Run CellRank analysis

CellRank is a toolkit for studying cellular dynamics using Markov state modeling.

Usage

RunCellRank(
  srt = NULL,
  assay_x = "RNA",
  layer_x = "counts",
  assay_y = c("spliced", "unspliced"),
  layer_y = "counts",
  adata = NULL,
  group.by = NULL,
  cores = 1,
  linear_reduction = NULL,
  nonlinear_reduction = NULL,
  basis = NULL,
  mode = "stochastic",
  fitting_by = "stochastic",
  magic_impute = FALSE,
  knn = 5,
  t = 2,
  min_shared_counts = 30,
  n_pcs = 30,
  n_neighbors = 30,
  stream_smooth = NULL,
  stream_density = 2,
  arrow_size = 5,
  arrow_length = 5,
  arrow_density = 0.5,
  calculate_velocity_genes = FALSE,
  denoise = FALSE,
  kinetics = FALSE,
  kernel_type = c("velocity", "pseudotime", "cytotrace", "wot"),
  time_key = "dpt_pseudotime",
  time_field = "Time",
  growth_iters = 3L,
  tmap_out = "tmaps/tmap_out",
  recalculate = FALSE,
  estimator_type = c("GPCCA", "CFLARE"),
  use_connectivity_kernel = TRUE,
  velocity_weight = 0.8,
  connectivity_weight = 0.2,
  softmax_scale = 4,
  n_macrostates = NULL,
  schur_method = c("brandts", "krylov"),
  schur_n_components = NULL,
  n_cells_terminal = 10,
  terminal_states = NULL,
  terminal_state_agg = c("top_n", "union"),
  driver_lineages = NULL,
  compute_lineage_drivers = TRUE,
  backward = FALSE,
  backend = c("python", "cpp"),
  allow_approximate = FALSE,
  max_dense_gib = 8,
  show_plot = TRUE,
  save_plot = FALSE,
  plot_format = c("pdf", "png", "svg"),
  plot_dpi = 300,
  plot_prefix = "cellrank",
  legend.position = "on data",
  palette = "Chinese",
  palcolor = NULL,
  dirpath = "./cellrank",
  return_seurat = !is.null(srt),
  envname = NULL,
  conda = "auto",
  recompute_neighbors = TRUE,
  verbose = TRUE
)

Arguments

srt: A Seurat object. If provided, adata will be ignored.
assay_x: Assay to convert as the main data matrix in the anndata object.
layer_x: Layer name for assay_x in the Seurat object.
assay_y: Assays to convert as layers in the anndata object.
layer_y: Layer names for the assay_y in the Seurat object.
adata: An anndata object.
group.by: Metadata column(s) used to color cells.
cores: The number of cores to use for cellrank.
linear_reduction: Linear reduction ("pca", "svd", "ica", "nmf", "mds", "glmpca"). linear_reduction_dims_use = NULL uses estimated dimensions, else the first 50.
nonlinear_reduction: Nonlinear reduction ("umap", "umap-naive", "tsne", "dm", "phate", "pacmap", "trimap", "largevis", "fr").
basis: The basis to use for reduction, e.g., "UMAP".
mode: Velocity estimation models to use. Can be "deterministic", "stochastic", or "dynamical". If the corresponding velocity reduction (e.g. stochastic_umap) is not found, falls back to the scVelo convention (e.g. velocity_umap), which is what an object converted from an AnnData (via adata_to_srt) contains.
fitting_by: Method used to fit gene velocities for dynamical modeling.
magic_impute: Flag indicating whether to perform magic imputation.
knn: The number of nearest neighbors for magic.MAGIC.
t: Power to which the diffusion operator is powered for magic.MAGIC.
min_shared_counts: Minimum number of counts (both unspliced and spliced) required for a gene.
n_pcs: Number of principal components to use for linear reduction.
n_neighbors: Number of neighbors to use for constructing the KNN graph.
stream_smooth: Multiplication factor for scale in Gaussian kernel around grid point.
stream_density: Controls the closeness of streamlines. When density = 2 (default), the domain is divided into a 60x60 grid, whereas density linearly scales this grid. Each cell in the grid can have, at most, one traversing streamline.
arrow_size: Size of arrows.
arrow_length: Length of arrows.
arrow_density: Amount of velocities to show.
calculate_velocity_genes: Boolean flag indicating whether to calculate velocity genes.
denoise: Boolean flag indicating whether to denoise.
kinetics: Boolean flag indicating whether to estimate RNA kinetics.
kernel_type: Type of kernel to use: "velocity" (default, requires spliced/unspliced), "pseudotime" (requires pre-computed pseudotime or auto-computes DPT), "cytotrace" (auto-computes CytoTRACE score, suitable for RNA-only data), or "wot" (uses Waddington-OT transport maps through CellRank's RealTimeKernel).
time_key: Key in metadata for pseudotime. Used when kernel_type = "pseudotime". If the key doesn't exist, DPT pseudotime will be computed automatically.
time_field: Key in metadata for experimental time. Used when kernel_type = "wot".
growth_iters: Number of growth iterations passed to wot.ot.OTModel.
tmap_out: Directory used to store or read Waddington-OT transport maps.
recalculate: Whether to recompute Waddington-OT transport maps even when tmap_out already exists.
estimator_type: Type of estimator to use: "GPCCA" (default) or "CFLARE". GPCCA provides coarse-grained analysis and Schur decomposition.
use_connectivity_kernel: Whether to combine the main kernel with ConnectivityKernel.
velocity_weight: Weight for the VelocityKernel when combining with ConnectivityKernel.
connectivity_weight: Weight for the ConnectivityKernel when combining with VelocityKernel. Weights are automatically normalized to sum to 1.0.
softmax_scale: Scaling parameter for softmax transformation of velocity kernel.
n_macrostates: Number of macrostates to compute. If NULL (default), automatically determined based on eigenvalue spectrum.
schur_method: Method for Schur decomposition: "brandts" (default) or "krylov". Only used for the GPCCA estimator. The "krylov" method requires the optional Python packages petsc4py and slepc4py.
schur_n_components: Number of Schur components. If NULL, retain the existing size heuristic.
n_cells_terminal: Minimum number of cells required for a state to be considered terminal.
terminal_states: Optional macrostate names to set as terminal states. Names are validated against the computed macrostates before fate estimation.
terminal_state_agg: Aggregation policy for combined terminal states.
driver_lineages: Optional lineage names for driver-gene computation.
compute_lineage_drivers: Whether to compute and store lineage drivers.
backward: Whether to compute backward transitions.
backend: Backend for computation: "python" (default) or "cpp". The C++ path is an explicitly opted-in approximation and does not reproduce the complete CellRank estimator.
allow_approximate: Whether to allow the approximate C++ path. This must be TRUE when backend = "cpp".
max_dense_gib: Maximum estimated GiB allowed for the dense cell-by-cell working matrices used by the C++ path.
show_plot: Whether to show the plot.
save_plot: Whether to save plots to files.
plot_format: Format for saved plots: "png" (default), "pdf", or "svg".
plot_dpi: Resolution (DPI) for saved plots.
plot_prefix: Prefix for saved plot filenames.
legend.position: Position of legend in plots. Can be "on data", "right margin", "bottom right", etc.
palette, palcolor: Palette name (thisplot::show_palettes) or custom colors.
dirpath: The directory to save the plots.
envname: Optional Python environment name. NULL uses the current SCOP environment selection.
conda: Conda-compatible executable used by PrepareEnv.
recompute_neighbors: Whether to rebuild Scanpy neighbors before a pseudotime kernel. If FALSE, the existing graph must be complete, connected, and aligned to the current cell order.
return_seurat: Whether to return a Seurat object instead of an anndata object.
verbose: Whether to print the message. Default is TRUE.

Value

Returns a Seurat object if return_seurat = TRUE or an anndata object with CellRank results stored in obsm, obs, and varm slots. The estimator and kernel objects are stored in srt@misc$cellrank.

Examples

if (FALSE) { # \dontrun{
data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
pancreas_sub <- RunCellRank(
  srt = pancreas_sub,
  group.by = "SubCellType"
)

CellDimPlot(
  pancreas_sub,
  group.by = "term_states_fwd",
  reduction = "umap",
  label = TRUE
)

FeatureDimPlot(
  pancreas_sub,
  features = "latent_time",
  reduction = "umap"
)

FeatureDimPlot(
  pancreas_sub,
  features = c("stochastic_confidence", "stochastic_length"),
  reduction = "umap"
)

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  lineages = "cellrank_pseudotime",
  lineages_span = 0.1,
  lineages_trim = c(0.05, 0.95)
)

DynamicPlot(
  pancreas_sub,
  lineages = "cellrank_pseudotime",
  features = c("Arxes1", "Ncoa2"),
  group.by = "SubCellType"
)
} # }

Usage

Arguments

Value

See also

Examples