Transfer reference labels to query cells — RunLabelTransfer • scop

Standalone label-transfer workflow for query cells using a reference object. The current implementation is optimized for scATAC query objects mapped to a scRNA-seq reference via gene activity.

Usage

RunLabelTransfer(
  srt,
  reference,
  assay = NULL,
  method = c("Seurat", "scOMM"),
  prefix = "ATAC",
  reference_assay = NULL,
  reference_reduction = "pca",
  reference_dims = 1:30,
  reference_label = NULL,
  add_gene_activity = TRUE,
  gene_activity_assay = "ACTIVITY",
  weight_reduction = NULL,
  dims = 2:30,
  features = NULL,
  prediction_prefix = "predicted_",
  k.weight = 100,
  evaluate = FALSE,
  truth_col = NULL,
  tool_name = NULL,
  rare_threshold = 0.05,
  scomm_python = NULL,
  scomm_hidden_nodes = c(128, 64),
  scomm_epochs = 10,
  scomm_batch_size = 32,
  scomm_threshold = 0.5,
  scomm_seed = 11,
  verbose = TRUE
)

Arguments

srt: A Seurat object.
reference: RNA reference Seurat object used for label transfer.
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used. When the object also contains ChromatinAssay, the default assay and additional ChromatinAssay will be preprocessed sequentially.
method: Label-transfer backend. One of "Seurat" or "scOMM".
prefix: Prefix used to resolve ATAC reductions. Default is "ATAC".
reference_assay: Assay used in the reference object.
reference_reduction: Reduction used in the reference object.
reference_dims: Dimensions used from the reference reduction.
reference_label: Metadata column in the reference used as transfer labels.
add_gene_activity: Whether to calculate a gene activity assay for the query.
gene_activity_assay: Name of the gene activity assay used for mapping.
weight_reduction: Reduction in srt used to weight transferred labels. If NULL, an ATAC linear reduction is resolved automatically from ATAC_default_linear_reduction, {prefix}lsi, {prefix}svd, or the current default reduction.
dims: Query reduction dimensions used by TransferData.
features: Features used by FindTransferAnchors. If NULL, reference variable features are used.
prediction_prefix: Prefix added to prediction metadata columns.
k.weight: Number of neighbors used when weighting transfer anchors.
evaluate: Whether to compute mapping metrics against a truth label.
truth_col: Metadata column in srt used as the truth label when evaluate = TRUE.
tool_name: Name used to store detailed results in srt@tools.
rare_threshold: Maximum class proportion used to define rare classes when calculating rare_recall.
scomm_python: Optional Python binary used by the scOMM backend. If NULL, SCOP_SCOMM_PYTHON is consulted and reticulate defaults are used otherwise.
scomm_hidden_nodes, scomm_epochs, scomm_batch_size, scomm_threshold, scomm_seed: Parameters passed to the optional scOMM backend.
verbose: Whether to print the message. Default is TRUE.

Value

A Seurat object with prediction metadata added.

Examples

data("pbmcmultiome_sub", package = "scop")
pbmcmultiome_sub <- standard_scop(
  pbmcmultiome_sub,
  assay = "RNA",
  linear_reduction_dims = 20
)
#> ℹ [2026-05-14 07:30:00] Start standard processing workflow...
#> ℹ [2026-05-14 07:30:01] Checking a list of <Seurat>...
#> ! [2026-05-14 07:30:01] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-05-14 07:30:01] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 07:30:03] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> Warning: pseudoinverse used at -2.3979
#> Warning: neighborhood radius 0.30103
#> Warning: reciprocal condition number  1.2589e-15
#> ℹ [2026-05-14 07:30:03] Use the separate HVF from `srt_list`
#> ℹ [2026-05-14 07:30:03] Number of available HVF: 2000
#> ℹ [2026-05-14 07:30:04] Finished check
#> ℹ [2026-05-14 07:30:04] Perform `Seurat::ScaleData()`
#> ℹ [2026-05-14 07:30:04] Perform pca linear dimension reduction
#> ℹ [2026-05-14 07:30:04] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-05-14 07:30:04] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-05-14 07:30:05] Reorder clusters...
#> ℹ [2026-05-14 07:30:05] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-05-14 07:30:05] Perform umap nonlinear dimension reduction
#> ℹ [2026-05-14 07:30:05] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-05-14 07:30:11] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-05-14 07:30:17] Standard processing workflow completed
reference <- subset(pbmcmultiome_sub, cells = colnames(pbmcmultiome_sub)[1:250])
query <- subset(pbmcmultiome_sub, cells = colnames(pbmcmultiome_sub)[251:350])
query <- standard_scop(
  query,
  assay = "peaks",
  normalization_method = "TFIDF",
  linear_reduction_dims = 20
)
#> ℹ [2026-05-14 07:30:17] Start standard processing workflow...
#> ℹ [2026-05-14 07:30:17] Checking a list of <Seurat>...
#> ! [2026-05-14 07:30:17] Data 1/1 of the `srt_list` is "raw_counts"
#> ℹ [2026-05-14 07:30:17] Perform `RunTFIDF()` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 07:30:17] Perform `FindTopFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 07:30:17] Use the separate HVF from `srt_list`
#> ℹ [2026-05-14 07:30:17] Number of available HVF: 11426
#> ℹ [2026-05-14 07:30:17] Finished check
#> ℹ [2026-05-14 07:30:17] `normalization_method` is TFIDF. Use lsi workflow
#> ℹ [2026-05-14 07:30:17] Perform svd linear dimension reduction
#> Running SVD
#> Scaling cell embeddings
#> ℹ [2026-05-14 07:30:18] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-05-14 07:30:18] Reorder clusters...
#> ℹ [2026-05-14 07:30:18] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-05-14 07:30:18] Perform umap nonlinear dimension reduction
#> ℹ [2026-05-14 07:30:18] Perform umap nonlinear dimension reduction using ATACsvd (2:30)
#> ℹ [2026-05-14 07:30:24] Perform umap nonlinear dimension reduction using ATACsvd (2:30)
#> ✔ [2026-05-14 07:30:29] Standard processing workflow completed
query <- RunLabelTransfer(
  srt = query,
  reference = reference,
  assay = "peaks",
  reference_assay = "RNA",
  reference_reduction = "Standardpca",
  reference_label = "CellType",
  reference_dims = 1:10,
  dims = 2:10
)
#> ℹ [2026-05-14 07:30:29] Use "ATAClsi" as the ATAC weight reduction
#> ℹ [2026-05-14 07:30:29] Calculating gene activity assay...
#> Extracting gene coordinates
#> Error in atac_add_activity(srt = srt, assay = assay, gene_activity_assay = gene_activity_assay,     verbose = verbose): Unable to calculate gene activity assay: "No fragment information found
#> for requested assay". Please provide a valid ATAC annotation.