R/plot_sammon.R

In dswatson/bioplotr: Pretty, simple, optionally interactive plots for bioinformatics analysis pipelines

#' Sammon Map
#'
#' This function plots a low-dimensional projection of an omic data matrix using
#' Sammon mapping.
#'
#' @param dat Omic data matrix or matrix-like object with rows corresponding to
#'   probes and columns to samples. It is strongly recommended that data be
#'   filtered and normalized prior to plotting. Raw counts stored in \code{
#'   \link[edgeR]{DGEList}} or \code{\link[DESeq2]{DESeqDataSet}} objects are
#'   automatically extracted and transformed to the log2-CPM scale, with a
#'   warning. Alternatively, an object of class \code{dist} which can be
#'   directly input to the Sammon mapping algorithm.
#' @param group Optional character or factor vector of length equal to sample
#'   size, or up to two such vectors organized into a list or data frame. Supply
#'   legend title(s) by passing a named list or data frame.
#' @param covar Optional continuous covariate. If non-\code{NULL}, then plot can
#'   render at most one \code{group} variable. Supply legend title by passing
#'   a named list or data frame.
#' @param dist Distance measure to be used. Supports all methods available in
#'   \code{\link[stats]{dist}}, \code{Rfast::\link[Rfast]{Dist}}, and \code{
#'   \link[vegan]{vegdist}}, as well as those implemented in the \code{bioDist} 
#'   package. See Details.
#' @param p Power of the Minkowski distance.
#' @param top Optional number (if > 1) or proportion (if < 1) of top probes to
#'   be used for mapping.
#' @param filter_method String specifying whether to apply a \code{"pairwise"}
#'   or \code{"common"} filter if \code{top} is non-\code{NULL}. See Details.
#' @param dims Vector specifying which dimensions to plot. Must be of length
#'   two unless \code{D3 = TRUE}.
#' @param label Label data points by sample name? Defaults to \code{FALSE}
#'   unless \code{group} and \code{covar} are both \code{NULL}. If \code{TRUE},
#'   then plot can render at most one phenotypic feature.
#' @param pal_group String specifying the color palette to use if \code{group}
#'   is non-\code{NULL}, or a vector of such strings with length equal to the
#'   number of vectors passed to \code{group}. Options include \code{"ggplot"}, 
#'   all qualitative color schemes available in \code{\href{
#'   https://bit.ly/2ipuEjn}{RColorBrewer}}, and the complete collection of 
#'   \code{\href{http://bit.ly/2bxnuGB}{ggsci}} palettes. Alternatively, a 
#'   character vector of colors with length equal to the cumulative number of 
#'   levels in \code{group}.
#' @param pal_covar String specifying the color palette to use if \code{covar}
#'   is non-\code{NULL}, or a vector of such strings with length equal to the
#'   number of vectors passed to \code{covar}. Options include the complete 
#'   collection of \code{\href{https://bit.ly/2n7D6tF}{viridis}} palettes, as 
#'   well as all sequential color schemes available in \code{\href{
#'   https://bit.ly/2ipuEjn}{RColorBrewer}}. Alternatively, a character vector 
#'   of colors representing a smooth gradient, or a list of such vectors with 
#'   length equal to the number of continuous variables to visualize.
#' @param size Point size. 
#' @param alpha Point transparency.
#' @param title Optional plot title.
#' @param legend Legend position. Must be one of \code{"bottom"}, \code{"left"},
#'   \code{"top"}, \code{"right"}, \code{"bottomright"}, \code{"bottomleft"},
#'   \code{"topleft"}, or \code{"topright"}.
#' @param hover Show sample name by hovering mouse over data point? If \code{
#'   TRUE}, the plot is rendered in HTML and will either open in your browser's
#'   graphic display or appear in the RStudio viewer.
#' @param D3 Render plot in three dimensions?
#'
#' @details
#' Sammon mapping is a variant of nonmetric MDS with a cost function designed
#' to better preserve local structure.
#'
#' The projection is calculated using the \code{MASS::\link[MASS]{sammon}}
#' function, which takes a distance matrix as input. Available distance
#' measures include: \code{"euclidean"}, \code{"maximum"}, \code{"manhattan"},
#' \code{"canberra"}, \code{"minkowski"}, \code{"cosine"}, \code{"pearson"},
#' \code{"kendall"}, \code{"spearman"}, \code{"bray"}, \code{"kulczynski"},
#' \code{"jaccard"}, \code{"gower"}, \code{"altGower"}, \code{"morisita"},
#' \code{"horn"}, \code{"mountford"}, \code{"raup"}, \code{"binomial"}, \code{
#' "chao"}, \code{"cao"}, \code{"mahalanobis"}, \code{"MI"}, or \code{"KLD"}.
#' Some distance measures are unsuitable for certain types of data. See \code{
#' \link{dist_mat}} for more details on these methods and links to documentation
#' on each.
#'
#' The \code{top} argument optionally filters data using either probewise
#' variance (if \code{filter_method = "common"}) or the leading fold change
#' method of Smyth et al. (if \code{filter_method = "pairwise"}). See \code{
#' \link{plot_mds}} for more details.
#'
#' @references
#' Sammon, J.W. (1969).
#' \href{http://theoval.cmp.uea.ac.uk/~gcc/matlab/sammon/sammon.pdf}{A
#' non-linear mapping for data structure analysis}. \emph{IEE Trans. Comput.},
#' \strong{C-18}: 401-409.
#'
#' @examples
#' mat <- matrix(rnorm(1000 * 5), nrow = 1000, ncol = 5)
#' plot_sammon(mat)
#'
#' library(DESeq2)
#' dds <- makeExampleDESeqDataSet()
#' dds <- rlog(dds)
#' plot_sammon(dds, group = colData(dds)$condition)
#'
#' @seealso
#' \code{\link[limma]{plot_mds}}, \code{\link{plot_tsne}}
#'
#' @export
#' @importFrom MASS sammon
#' @import dplyr
#' @import ggplot2
#'

plot_sammon <- function(
  dat,
          group = NULL,
          covar = NULL,
           dist = 'euclidean',
              p = 2L,
            top = 500L,
  filter_method = 'pairwise',
           dims = c(1L, 2L),
          label = FALSE,
      pal_group = 'npg',
      pal_covar = 'Blues',
           size = NULL, 
          alpha = NULL,
          title = 'Sammon Map',
         legend = 'right',
          hover = FALSE,
             D3 = FALSE
) {

  # Preliminaries
  if (!dat %>% is('dist')) {
    if (ncol(dat) < 3L) {
      stop('dat includes only ', ncol(dat), ' samples; need at least 3 for ',
           'Sammon mapping.')
    }
    d <- c('euclidean', 'maximum', 'manhattan', 'canberra', 'minkowski',
           'bhattacharyya', 'hellinger', 'kullback_leibler', 'cosine', 
           'bray', 'kulczynski', 'jaccard', 'gower', 'altGower', 'morisita', 
           'horn', 'mountford', 'raup' , 'binomial', 'chao', 'cao',
           'mahalanobis', 'pearson', 'kendall', 'spearman', 'MI')
    dist <- match.arg(dist, d)
    filter_method <- match.arg(filter_method, c('pairwise', 'common'))
  }
  if (!group %>% is.null) {
    group <- dat %>% format_features(group, var_type = 'Categorical')
    if (length(group) > 2L) {
      stop('Plot can render at most two categorical features.')
    }
    if (length(group) == 2L && !(covar %>% is.null)) {
      stop('Plot can render at most one categorical feature when a continuous ',
           'covariate is also supplied.')
    }
    group_cols <- colorize(pal = pal_group, var_type = 'Categorical',
                           n = length(levels(group[[1L]])))
  } else {
    group_cols <- NULL
  }
  if (!covar %>% is.null) {
    covar <- dat %>% format_features(covar, var_type = 'Continuous')
    if (length(covar) != 1L) {
      stop('Plot can render at most one continuous feature.')
    }
    covar_cols <- colorize(pal = pal_covar, var_type = 'Continuous')
  } else {
    covar_cols <- NULL
  }
  if (!c(group, covar) %>% is.null) {
    features <- c(covar, group)
    feature_names <- names(features)
    names(features) <- paste0('Feature', seq_along(features))
  } else {
    features <- feature_names <- NULL
  }
  if (length(dims) > 2L & !D3) {
    stop('dims must be of length 2 when D3 = FALSE.')
  } else if (length(dims) > 3L) {
    stop('dims must be a vector of length <= 3.')
  }
  if (label && length(features) == 2L) {
    stop('If label is TRUE, then plot can render at most one phenotypic ',
         'feature.')
  }
  locations <- c('bottom', 'left', 'top', 'right',
                 'bottomright', 'bottomleft', 'topleft', 'topright')
  legend <- match.arg(legend, locations)

  # Tidy data
  if (!dat %>% is('dist')) {
    dat <- matrixize(dat)
    dm <- dist_mat(dat, dist, p, top, filter_method)
  } else {
    dm <- dat
  }
  sm <- sammon(dm, k = max(dims), trace = FALSE)$points    # Project
  df <- tibble(Sample = colnames(dat))                     # Melt
  if (length(dims) == 2L) {
    df <- df %>% mutate(PC1 = sm[, min(dims)],
                        PC2 = sm[, max(dims)])
  } else {
    other <- setdiff(dims, c(min(dims), max(dims)))
    df <- df %>% mutate(PC1 = sm[, min(dims)],
                        PC2 = sm[, other],
                        PC3 = sm[, max(dims)])
  }
  if (!features %>% is.null) {
    df <- df %>% cbind(tbl_df(features))
  }

  # Build plot
  xlab <- paste('Sammon Mapping Dim', min(dims))
  ylab <- paste('Sammon Mapping Dim', max(dims))
  embed(df, group, covar, group_cols, covar_cols, feature_names,
        label, size, alpha, title, xlab, ylab, legend, hover, D3)

}

# PASS ARBITRARY DISTANCE MATRICES