Assess clustering quality directly on a Seurat object
Usage
RunAssessment(
object,
cluster_col = NULL,
assay = NULL,
use = "pca",
dims = 1:50,
features = NULL,
classifier = "LR",
penalty = "l1",
test_size = 0.5,
n_per_class = 100,
cv = 5,
seed = 1,
n_cores = NULL,
verbose = TRUE,
...
)Arguments
- object
Seurat object
- cluster_col
Column name in meta.data containing cluster labels (default: uses Idents)
- assay
Assay to use (default: DefaultAssay)
- use
Feature space to use: "raw" (normalized data), "pca", or other reduction name
- dims
Dimensions to use for PCA/reduction (default: 1:50)
- features
Optional: specific features to use. If NULL, uses all (for raw) or VariableFeatures
- classifier
Classifier type: "LR", "RF", etc.
- penalty
For LR: "l1" or "l2"
- test_size
Fraction for test set
- n_per_class
Max samples per class
- cv
Cross-validation folds
- seed
Random seed
- n_cores
Number of cores
- verbose
Print progress
- ...
Additional arguments passed to self_projection
Details
This function extracts the appropriate data from the Seurat object and runs self_projection assessment. By default, it uses PCA coordinates if available, otherwise normalized data.
For Seurat V4, uses GetAssayData with slots. For Seurat V5, automatically uses LayerData when appropriate.
Examples
if (FALSE) { # \dontrun{
# Assess default clustering
result <- RunAssessment(seurat_obj)
# Assess specific clustering with PCA
result <- RunAssessment(
seurat_obj,
cluster_col = "seurat_clusters",
use = "pca",
dims = 1:30
)
# Assess with raw expression
result <- RunAssessment(
seurat_obj,
cluster_col = "manual_annotation",
use = "raw"
)
} # }