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helper_module.R
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helper_module.R
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################################################################################
## UI Functions ##
################################################################################
#describe positions for sliders
usher <- function(data){
a = 1
b = a + floor((length(data)-1)/4)
c = b + 1
d = c + floor((length(data)-2)/4)
e = d + 1
f = e + floor((length(data)-3)/4)
g = f + 1
h = g + floor((length(data)-4)/4)
return(c(a, b, c, d, e, f, g, h))
}
#create sliders with columnsname as label
create_sliders <- function(data){
sliders = lapply(data, FUN = function(i){
sliderTextInput(
inputId = i,
label = paste("Weight:", strsplit(i, split = "-")[[1]][2]),
choices = c(0, 0.5, 1, 1.5, 2),
grid = TRUE,
selected = 0
)
})
boarder = usher(data)
list(column(2, sliders[boarder[1]:boarder[2]]),
column(2, sliders[boarder[3]:boarder[4]]),
column(2, sliders[boarder[5]:boarder[6]]),
column(2, sliders[boarder[7]:boarder[8]])
)
}
################################################################################
## Modules ##
################################################################################
#create namespace for every set of sliders
slidersUI <- function(id, data){
ns <- NS(id)
tagList(
create_sliders(ns(data))
)
}
sliders_mod <- function(input, output, session, data){
slider_vals <- c()
for(i in 1:length(data)) {
slider_vals <- c(slider_vals, input[[data[i]]])
}
return(slider_vals)
}
################################################################################
## Server functions ##
################################################################################
#plot profiles as barplot
plot_profiles <- function(data, data1 = as.data.frame(t(data)), ID, profile_columns, color = "#c00000",
grouped = FALSE, profile_columns2 = NULL, range_w = c(0,12), Norm_str = "[TMM]", title = F) {
data1$names = rownames(data1)
data1$names <- factor(data1$names, levels = data1[["names"]])
profile <- plot_ly(data1,
x = ~names[profile_columns],
y = t(data[ID, profile_columns]),
name = data1[1, ID],
type = "bar",
marker = list(color = color,
line = list(color = "#506784",
width = 1.5))
) %>% plotly::layout( title = title,
xaxis = list(title = data1[1, ID]),
yaxis = list(title = "Expression", range = range_w)
)
if(grouped == TRUE){
profile <- profile %>% add_trace(x = data1$names[profile_columns2],
y = t(data[ID, profile_columns2]),
name = paste(data1[1, ID], "B"),
marker = list(color = c('blue'))
)
}
profile
}
#create subplots
subplot_profiles <- function(dat, dat1, Gene_names, profile_columns = c(5:14), col_vec = c( "#618C84","#726E75","#948B89","#D0D1AC"), range_ = c(0:12), Norm = "[TMM]", title_ = "RNAseq Profile"){
plots = list()
j = 0
for(i in Gene_names){
j = j + 1
plot = plot_profiles(data = dat, data1 = dat1, ID = as.numeric(i) , profile_columns = profile_columns, TRUE, color = col_vec[j], range_w = range_ , Norm_str = Norm, title = title_)
plots = list.append(plots, plot)
}
subplot(plots, nrows = ceiling(length(Gene_names)/2), margin = 0.06, titleX = TRUE, titleY = TRUE)
}
#generate tables
generate_table <- function(data, columns = c(1:ncol(data)), columnwidth = 80, header_size = 11, font_size = 10,
c_orientation = c("left", "left", "center"),
h_orientation = c("left", "left", "center"),
Table_rows = 50 ){
m <- list(l = 3, r = 30, b = 20, t = 5, pad = 4)
plot_ly(type = "table",
columnwidth = columnwidth,
header = list(
values = paste("<b>",t(as.matrix(colnames(data)[columns])), "</b>"),
line = list(color = '#506784'),
fill = list(color = '#c00000'),
align = h_orientation,
font = list(color = 'white', size = c(header_size))),
cells = list(
values = t(as.matrix(unname(data[1:Table_rows, columns]))),
line = list(color = '#506784'),
fill = list(color = '#f1f1f1'),
align = c_orientation,
font = list(color = '#506784', size = font_size))) %>%
plotly::layout(autosize = FALSE, width = 1500, margin = m)
}
#get indices for specific column search
get_indices <- function(data = top_df, TextInput){
data$rank = rownames(data)
rownames(data) = data[,1]
Gene_indices = data[TextInput, "rank"]
rownames(data) = data$rank
data = data[, - ncol(data)]
return(Gene_indices)
}
#create single cell dotplot
sc_dotplot <- function(data){
fig <- plot_ly(data, x = ~State, y = ~Gene, text = ~paste("Detected in ", Pct*5, "% of cells", "<br>Average expression in detected cells: ", Expression), type = 'scatter', mode = 'markers',
marker = list(size = ~Pct, opacity = 0.8), color = ~Expression, colors = c( "blue", "#c00000"),
hoverinfo = "text")
fig <- fig %>% plotly::layout(title = 'Gene expression through the lifecycle',
xaxis = list(showgrid = T ,
tickvals = list(0,1,2,3,4,5,6,7,8,9),
tickmode = "array"),
yaxis = list(showgrid = T, type = "category",
categoryorder = "category descending"))
fig <- fig %>% plotly::layout(showlegend = F)
}
#create unicolor barplot
bulk_dotplot <- function(data, ytitle = "Genes", xtitle = "Variables", label = data$Ensembl_ID, boarder1 = 2, boarder2 = 4){
data$Ensembl_ID = paste(0:9, label)
data = melt(data[,c(1:boarder1, boarder2:ncol(data))])
fig <- plot_ly(data, x = ~variable, y = ~Ensembl_ID, text = paste("Expression: TPM", data$value), type = 'scatter', mode = 'markers',
marker = list(size = ~(value/max(value))*20, opacity = 0.8), color = "#c00000", colors = c( "#c00000"),
hoverinfo = "text")
fig <- fig %>% plotly::layout(title = 'Gene expression through the lifecycle',
xaxis = list(showgrid = T , title = xtitle,
tickvals = list(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22),
tickmode = "array"),
yaxis = list(showgrid = T, type = "category", title = ytitle,
categoryorder = "category descending"))
fig <- fig %>% plotly::layout(showlegend = F)
}
################################################################################
## Ranking ##
################################################################################
#calculate spot score
spot <- function(data, Variables, columns = c(1:ncol(data)), preamble = c(1:2), Candidate_Number = 50){
data1 = as.matrix(scale(data[,columns]))
data1[which(data1 > 4)] = 4
if(length(which(Variables > 0)) == 1){
mean_unwanted_cols = rowMeans(data1[, which(Variables == 0)])
mean_wanted_cols = data1[, which(Variables > 0)]
}else if(length(which(Variables > 0)) == 0){
mean_unwanted_cols = rowMeans(data1[, which(Variables == 0)])
mean_wanted_cols = 0
}else if(length(which(Variables == 0)) == 1){
mean_unwanted_cols = data1[, which(Variables == 0)]
mean_wanted_cols = data1[, which(Variables > 0)] %*% Variables[which(Variables > 0)]/sum(Variables[which(Variables > 0)])
}else if(length(which(Variables == 0)) == 0){
mean_unwanted_cols = 0
mean_wanted_cols = data1[, which(Variables > 0)] %*% Variables[which(Variables > 0)]/sum(Variables[which(Variables > 0)])
}else{
mean_unwanted_cols = rowMeans(data1[, which(Variables == 0)])
mean_wanted_cols = data1[, which(Variables > 0)] %*% Variables[which(Variables > 0)]/sum(Variables[which(Variables > 0)])
}
spot_Score = (mean_wanted_cols - mean_unwanted_cols)*(1 - mean_unwanted_cols)
merged_df <- cbind(data[,preamble], spot_Score, data[,columns], data[,ncol(data)])
merged_df = subset(merged_df, (mean_wanted_cols - mean_unwanted_cols) > 0)
merged_df_sorted <- merged_df %>% dplyr::arrange(desc(spot_Score))
top_df <- merged_df_sorted[1:Candidate_Number,]
}
#calculate correlation
correlation <- function(data, Variables, columns = c(1:ncol(data)), preamble = c(1:2), Candidate_Number = 50){
data1 = as.matrix(scale(data[,columns]))
data1[which(data1 > 4)] = 4
Correlation = cor(t(data1), Variables)
merged_df <- cbind(data[,preamble], Correlation, data[,columns], data[,ncol(data)])
merged_df_sorted <- merged_df %>% dplyr::arrange(desc(Correlation))
top_df <<- merged_df_sorted[1:Candidate_Number,]
}