select_genes: Select genes in a cell_data_set for dimensionality reduction
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 select_features 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.

select_genes(
  cds,
  fit_min = 1,
  fit_max = Inf,
  logmean_ul = Inf,
  logmean_ll = -Inf,
  top_n = NULL
)

`cds`	the cell_data_set upon which to perform this operation.
`fit_min`	the minimum multiple of the dispersion fit calculation; default = 1
`fit_max`	the maximum multiple of the dispersion fit calculation; default = Inf
`logmean_ul`	the maximum multiple of the dispersion fit calculation; default = Inf
`logmean_ll`	the maximum multiple of the dispersion fit calculation; default = Inf
`top`	top_n if specified, will override the fit_min and fit_max to select the top n most variant features. logmena_ul and logmean_ll can still be used.