order_cells | R Documentation |
Assigns cells a pseudotime value based on their projection on the principal
graph learned in the learn_graph
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.
order_cells()
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.
order_cells( cds, reduction_method = "UMAP", root_pr_nodes = NULL, root_cells = NULL, verbose = FALSE )
cds |
the cell_data_set upon which to perform this operation |
reduction_method |
a string specifying the reduced dimension method to use when ordering cells. Currently only "UMAP" is supported. |
root_pr_nodes |
NULL or a vector of starting principal points. If
provided, pseudotime will start (i.e. be zero) at these graph nodes. You
can find the principal point names by running plot_cells with
label_principal_points = TRUE. Both |
root_cells |
NULL or a vector of starting cells. If provided,
pseudotime will start (i.e. be zero) at these cells. Both
|
verbose |
Whether to show running information for order_cells |
an updated cell_data_set object.
cell_metadata <- readRDS(system.file('extdata', 'worm_embryo/worm_embryo_coldata.rds', package='monocle3')) gene_metadata <- readRDS(system.file('extdata', 'worm_embryo/worm_embryo_rowdata.rds', package='monocle3')) expression_matrix <- readRDS(system.file('extdata', 'worm_embryo/worm_embryo_expression_matrix.rds', package='monocle3')) cds <- new_cell_data_set(expression_data=expression_matrix, cell_metadata=cell_metadata, gene_metadata=gene_metadata) cds <- preprocess_cds(cds) cds <- align_cds(cds, alignment_group = "batch", residual_model_formula_str = "~ bg.300.loading + bg.400.loading + bg.500.1.loading + bg.500.2.loading + bg.r17.loading + bg.b01.loading + bg.b02.loading") cds <- reduce_dimension(cds) cds <- cluster_cells(cds) cds <- learn_graph(cds) cds <- order_cells(cds, root_pr_nodes='Y_21')
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