R/normSL.R

In twesleyb/SwipProteomics: Analysis of Swip TMT Spatial Proteomics

normSL <- function(tp, groupBy = NULL) {
  suppressPackageStartupMessages({
    library(dplyr)
    library(data.table)
  })

  # Create an expression that will be evaluated to dynamically
  # group data based on user provided grouping factors. Default's
  # to Sample -- every replicate.
  tp <- ungroup(as.data.frame(tp))
  cmd <- paste0("group_by(tp, ", paste(groupBy, collapse = ", "), ")")

  # Calculate column sums for grouped samples.
  # FIXME: if .groups is set to 'drop' to avoid Warning msg, then error?
  data_list <- eval(parse(text = cmd)) %>%
    summarize(Total = sum(Intensity, na.rm = TRUE), .groups = "drop") %>%
    group_split()

  # Calculate normalization factors.
  data_SL <- lapply(data_list, function(x) {
    x$"Mean(Total)" <- mean(x$Total, na.rm = TRUE)
    x$NormFactor <- x$"Mean(Total)" / x$Total
    x$Norm <- x$Total * x$NormFactor
    return(x)
  }) %>% bind_rows()

  # Collect normalization factors as named vector.
  SL_factors <- data_SL$NormFactor
  names(SL_factors) <- as.character(data_SL[["Sample"]])

  # Normalize measurements by sample factors.
  tp$Intensity <- tp$Intensity * SL_factors[as.character(tp[["Sample"]])]

  return(as.data.table(tp))
}