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Run Monocle2 analysis

Source: R/RunMonocle.R
RunMonocle2.Rd

Run Monocle2 analysis

Usage

RunMonocle2(
  srt,
  assay = NULL,
  layer = "counts",
  group.by = NULL,
  expressionFamily = "negbinomial.size",
  features = NULL,
  feature_type = "HVF",
  disp_filter = "mean_expression >= 0.1 & dispersion_empirical >= 1 * dispersion_fit",
  max_components = 2,
  reduction_method = "DDRTree",
  norm_method = "log",
  residualModelFormulaStr = NULL,
  pseudo_expr = 1,
  root_state = NULL,
  backend = c("r", "cpp"),
  n_neighbors = 30,
  ddrtree_maxIter = NULL,
  ddrtree_ncenter = NULL,
  ddrtree_tol = NULL,
  show_plot = FALSE,
  xlab = NULL,
  ylab = NULL,
  seed = 11,
  verbose = TRUE
)

Arguments

srt

A Seurat object.

assay

Which assay to use. If NULL, the default assay of the Seurat object will be used. When the object also contains ChromatinAssay, the default assay and additional ChromatinAssay will be preprocessed sequentially.

layer

Which layer to use. Default is "counts".

group.by

Name of one or more meta.data columns to group (color) cells by.

expressionFamily

The distribution family to use for modeling gene expression. Default is "negbinomial.size".

features

A character vector of features to use. Defaults to NULL, in which case features were determined by feature_type.

feature_type

The type of features to use in the analysis. Possible values are "HVF" for highly variable features or "Disp" for features selected based on dispersion. Default is "HVF".

disp_filter

A string specifying the filter to use when feature_type is "Disp". Default is "mean_expression >= 0.1 & dispersion_empirical >= 1 * dispersion_fit".

max_components

The maximum number of dimensions to use for dimensionality reduction. Default is 2.

reduction_method

The dimensionality reduction method to use. Possible values are "DDRTree", "ICA", "tSNE", "SimplePPT", "L1-graph", "SGL-tree". Default is "DDRTree".

norm_method

The normalization method to use. Possible values are "log" and "none". Default is "log".

residualModelFormulaStr

A model formula specifying the effects to subtract. Default is NULL.

pseudo_expr

Amount to increase expression values before dimensionality reduction. Default is 1.

root_state

The state to use as the root of the trajectory. If NULL, the R backend prompts for user input, and the C++ backend prompts in interactive sessions after initial ordering. In non-interactive C++ runs, the first cell is used. For backend = "cpp", root_state can also match a C++ trajectory state id after initial ordering, or a group.by label when group.by is provided.

backend

Backend used to compute the trajectory. "r" keeps the original Monocle2 workflow and remains the default. "cpp" keeps Monocle2 dimensional reduction, uses native C++ ordering for DDRTree, and falls back to Monocle2 ordering for other reduction methods.

n_neighbors

Deprecated compatibility parameter for the C++ backend. The current C++ backend reuses Monocle2's learned minimum spanning tree and ignores this value.

ddrtree_maxIter

Optional maximum iteration count passed to DDRTree::DDRTree() when reduction_method = "DDRTree". Lower values can speed up exploratory runs but may reduce agreement with the default Monocle2 trajectory.

ddrtree_ncenter

Optional number of DDRTree centers. This can change trajectory topology and is intended for advanced exploratory use.

ddrtree_tol

Optional convergence tolerance passed to DDRTree::DDRTree().

show_plot

Whether to print diagnostic plots during the run. Default is FALSE.

xlab

The x-axis label of the plot. Default is NULL.

ylab

The y-axis label of the plot. Default is NULL.

seed

Random seed for reproducibility. Default is 11.

verbose

Whether to print the message. Default is TRUE.

Value

A Seurat object with the Monocle2 analysis results added to the @tools slot.

See also

RunSlingshot, CellDimPlot, FeatureDimPlot

Examples

if (interactive()) {
  data(pancreas_sub)
  pancreas_sub <- standard_scop(pancreas_sub)
  pancreas_sub <- RunMonocle2(
    pancreas_sub,
    group.by = "SubCellType"
  )
  names(pancreas_sub@tools$Monocle2)
  trajectory <- pancreas_sub@tools$Monocle2$trajectory

  p1 <- CellDimPlot(
    pancreas_sub,
    group.by = "Monocle2_State",
    reduction = "DDRTree",
    label = TRUE,
    theme_use = "theme_blank"
  )
  p1

  p1 + trajectory

  FeatureDimPlot(
    pancreas_sub,
    features = "Monocle2_Pseudotime",
    reduction = "UMAP",
    theme_use = "theme_blank"
  )

  pancreas_sub <- RunMonocle2(
    pancreas_sub,
    feature_type = "Disp",
    disp_filter = "mean_expression >= 0.01 & dispersion_empirical >= 1 * dispersion_fit"
  )
  trajectory <- pancreas_sub@tools$Monocle2$trajectory
  p2 <- CellDimPlot(
    pancreas_sub,
    group.by = "Monocle2_State",
    reduction = "DDRTree",
    label = TRUE,
    theme_use = "theme_blank"
  )
  p2

  p2 + trajectory

  FeatureDimPlot(
    pancreas_sub,
    features = "Monocle2_Pseudotime",
    reduction = "UMAP",
    theme_use = "theme_blank"
  )
}