Run FitDevo developmental potential scoring

Usage

RunFitDevo(
  object,
  assay = NULL,
  layer = "data",
  features = NULL,
  nfeatures = 2000,
  reference.by = NULL,
  score.name = "FitDevo_Score",
  relative.name = "FitDevo_Relative",
  tool_name = "FitDevo",
  verbose = TRUE
)

Arguments

object: A Seurat object or expression matrix with genes in rows and cells in columns.
assay: Assay used for Seurat input.
layer: Layer used for Seurat input.
features: Features used for scoring. If NULL, the most variable genes are selected.
nfeatures: Number of variable genes selected when features = NULL.
reference.by: Optional metadata column containing ordered development labels. Numeric values are used directly; factors use their level order.
score.name: Metadata column for the developmental potential score.
relative.name: Metadata column for the relative rank.
tool_name: Name used in srt@tools.
verbose: Whether to print progress messages.

Value

A modified Seurat object or a result bundle for matrix input.

References

Zhang F, Yang C, Wang Y, Jiao H, Wang Z, Shen J, Li L. FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight. Briefings in Bioinformatics, 2022. doi:10.1093/bib/bbac293.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-07-28 03:33:31] Start standard processing workflow...
#> ℹ [2026-07-28 03:33:31] Checking a list of <Seurat>...
#> ! [2026-07-28 03:33:31] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-07-28 03:33:31] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-07-28 03:33:31] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-28 03:33:31] Use the separate HVF from `srt_list`
#> ℹ [2026-07-28 03:33:32] Number of available HVF: 2000
#> ℹ [2026-07-28 03:33:32] Finished check
#> ℹ [2026-07-28 03:33:32] Perform `ScaleData()`
#> ℹ [2026-07-28 03:33:32] Perform pca linear dimension reduction
#> ℹ [2026-07-28 03:33:32] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-07-28 03:33:33] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-28 03:33:33] Reorder clusters...
#> ℹ [2026-07-28 03:33:33] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-28 03:33:33] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-28 03:33:38] Standard processing workflow completed
pancreas_sub <- RunFitDevo(
  pancreas_sub,
  nfeatures = 300
)
#> ℹ [2026-07-28 03:33:38] Run FitDevo scoring with 300 features and 1000 cells
FeatureDimPlot(pancreas_sub, features = "FitDevo_Score")

FitDevoPlot(
  pancreas_sub,
  group.by = "SubCellType",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)