Run CytoTRACE 2

Usage

RunCytoTRACE(object, ...)

# S3 method for class 'Seurat'
RunCytoTRACE(
  object,
  assay = NULL,
  layer = c("counts", "data"),
  species = c("Homo_sapiens", "Mus_musculus"),
  batch_size = 10000,
  smooth_batch_size = 1000,
  parallelize_models = TRUE,
  parallelize_smoothing = TRUE,
  cores = 1,
  seed = 11,
  verbose = TRUE,
  ...
)

# Default S3 method
RunCytoTRACE(
  object,
  species = c("Homo_sapiens", "Mus_musculus"),
  batch_size = 10000,
  smooth_batch_size = 1000,
  parallelize_models = TRUE,
  parallelize_smoothing = TRUE,
  cores = 1,
  seed = 11,
  verbose = TRUE,
  ...
)

Arguments

object: An object. This can be a Seurat object or a matrix-like object (genes as rows, cells as columns).
...: Additional arguments to be passed to CytoTRACE2::cytotrace2.
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used.
layer: Which layer to use. Default is "counts".
species: The species of the input data. Currently supported values are "human" and "mouse". Default is "human".
batch_size: The number of cells to process at once, including subsampling for KNN smoothing. No subsampling if NULL. Default is 10000 (recommended for input data size > 10K cells).
smooth_batch_size: The number of cells to subsample further within the batch_size for the smoothing by diffusion step of the pipeline. No subsampling if NULL. Default is 1000 (recommended for input data size > 1K cells).
parallelize_models: Whether to run the prediction function on models in parallel on multiple threads. Default is TRUE.
parallelize_smoothing: Whether to run the smoothing function on subsamples in parallel on multiple threads. Default is TRUE.
cores: The number of cores to use for parallelization with foreach::foreach. Default is 1.
seed: Random seed for reproducibility. Default is 11.
verbose: Whether to print the message. Default is TRUE.

Value

When the input is a Seurat object, the function returns a Seurat object with the following metadata columns added:

CytoTRACE2_Score: The final predicted cellular potency score (0-1)
CytoTRACE2_Potency: The final predicted cellular potency category (Differentiated, Unipotent, Oligopotent, Multipotent, Pluripotent, Totipotent)
CytoTRACE2_Relative: The predicted relative order (normalized to 0-1)
preKNN_CytoTRACE2_Score: The potency score before KNN smoothing
preKNN_CytoTRACE2_Potency: The potency category before KNN smoothing

When the input is a matrix or data.frame, the function returns a data.frame with the same columns as above, with cell IDs as row names.

Examples

if (thisplot::check_ci_env()) {
  data(pancreas_sub)
  pancreas_sub <- standard_scop(pancreas_sub)
  pancreas_sub <- RunCytoTRACE(
    pancreas_sub,
    species = "Mus_musculus"
  )
  CytoTRACEPlot(
    pancreas_sub,
    group.by = "CellType"
  )
}
#> ℹ [2026-01-27 08:02:57] Start standard scop workflow...
#> ℹ [2026-01-27 08:02:58] Checking a list of <Seurat>...
#> ! [2026-01-27 08:02:58] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:02:58] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:03:00] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:03:00] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:03:00] Number of available HVF: 2000
#> ℹ [2026-01-27 08:03:00] Finished check
#> ℹ [2026-01-27 08:03:01] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 08:03:01] Perform pca linear dimension reduction
#> ℹ [2026-01-27 08:03:02] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 08:03:02] Reorder clusters...
#> ℹ [2026-01-27 08:03:02] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 08:03:02] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 08:03:06] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 08:03:11] Run scop standard workflow completed
#> ◌ [2026-01-27 08:03:11] Running CytoTRACE2
#> cytotrace2: Started loading data
#> Dataset contains 15998 genes and 1000 cells.
#> The number of cells in your dataset is less than 1000. Fast mode has been disabled.
#> The passed subsample size is greater than the number of cells in dataset.
#> Now setting subsample size to 1000
#> cytotrace2: Running on 1 subsample(s) approximately of length 1000
#> cytotrace2: Started running on subsample(s). This will take a few minutes.
#> cytotrace2: Started preprocessing.
#> 12486 input genes mapped to model genes.
#> cytotrace2: Started prediction.
#> This section will run using  1 / 4 core(s).
#> cytotrace2: Started postprocessing.
#> cytotrace2: Running with slow mode (subsamples are processed sequentially)
#> Number of cores for KNN: 1
#> cytotrace2: Finished
#> ✔ [2026-01-27 08:04:15] CytoTRACE2 computed successfully