calculate_gene_dispersion: Calculate dispersion genes in a cell_data_set object
In scfurl/m3addon: This package adds to the popular "monocle3"

Monocle3 aims to learn how cells transition through a biological program of gene expression changes in an experiment. Each cell can be viewed as a point in a high-dimensional space, where each dimension describes the expression of a different gene. Identifying the program of gene expression changes is equivalent to learning a trajectory that the cells follow through this space. However, the more dimensions there are in the analysis, the harder the trajectory is to learn. Fortunately, many genes typically co-vary with one another, and so the dimensionality of the data can be reduced with a wide variety of different algorithms. Monocle3 provides two different algorithms for dimensionality reduction via reduce_dimensions (UMAP and tSNE). The function calculate_dispersion is an optional step in the trajectory building process before preprocess_cds. After calculating dispersion for a cell_data_set using the calculate_gene_dispersion function, the select_genes function allows the user to identify a set of genes that will be used in downstream dimensionality reduction methods. These genes and their disperion and mean expression can be plotted using the plot_gene_dispersion function.

calculate_gene_dispersion(
  cds,
  q = 3,
  id_tag = "id",
  symbol_tag = "gene_short_name",
  method = "m3addon",
  removeOutliers = T
)

`cds`	the cell_data_set upon which to perform this operation.
`q`	the polynomial degree; default = 3.
`id_tag`	the name of the feature data column corresponding to the unique id - typically ENSEMBL id; default = "id".
`symbol_tag`	the name of the feature data column corresponding to the gene symbol; default = "gene_short_name".