Create an R function that normalizes data based on input values-CodePudding

I don't make to many complicated functions and typically stick with very basic ones. I have a question, how do I create a function that takes a dataset and normalizes based on desired normalization method and boxplots the output? Currently norm_method is different between the norm methods, was wondering if there is a way to call this in the start of function to pull through the correct method? Below is the code I created, but am stuck how to proceed.

library(reshape2)  # for melt
library(cowplot)   

demoData;

# target_deoData will need to be changed at some point
TestFunc <- function(demoData) {
  
  # Q3 norm (75th percentile)
  target_demoData <- normalize(demoData ,
                             norm_method = "quant", 
                             desiredQuantile = .75,
                             toElt = "q_norm")

  # Background normalization without spike
  target_demoData <- normalize(demoData ,
                             norm_method = "neg", 
                             fromElt = "exprs",
                             toElt = "neg_norm")
        
  boxplot(assayDataElement(demoData[,1:10], elt = "q_norm"),
        col = "red", main = "Q3",
        log = "y", names = 1:10, xlab = "Segment",
        ylab = "Counts, Q3 Normalized")
        
  boxplot(assayDataElement(demoData[,1:10], elt = "neg_norm"),
        col = "blue", main = "Neg",
        log = "y", names = 1:10, xlab = "Segment",
        ylab = "Counts, Neg. Normalized")
}

CodePudding user response：

You might want to consider designing your normalize() and assayDataElement() functions to take ..., which provides more flexibility.

In lieu of that, given the examples above, you could make a simple configuration list, and elements of that configuration are passed to your normalize() and assayDataElement() functions, like this:

TestFunc <- function(demoData, method=c("quant", "neg")) {
  
  method = match.arg(method)
  method_config = list(
    "quant" = list("norm_args" = list("norm_method" = "quant", desired_quantile = 0.75, "toElt" = "q_norm"),
                   "plot_args" = list("col"="red", main="Q3", ylab = "Counts, Q3 Normalized")),
    "neg" = list("norm_args" = list("fromElt" = "exprs", "toElt" = "neg_norm"),
                 "plot_args" = list("col"="blue", main="Neg", ylab = "Counts, Neg Normalized"))
  )
  
  mcn = method_config[[method]][["norm_args"]]
  mcp = method_config[[method]][["plot_args"]]
  
  # normalize the data
  target_demoData = do.call(normalize, c(list(data = demoData[1:10]), mcn))
  
  # get the plot
  boxplot(assayDataElement(
    demoData[1:10], elt=mcp[["toElt"]],col = mcp[["col"],main = mcp[["main"]],
    log = "y", names = 1:10, xlab = "Segment",ylab = mcp[["ylab"]]
  )
}

Again, using this approach is not as flexible as ... (and consider splitting into two functions.. one that returns normalized data, and a second function that generates the plot..