R/krsa_heatmap.R

In CogDisResLab/KRSA: KRSA: Kinome Random Sampling Analyzer

#' Generates a heatmap based on the modeled pw data
#'
#' This function takes in the modeled pw data, peptides and produces and pheatmap
#'
#' @param data modeled pw data
#' @param peptides peptide list
#' @param samples (optional) sample names
#' @param groups (optional) group names
#' @param ... to pass to the pheatmap function
#'
#'
#' @return pheatmap object
#'
#' @family plots
#'
#' @export
#'
#' @examples
#' TRUE
krsa_heatmap <- function(data, peptides, samples = NULL, groups = NULL, ...) {
    data %>%
        dplyr::filter(Peptide %in% peptides) %>%
        {
            if (!is.null(samples)) dplyr::filter(., SampleName %in% samples) else .
        } %>%
        {
            if (!is.null(groups)) dplyr::filter(., Group %in% groups) else .
        } %>%
        dplyr::select(Peptide, SampleName, slope) %>%
        tidyr::spread(key = SampleName, value = slope) %>%
        column_to_rownames("Peptide") %>%
        as.matrix() -> HM_matrix2_test1

    data %>%
        dplyr::select(SampleName, Group) %>%
        dplyr::distinct() %>%
        column_to_rownames("SampleName") -> SamplesAnnotation

    dd <- stats::dist(scale(t(HM_matrix2_test1)))
    pheatmap::pheatmap(HM_matrix2_test1,
        clustering_distance_cols = dd,
        annotation_col = SamplesAnnotation,
        color = grDevices::colorRampPalette(c("yellow", "white", "red"))(n = 50),
        fontsize_row = 5,
        ...
    ) -> p1


    p1
}