utils.R

library(Seurat)
library(dplyr)
library(ggplot2)
library(plotly)
library(plyr)
library(dplyr)
library(varhandle)
library(reshape2)


##Helper calculation and data functions

#All plots were desigend around a width of 330 pixels, so we scale around that for different screen sizes
scaleRatio <- function(inputWidth){
  return(inputWidth/330)
}

PercentAbove <- function(x){
  return(length(x = x[x > 0]) / length(x = x))
}

MaxMutate <- function(x){
  return(x / max(x))
}

get_shared_genes <- function(inputGeneList1, inputGeneList2, topN){
  gene_list1 = dplyr::distinct(as.data.frame(inputGeneList1) %>% mutate_if(is.factor,as.character))
  gene_list2 = dplyr::distinct(as.data.frame(inputGeneList2) %>% mutate_if(is.factor,as.character))
  colnames(gene_list1) = c("gene")
  colnames(gene_list2) = c("gene")
  shared <- dplyr::semi_join(as.data.frame(gene_list1),as.data.frame(gene_list2),by="gene")
  return(dplyr::top_n(shared,topN)$gene)
}

##Helper plotting functions

GetClusterPlot <- function(inputDataList, inputDataIndex, inputWidth, inputHeight){
  
  inputDataObj = inputDataList[[inputDataIndex]]
  
  x_ax <- list(
    title = "",
    zeroline = FALSE,
    showline = FALSE,
    showticklabels = FALSE,
    showgrid = FALSE,
    scaleanchor = 'y',
    scaleratio = inputDataObj$x_scale_ratio_clusterPlot
  )
  
  y_ax <- list(
    title = "",
    zeroline = FALSE,
    showline = FALSE,
    showticklabels = FALSE,
    showgrid = FALSE
    #scaleratio = inputDataObj$y_scale_ratio_clusterPlot
  )
  
  #p <- plot_ly(inputDataObj$plot_df,source="plot_cluster",hoverinfo="skip",x=~dim1,y=~dim2,type="scattergl",width=300,mode="markers",marker=list(color=~colorVec,size=2)) %>%
  #  layout(
  #    #autosize = TRUE,
  #    title=inputDataObj$name,
  #    xaxis = x_ax,
  #    yaxis= y_ax
  #  ) %>% config(displayModeBar = F)
  
  p <- plot_ly(inputDataObj$plot_df, source="plot_cluster", width=inputWidth) %>%
    add_trace(x=~dim1,y=~dim2,hoverinfo="text",type="scattergl",mode="markers",text=~cluster_description,key=~cluster,marker=list(size=2*scaleRatio(inputHeight),color=~colorVec),opacity=0.4) %>%
    #add_trace(type="scatter",mode="text",textposition="center",x=organoid$title_coords$x_center, y=organoid$title_coords$y_center, text=organoid$title_coords$cluster,font=list(face="bold")) %>%
    add_annotations(x=inputDataObj$title_coords$x_center, y=inputDataObj$title_coords$y_center, text=sprintf("<b>%s</b>",inputDataObj$title_coords$cluster), showarrow=FALSE, font=list(size=11*scaleRatio(inputHeight))) %>%
    layout(
      autosize = TRUE,
      title=inputDataObj$name,
      xaxis = x_ax,
      yaxis= y_ax
    ) %>% hide_colorbar() %>% config(displayModeBar = F)
                                     
  return(p)
}

GetPlotData <- function(inputDataObj, inputGene){
  single_gene <- mutate(inputDataObj$plot_df[,1:2],gene=as.numeric(FetchData(inputDataObj$seurat_data,inputGene))) %>% arrange(gene)
  colnames(single_gene) = c("dim1","dim2","gene")
  return(single_gene)
}

GetExpressionPlot <- function(inputDataList, inputDataIndex, inputGene, inputWidth, inputHeight){
  
  inputDataObj = inputDataList[[inputDataIndex]]
  
  #On initialization, check if the inputGene is not defined
  if(inputGene==""){
    return(NULL)
  }
  else{
    x_ax <- list(
      title = "",
      zeroline = FALSE,
      showline = FALSE,
      showticklabels = FALSE,
      showgrid = FALSE,
      scaleanchor = 'y',
      scaleratio = inputDataObj$x_scale_ratio_clusterPlot
    )
    
    y_ax <- list(
      title = "",
      zeroline = FALSE,
      showline = FALSE,
      showticklabels = FALSE,
      showgrid = FALSE
      #scaleratio = inputDataObj$y_scale_ratio_clusterPlot
    )
    
    single_gene <- GetPlotData(inputDataObj, inputGene)
    p <- plot_ly(single_gene,source="plot_expression",x=~dim1,y=~dim2,type="scattergl",width=inputWidth,mode="markers", text=~gene, color=~gene,marker=list(size=2*scaleRatio(inputHeight)),hoverinfo="text",name=inputGene,colors=c("grey90", "red")) %>%
      layout(
        #autosize = TRUE,
        title=inputGene,
        xaxis = x_ax,
        yaxis= y_ax
      ) %>% hide_colorbar() %>% config(displayModeBar = F)
    
    return(p)
  }
}

GetDotPlot <- function(inputDataList, inputDataIndex, inputGeneList, inputWidth)
{
  
  inputDataObj = inputDataList[[inputDataIndex]]
  
  if(length(inputGeneList)==0){
    return(NULL)
  }
  else{
    #Get the gene expression values and scale them so the max value for each gene is 1
    gene_exp = FetchData(inputDataObj$seurat_data,inputGeneList)
    #Combine the cluster assignments with the gene expression data
    multiple_genes <- as.data.frame(cbind(cluster=as.character(inputDataObj$plot_df$cluster),as.data.frame(gene_exp)))
    
    #Calculate the average expression per gene per cluster
    avgs <- multiple_genes %>% group_by(cluster) %>% dplyr::summarise_all(funs(mean))
    #Normalize so max is 1, melt the dataframe so we can plot it, and max sure the clusters are factors for proper plotting
    avgs <- melt(cbind(cluster=avgs$cluster,avgs %>% select(-cluster) %>% dplyr::mutate_all(funs(MaxMutate))),id.vars=c("cluster"))
    colnames(avgs) = c("cluster","gene","average_expression")
    #avgs$cluster = as.factor(avgs$cluster)
    
    #Calculate the percent of cells that each gene was detected in per cluster
    p_above <- melt(multiple_genes %>% group_by(cluster) %>% dplyr::summarise_all(funs(PercentAbove)),id.vars=c("cluster"))
    
    #Combine the calculations
    combined = cbind(avgs,percent_above=100*p_above[,3]*scaleRatio(inputWidth))
    #Reverse the row order so it plots correctly - from https://stat.ethz.ch/pipermail/r-help/2008-September/175012.html
    rev_combined <- combined[rev(rownames(combined)),]
    #Add the hover text
    rev_combined <- mutate(rev_combined,hover_text=sprintf("Cluster: %s\nAvg. Expression: %0.3f\nPercent Cells: %0.2f",cluster,average_expression,percent_above))
    
    y_ax <- list(
      title = "",
      zeroline = FALSE,
      showline = TRUE,
      showticklabels = TRUE,
      showgrid = FALSE,
      categoryorder = "trace"
    )
    
    x_ax <- list(
      title = "",
      zeroline = FALSE,
      showline = TRUE,
      showticklabels = TRUE,
      showgrid = FALSE,
      categoryorder = "array",
      categoryarray = inputDataObj$category_order
    )
    
    t <- list(
      size = 12*scaleRatio(inputWidth))
    
    #colorbar=list(title='Avg. expr.')
    p <- plot_ly(rev_combined,source="plot_dot",x=~cluster,y=~gene,type="scattergl",mode="markers",width=inputWidth,text=~hover_text,hoverinfo="text",marker=list(symbol="circle",size=rev_combined$percent_above,sizemode="area",color=~average_expression)) %>%
      layout(
        title = 'Dot Plot',
        #autosize = TRUE,
        showlegend = FALSE,
        yaxis = y_ax,
        xaxis = x_ax,
        font = t
      ) #%>% config(displayModeBar = F)
    
    return(p)
  }
}