional output printed to R console
DEB = T

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# font sizes in pts for plots
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# PLOTFONTBASE = 8
# PLOTFONTAXISTEXT = 8
# PLOTFONTAXISTITLE = 8
# PLOTFONTFACETSTRIP = 10
# PLOTFONTLEGEND = 8

PLOTFONTBASE = 12
PLOTFONTAXISTEXT = 12
PLOTFONTAXISTITLE = 12
PLOTFONTFACETSTRIP = 16
PLOTFONTLEGEND = 12

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# default number of facets in plots
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PLOTNFACETDEFAULT = 2
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# 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'

# file names
FCSVOUTLIERS = 'outliers.csv'
FCSVTCCLEAN  = 'tCoursesSelected_clean.csv'
FPDFTCMEAN   = "tCoursesMeans.pdf"
FPDFTCSINGLE = "tCourses.pdf"
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FPDFTCPSD    = 'tCoursesPsd.pdf'
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FPDFBOXAUC   = 'boxplotAUC.pdf'
FPDFBOXTP    = 'boxplotTP.pdf'
FPDFSCATTER  = 'scatter.pdf'

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# Colour definitions ----
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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"
)

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# list of palettes for the heatmap
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l.col.pal = list(
  "White-Orange-Red" = 'OrRd',
  "Yellow-Orange-Red" = 'YlOrRd',
  "Reds" = "Reds",
  "Oranges" = "Oranges",
  "Greens" = "Greens",
  "Blues" = "Blues",
  "Spectral" = 'Spectral'
)

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# 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'
)

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# 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 ----
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# Creates a popup with help text
# From: https://gist.github.com/jcheng5/5913297
helpPopup <- function(title, content,
                      placement=c('right', 'top', 'left', 'bottom'),
                      trigger=c('click', 'hover', 'focus', 'manual')) {
  tagList(
    singleton(
      tags$head(
        tags$script("$(function() { $(\"[data-toggle='popover']\").popover(); })")
      )
    ),
    tags$a(
      href = "#", class = "btn btn-mini", `data-toggle` = "popover",
      title = title, `data-content` = content, `data-animation` = TRUE,
      `data-placement` = match.arg(placement, several.ok=TRUE)[1],
      `data-trigger` = match.arg(trigger, several.ok=TRUE)[1],
      #tags$i(class="icon-question-sign")
      # changed based on http://stackoverflow.com/questions/30436013/info-bubble-text-in-a-shiny-interface
      icon("question")
    )
  )
}

help.text = c(
  'Accepts CSV file with a column of cell IDs for removal. 
                   IDs should correspond to those used for plotting. 
  Say, the main data file contains columns Metadata_Site and TrackLabel. 
  These two columns should be then selected in UI to form a unique cell ID, e.g. 001_0001 where former part corresponds to Metadata_Site and the latter to TrackLabel.',
  'Plotting and data processing requires a unique cell ID across entire dataset. A typical dataset from CellProfiler assigns unique cell ID (TrackLabel) within each field of view (Metadata_Site).
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                   Therefore, a unique ID is created by concatenating these two columns. If the dataset already contains a unique ID, UNcheck this box and select a single column only.',
  'This option allows to interpolate NAs or missing data. Some rows in the input file might be missing because a particular time point might not had been acquired. 
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  This option, interpolates such missing points as well as points with NAs in the measurement column. When this option is checked, the interval of time column must be provided!',
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  'Accepts CSV file with 5 columns: grouping (e.g. condition), start and end time points of stimulation, start and end points of y-position, dummy column with id.'
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)

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help.text.short = c(
  'Load CSV file with a column of cell IDs for removal. IDs should correspond to those used for plotting.',
  'If the track ID is unique only within a group, make it unique globally by combining with the grouping column.',
  'Interpolate missing tpts and pre-existing NAs. When checked, the interval of 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.',
  '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.',
  'Normalise data to a selected region.',
  'Download data after modification in this section.'
)

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# 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)
}

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#' 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,
                    ...){
  require(data.table)
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  # 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)
  }
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  if(!in.method %in% c("pgram", "ar")){
    stop('Method should be one of: c("pgram", "ar"')
  }
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  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")]
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  if(in.return.period){
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    dt_agg[, period := 1/freq]
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    dt_agg[, frequency := NULL]
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  } else {
    setnames(dt_agg, "freq", "frequency")
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  }
  return(dt_agg)
}


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#' 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

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LOCgenTraj <- function(in.ntpts = 60, in.ntracks = 10, in.nfov = 6, in.nwells = 1, in.addna = NULL, in.addout = NULL) {
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  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
  }
  
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  # add outliers for testing
  if (!is.null(in.addout)) {
    locTabLen = length(x.rand.1)
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    x.rand.1[round(runif(in.addout) * locTabLen)] = 5
    x.rand.2[round(runif(in.addout) * locTabLen)] = 5
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  }
  
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  x.arg = rep(seq(1, in.ntpts), in.ntracks * in.nfov)
  
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  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))
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  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,
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                    in.rt.col = COLRT,
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                    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)
}


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# Functions for clustering ----
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# 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) {
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  cat(file = stderr(), 'getDataCl \n')
  
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  loc.m = dendextend::cutree(in.dend, in.k, order_clusters_as_data = TRUE)
  #print(loc.m)
  
  # The result of cutree containes named vector with names being cell id's
  # THIS WON'T WORK with sparse hierarchical clustering because there, the dendrogram doesn't have original id's
  loc.dt.cl = data.table(id = names(loc.m),
                         cl = loc.m)
  
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  #cat('===============\ndataCl:\n')
  #print(loc.dt.cl)
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  return(loc.dt.cl)
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}

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# 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)
  
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  #cat('===============\ndataCl:\n')
  #print(loc.dt.cl)
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  return(loc.dt.cl)
}



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# 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)))
}


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# Custom plotting functions ----
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#' 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)
}

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# Build Function to Return Element Text Object
# From: https://stackoverflow.com/a/36979201/1898713
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LOCrotatedAxisElementText = function(angle, position='x', size = 12){
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  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
  element_text(size = 12, angle = angle, vjust = vjust, hjust = hjust)
}

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# Plot individual time series
LOCplotTraj = function(dt.arg, # input data table
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                        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!)
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                        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
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                        tfreq.arg = 1,
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                        ylim.arg = NULL,
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                        stim.bar.width.arg = 0.5,
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                        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
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                        aux.label2 = NULL,
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                        aux.label3 = NULL,
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                        stat.arg = c('', 'mean', 'CI', 'SE')) {
  
  # match arguments for stat plotting
  loc.stat = match.arg(stat.arg, several.ok = TRUE)

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  # aux.label12 are required for plotting XY positions in the tooltip of the interactive (plotly) graph
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  p.tmp = ggplot(dt.arg,
                 aes_string(x = x.arg,
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                            y = y.arg,
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                            group = group.arg,
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                            label = group.arg))
  #,
  #                          label  = aux.label1,
  #                          label2 = aux.label2,
  #                          label3 = aux.label3))
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  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]))
  }
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  # 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)
    
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    # adjust facet.color.arg to plot
    
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    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 = '')
  }
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  if ('mean' %in% loc.stat)
    p.tmp = p.tmp + 
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    stat_summary(
      aes_string(y = y.arg, group = 1),
      fun.y = mean,
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      colour = 'red',
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      linetype = 'solid',
      size = 1,
      geom = "line",
      group = 1
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    )

  if ('CI' %in% loc.stat)
    p.tmp = p.tmp + 
    stat_summary(
      aes_string(y = y.arg, group = 1),
      fun.data = mean_cl_normal,
      colour = 'red',
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      alpha = 0.25,
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      geom = "ribbon",
      group = 1
    )
  
  if ('SE' %in% loc.stat)
    p.tmp = p.tmp + 
    stat_summary(
      aes_string(y = y.arg, group = 1),
      fun.data = mean_se,
      colour = 'red',
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      alpha = 0.25,
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      geom = "ribbon",
      group = 1
    )
  
  
  
  p.tmp = p.tmp + 
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    facet_wrap(as.formula(paste("~", facet.arg)),
               ncol = facet.ncol.arg,
               scales = "free_x")
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  # 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
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  if(!is.null(dt.stim.arg)) {
    p.tmp = p.tmp + geom_segment(data = dt.stim.arg,
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                                 aes_string(x = x.stim.arg[1],
                                            xend = x.stim.arg[2],
                                            y = y.stim.arg[1],
                                            yend = y.stim.arg[2],
                                            group = 'group'),
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                                 colour = rhg_cols[[3]],
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                                 size = stim.bar.width.arg) 
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  }
  
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  if (!is.null(ylim.arg)) 
    p.tmp = p.tmp + coord_cartesian(ylim = ylim.arg)
  
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  p.tmp = p.tmp + 
    xlab(paste0(xlab.arg, "\n")) +
    ylab(paste0("\n", ylab.arg)) +
    ggtitle(plotlab.arg) +
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    LOCggplotTheme(in.font.base = PLOTFONTBASE, 
                   in.font.axis.text = PLOTFONTAXISTEXT, 
                   in.font.axis.title = PLOTFONTAXISTITLE, 
                   in.font.strip = PLOTFONTFACETSTRIP, 
                   in.font.legend = PLOTFONTLEGEND) + 
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    theme(legend.position = "top")
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  return(p.tmp)
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}

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# 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,
                          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))
  
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  # 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) 
  }

  
  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)
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}

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# Plot average power spectrum density per facet
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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,
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                    ylab.arg = y.arg,
                    col.arg = NULL){
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  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() +
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    geom_rug(sides="b", alpha = 1, color = "lightblue") +
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    facet_wrap(group.arg) +
    labs(x = xlab.arg, y = ylab.arg)
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  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 = '')
  }
  
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  return(p.tmp)
}
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# 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)

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LOCggplotScat = function(dt.arg,
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                        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(
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        # method = function(formula, data, weights = weight)
        #   rlm(formula, data, weights = weight, method = 'MM'),
        method = "lm",
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        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 +
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    LOCggplotTheme(in.font.base = PLOTFONTBASE, 
                   in.font.axis.text = PLOTFONTAXISTEXT, 
                   in.font.axis.title = PLOTFONTAXISTITLE, 
                   in.font.strip = PLOTFONTFACETSTRIP, 
                   in.font.legend = PLOTFONTLEGEND) + 
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    theme(legend.position = "none")

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  # Marginal distributions don;t work with plotly...
  # if (is.null(facet.arg))
  #   ggExtra::ggMarginal(p.scat, type = "histogram",  bins = 100)
  # else
  return(p.tmp)
}
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LOCplotHeatmap <- function(data.arg,
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                          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,
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                          breaks.arg = NULL,
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                          title.arg = 'Clustering') {
  
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  loc.n.colbreaks = 99
  
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  if (palette.rev.arg)
    my_palette <-
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    rev(colorRampPalette(brewer.pal(9, palette.arg))(n = loc.n.colbreaks))
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  else
    my_palette <-
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    colorRampPalette(brewer.pal(9, palette.arg))(n = loc.n.colbreaks)
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  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,
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    main = title.arg,
    symbreaks = FALSE,
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    symkey = FALSE,
    breaks = if (is.null(breaks.arg)) NULL else seq(breaks.arg[1], breaks.arg[2], length.out = loc.n.colbreaks+1)
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  )
  
  return(loc.p)
}