Run Monocle2 analysis — RunMonocle2 • SCP

Runs the Monocle2 algorithm on a Seurat object.

Usage

RunMonocle2(
  srt,
  assay = NULL,
  slot = "counts",
  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,
  seed = 11
)

Arguments

srt: A Seurat object.
assay: The name of the assay in the Seurat object to use for analysis. Defaults to NULL, in which case the default assay of the object is used.
slot: The slot in the Seurat object to use for analysis. Default is "counts".
expressionFamily: The distribution family to use for modeling gene expression. Default is "negbinomial.size".
features: A vector of gene names or indices specifying the features to use in the analysis. 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" and "UMAP". 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, will prompt for user input.
seed: An integer specifying the random seed to use. Default is 11.

Examples

if (interactive()) {
  data("pancreas_sub")
  pancreas_sub <- RunMonocle2(srt = pancreas_sub)
  names(pancreas_sub@tools$Monocle2)
  trajectory <- pancreas_sub@tools$Monocle2$trajectory

  CellDimPlot(pancreas_sub, group.by = "Monocle2_State", reduction = "DDRTree", label = TRUE, theme_use = "theme_blank") + trajectory
  CellDimPlot(pancreas_sub, group.by = "Monocle2_State", reduction = "UMAP", label = TRUE, theme_use = "theme_blank")
  FeatureDimPlot(pancreas_sub, features = "Monocle2_Pseudotime", reduction = "UMAP", theme_use = "theme_blank")

  pancreas_sub <- RunMonocle2(
    srt = pancreas_sub,
    feature_type = "Disp", disp_filter = "mean_expression >= 0.01 & dispersion_empirical >= 1 * dispersion_fit"
  )
  trajectory <- pancreas_sub@tools$Monocle2$trajectory
  CellDimPlot(pancreas_sub, group.by = "Monocle2_State", reduction = "DDRTree", label = TRUE, theme_use = "theme_blank") + trajectory
  CellDimPlot(pancreas_sub, group.by = "Monocle2_State", reduction = "UMAP", label = TRUE, theme_use = "theme_blank")
  FeatureDimPlot(pancreas_sub, features = "Monocle2_Pseudotime", reduction = "UMAP", theme_use = "theme_blank")
}