run_interactive_monocle_gadget: Explore a dataset with monocle
In robAndrewCarter/rnaseqUtils: Utility Functions for RNASeq Data Analysis

This gadget uses monocle to plot a DDRTree and visualization of a branched heatmap. The branch point and root of the tree can be changed interactively.

run_interactive_monocle_gadget(expression_mat = matrix(),
  max_cell_counts = 1000, metadata_df = data.frame(),
  species = c("mmusculus", "hsapiens"), normalizing_scale_factor = NULL,
  cell_umi_counts = NULL)

`expression_mat:`	This is a CellxGene matrix that will be used to run monocle.
`max_cell_counts:`	Moncole can choke on large datasets, so this puts a cap on then number of cells from expression_mat that will be used to generate a 2D structure.
`metadata_df:`	This is a data.frame that contains metatadata for the cells in expression_mats. It is used for coloring the monocle plots by additinal variables. It must at the very least have a sample_name column that matches the rownames of expression_mat.
`species:`	this should be pretty obvious. Currently, 'mmusculus' and 'hsapiens' are valid
`normalizing_scale_factor:`	This is to un-normalize the data. This has to be done because of the different normalization procedure that monocle uses. This factor is just the median umi count that was used in the normalization of expression_mat. It can be found as an element of the list returned from get_normalized_expression_matrix.
`cell_umi_counts:`	This is a named character vector of cell UMI counts, again required for un-normalizing the data in expression_mat. It can also be found as an element of the list returned from get_normalized_expression_matrix.