embedCells: Generate cell-state embedding
In phemd: Phenotypic EMD for comparison of single-cell samples

Description Usage Arguments Details Value Examples

Takes as input a Phemd object with aggregated data and returns updated object containing cell-state embedding

embedCells(
  obj,
  cell_model = c("monocle2", "seurat", "phate"),
  data_model = "negbinomial_sz",
  phate_ncluster = 8,
  phate_cluster_seed = NULL,
  ...
)

`obj`	'Phemd' object containing aggregated data
`cell_model`	Method to use to generate cell-state embedding. Currently supports "phate" and "monocle2". If using the Seurat to model the cell-state space, please identify cell subtypes as outlined in the Seurat software package and then use the `bindSeuratObj` function.
`data_model`	Only relevant if cell_model = "monocle2". One of the following: 'negbinomial_sz', 'negbinomial', 'tobit', 'uninormal', 'gaussianff'. See "Family Function" table at the following link for more details on selecting the proper one. http://cole-trapnell-lab.github.io/monocle-release/docs/#getting-started-with-monocle
`phate_ncluster`	Only relevant if cell_model = "phate". Number of cell state clusters to return when using PHATE
`phate_cluster_seed`	Only relevant if cell_model = "phate". Seed to use when performing cell state clustering (optional)
`...`	Additional parameters to be passed to `reduceDimension` function for Monocle or `phate` function for PHATE

aggregateSamples needs to be called before running this function.

Same as input 'Phemd' object containing additional cell-state embedding object

my_phemdObj <- createDataObj(all_expn_data, all_genes, as.character(snames_data))
my_phemdObj_lg <- removeTinySamples(my_phemdObj, 10)
my_phemdObj_lg <- aggregateSamples(my_phemdObj_lg, max_cells=1000)
my_phemdObj_lg <- embedCells(my_phemdObj_lg, cell_model='monocle2', data_model = 'gaussianff', sigma=0.02, maxIter=2)