Overview

This vignette describes the usage of the dimReduction parameter in the pandemonium app and how new functions can be written for it.

The input for dimReduction should be a named list of functions, where each function defines a dimension reduction and needs to be structured as follows.

Required function inputs:

mat = n x m matrix of coordinates to reduce dist = n x n distance matrix

While typically only one of the two is used for the dimension reduction, the interface requires that both arguments are accepted to avoid errors.

What the function should return:

A list object that contains the output of the dimension reduction and can be accessed as dimReduction(mat = mat, dist = dist)$Y = n x 2 matrix or data frame with dimension reduced coordinates for all observations.

How to write new functions

The standard way to write a new function to be passed to dimReduction is as a wrapper for a common dimension reduction algorithm. The defined function must be able to take mat and dist as parameters. If only one of them is used it is best to write the function with space for additional parameters with ... as seen in the following example.

tSNE_mat <- function(mat,...) {
  coord_red = list()
  coord_red$Y = Rtsne::Rtsne(mat)$Y
  coord_red
}

tSNE_dist <- function(dist,...) {
  coord_red = list()
  coord_red$Y = Rtsne::Rtsne(dist, is.dist = TRUE)$Y
  coord_red
}

newPandemonium(df, dim_reduction = list(tSNE_mat = tSNE_mat, tSNE_dist = tSNE_dist))

Some algorithms may take a dist object instead of a distance matrix. This can be done by converting the distance matrix using stats::as.dist(). An example is the implementation of umap.

umap <- function(dist,...) {
  ret <- list()
  ret$Y <- uwot::umap(stats::as.dist(dist))
  ret
}

newPandemonium(df, dim_reduction = list(umap = umap))

Understanding the dimReduction Function Input

Overview

Required function inputs:

What the function should return:

How to write new functions