order_cells

the cell_data_set upon which to perform this operation

a string specifying the reduced dimension method to
use when ordering cells. Currently only "UMAP" is supported.

reduction_method

NULL or a vector of starting principal points. If
provided, pseudotime will start (i.e. be zero) at these graph nodes. Both
<code>root_pr_nodes</code> and <code>root_cells</code> cannot be provided.

root_pr_nodes

NULL or a vector of starting cells. If provided,
pseudotime will start (i.e. be zero) at these cells. Both
<code>root_pr_nodes</code> and <code>root_cells</code> cannot be provided.

root_cells

Whether to show running information for order_cells

verbose

Assigns cells a pseudotime value based on their projection on the principal
graph learned in the <code>learn_graph</code> function and the position of chosen
root states. This function takes as input a cell_data_set and returns it
with pseudotime information stored internally.
<code>order_cells()</code> optionally takes "root" state(s) in the form of cell
or principal graph node IDs, which you can use to specify the start of the
trajectory. If you don't provide a root state, an plot will be generated
where you can choose the root state(s) interactively. The trajectory will be
composed of segments.

Monocle 3 performs clustering, differential expression and
trajectory analysis for single-cell expression experiments. It orders
individual cells according to progress through a biological process,
without knowing ahead of time which genes define progress through that
process. Monocle 3 also performs differential expression analysis,
clustering, visualization, and other useful tasks on single-cell expression
data.  It is designed to work with RNA-Seq data, but could be used with
other types as well.

order_cells: Orders cells according to pseudotime.

Description

Usage

Arguments

Value