R/pattern_plots.R

In OmicInt: Omics Network Exploration

#' @title pattern_plots

#' @description pattern_plots function uses a subsetted pattern data from the function pattern_search. The function plots distribution plots as well as a selected set of genes and how they changed patterns. NOTE: if there are too many genes (>300), then individual gene expression plots will not be full because there is not enough colours in a palette to depict them all; in such a case, the function's violin plot can be used to assess the overall distribution and then the function should be repeated for filtered gene sets of interest to inspect individual expression values for each gene.
#'
#' @param data Requires a  data frame of normalised scores subsetted from pattern_search function. Class - string
#' @param meta Requires a  path variable to a data frame of metadata  in CSV format. Class - string
#' @param Condition Requires a condition  name to select if there are multiple conditions in meta data file, default "Condition_1". Conditions need to match between pattern_search and pattern_plot functions.Class - string
#' @param low the lowest value for the expression value; class - integer or float
#' @param high the highest value for the expression value; class -integer or float
#' @return  function plots multiple plots, class - plots
#' @import RColorBrewer
#' @importFrom reshape2 melt
#' @importFrom dplyr group_by
#' @importFrom dplyr ungroup
#' @importFrom plotly plot_ly
#' @importFrom tidyr spread
#' @importFrom gtools permutations
#' @importFrom ggplot2 aes
#' @importFrom ggplot2 ggtitle
#' @importFrom ggplot2 geom_point
#' @ImpportFrom ggplot2 geom_violin
#' @ImportFrom ggplot2 geom_line
#' @ImportFrom ggplot2 ggplot
#' @ImportFrom stats aggregate
#' @Import viridis
#' @import methods
#' @import utils
#' @examples
#' \dontrun{
#' path_to_test_data<- system.file("extdata","subsetted_data.csv", package="OmicInt")
#' path_to_meta_data<- system.file("extdata", "meta_data.csv", package="OmicInt")
#' # basic usage of pattern_search
#' pattern_plots(path_to_test_data,path_to_meta_data, 20, 10000)}
#' @export
pattern_plots<-function(data, meta, low=NA,high=NA, Condition="Condition_1"){


  if((is.na(low))||(is.na(high))){stop("You need to supply low an d high parameters for plotting")}

  #access data and select data

  meta<-utils::read.csv(meta, header=TRUE)
  meta<-meta[,c("Sample_ID",Condition)]
  colnames(meta)<-c("Sample_ID","Condition")

  data_temp<-data
  rownames(data_temp)<-data$"Symbol"
  data_temp<-data_temp[meta$"Sample_ID"]

  data_temp$"Mean"<-apply(data_temp,1,mean)
  data_temp<-data_temp[data_temp$"Mean">=low,]
  data_temp<-data_temp[data_temp$"Mean"<=high,]

  symbol_vals<-rownames(data_temp)

  data<-data[which(data$"Symbol"%in%symbol_vals),]



  #transform and merge data
  data_transform<-reshape2::melt(data,"Symbol")
  data_transform<-merge(data_transform,meta,by.x="variable",by.y="Sample_ID")

  temp_df_mean <-stats::aggregate(.~ Condition+Symbol, data_transform, mean, na.rm = TRUE)

  #plot data

      df<-data_transform

      df_mean <- dplyr::group_by(df, Condition)
      df_mean <-dplyr::ungroup(dplyr::summarize(df_mean, average = mean(value)))

      #to avoid plotting conflicts
      Condition<-df[,"Condition"]
      value<-df$"value"
      Condition_mean<-as.data.frame(df_mean)[,"Condition"]
      average<-df_mean$"average"

      p<- ggplot2::ggplot(df, ggplot2::aes(Condition,value, fill=Condition)) +ggplot2::geom_violin(trim=FALSE, alpha=0.5)+ggplot2::geom_point(data = df_mean,  mapping = ggplot2::aes(x = Condition_mean, y = average),color="red") +ggplot2::geom_line(data = df_mean, mapping = aes(x = Condition_mean, y = average,group=1))+viridis::scale_fill_viridis(discrete = TRUE)

      methods::show(p)

#prepare colours
      qual_col_pals <- RColorBrewer::brewer.pal.info[which(RColorBrewer::brewer.pal.info$"category"%in%c('qual')),] #max number of colours 335, setting for qual gives 74
      col_vector <- unlist(mapply(RColorBrewer::brewer.pal, qual_col_pals$"maxcolors", rownames(qual_col_pals)))

      colors <- col_vector[1:nlevels(as.factor(data$"Symbol"))]

    plot_ly(data = temp_df_mean, x = ~Condition, y = ~value, color = ~Symbol, colors = colors, type="bar")


}