Run FWP feature-weight phenotype scoring

Usage

RunFWP(
  object,
  assay = NULL,
  layer = "data",
  features = NULL,
  nfeatures = 2000,
  phenotype.by = NULL,
  positive = NULL,
  weights = NULL,
  score.name = "FWP_Score",
  tool_name = "FWP",
  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.
phenotype.by: Metadata column used to train feature weights.
positive: Positive phenotype label. If NULL, the last ordered label is used.
weights: Optional named vector of precomputed feature weights.
score.name: Metadata column for the FWP score.
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

Wang Q, Song JJ, Zhang F. Feature-weight based measurement of cancerous transcriptome using cohort-wide and sample-specific information. Cellular Oncology, 2024. doi:10.1007/s13402-023-00879-6.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-07-28 03:33:22] Start standard processing workflow...
#> ℹ [2026-07-28 03:33:22] Checking a list of <Seurat>...
#> ! [2026-07-28 03:33:22] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-07-28 03:33:22] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-07-28 03:33:22] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-28 03:33:23] Use the separate HVF from `srt_list`
#> ℹ [2026-07-28 03:33:23] Number of available HVF: 2000
#> ℹ [2026-07-28 03:33:23] Finished check
#> ℹ [2026-07-28 03:33:23] Perform `ScaleData()`
#> ℹ [2026-07-28 03:33:23] Perform pca linear dimension reduction
#> ℹ [2026-07-28 03:33:24] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-07-28 03:33:24] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-28 03:33:24] Reorder clusters...
#> ℹ [2026-07-28 03:33:24] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-28 03:33:24] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-28 03:33:30] Standard processing workflow completed
pancreas_sub$IsEndocrine <- pancreas_sub$CellType == "Endocrine"
pancreas_sub <- RunFWP(
  pancreas_sub,
  phenotype.by = "IsEndocrine",
  nfeatures = 300
)
#> ℹ [2026-07-28 03:33:30] Run FWP scoring with 300 features
FeatureDimPlot(
  pancreas_sub,
  features = "FWP_Score"
) + CellDimPlot(
  pancreas_sub,
  group.by = "IsEndocrine",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)