R/krsa_scaleModel.R

In kalganem/KRSA: KRSA: Kinome Random Sampling Analyzer

#' Fits, scales, transforms, and normalize kinome array data
#'
#' Runs a linear model on the tidy kinome array data of the signal as a function of exposure time. It also scales, transforms, and normalize kinome array data based on the barcode
#'
#' @param df kinome array data tidy data
#' @param pep Peptide List
#' @param nobs_filter min number of data points to fit linear model
#'
#' @return list of scaled , normalized and grouped modeled data
#'
#' @family core functions
#'
#' @export
#'
#' @examples
#' TRUE

krsa_scaleModel <- function(df, pep, nobs_filter = 0) {

  df %>%
    dplyr::filter(Peptide %in% pep) %>%
    dplyr::select(SampleName, Peptide, Group, Barcode, Signal, ExposureTime) %>%
    tidyr::nest(-SampleName, -Peptide,-Group, -Barcode) %>%
    dplyr::group_by(SampleName, Peptide) %>%
    dplyr::mutate(
      fit = purrr::map(data,~ stats::lm(Signal ~ ExposureTime + 0, data = .x)),
      coefs = purrr::map(fit, broom::tidy),
      summary = purrr::map(fit, broom::glance),
      #.default argument to replace NA/NaN values
      slope = purrr::map_dbl(coefs,purrr::pluck,2,.default = 0),
      r.seq = purrr::map_dbl(summary, "r.squared"),
      nobs = purrr::map_dbl(summary, "nobs")
    ) %>%
    dplyr::ungroup() %>%
    dplyr::select(Group, Barcode, SampleName, Peptide, slope, r.seq,nobs) %>%
    dplyr::mutate(slope = log2(slope*100),
                  slope = ifelse(slope < 0, NA, slope),
                  slope = ifelse(nobs >= nobs_filter, slope, NA),
                  r.seq = ifelse(nobs >= nobs_filter, r.seq, NA),
                  ) -> df_model

  df_model %>%
    dplyr::group_by(Barcode, Peptide) %>%
    dplyr::mutate(Mean = mean(slope, na.rm = TRUE)) %>%
    dplyr::ungroup() %>%
    dplyr::mutate(
      slope = ifelse(Mean == 0,  0,  slope/Mean)
      ) -> df_model_norm

  df_model %>%
    dplyr::group_by(Group, Peptide) %>%
    dplyr::summarise(
      slope =  EnvStats::geoMean(slope, na.rm = TRUE),
      r.seq=EnvStats::geoMean(r.seq,na.rm = TRUE)) -> df_model_grouped

  return(list(scaled = df_model, normalized = df_model_norm, grouped = df_model_grouped))

}