Co-embed reference and query cells — RunCoEmbedding • scop

Impute reference expression into query cells with TransferData, merge reference and query cells, and compute PCA/UMAP on the shared expression space. The current implementation mirrors the Seurat ATAC integration vignette for ATAC query and RNA reference.

Usage

RunCoEmbedding(
  srt,
  reference,
  assay = NULL,
  reference_assay = NULL,
  reference_reduction = "pca",
  reference_dims = 1:30,
  gene_activity_assay = "ACTIVITY",
  weight_reduction = NULL,
  dims = 2:30,
  genes_use = NULL,
  imputed_assay = "RNA",
  modality_col = "modality",
  coembed_prefix = "CoEmbed",
  npcs = 30,
  umap_dims = 1:30,
  k.weight = 100,
  verbose = TRUE,
  seed = 11
)

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.
reference_assay: Assay used in the reference object.
reference_reduction: Reduction used in the reference object.
reference_dims: Dimensions used from the reference reduction.
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.
genes_use: Genes/features used for expression imputation and co-embedding. If NULL, reference variable features are used.
imputed_assay: Assay name for imputed RNA expression in ATAC cells.
modality_col: Metadata column storing RNA/ATAC origin after merge.
coembed_prefix: Prefix for co-embedding reductions.
npcs: Number of PCs to compute.
umap_dims: PC dimensions used for UMAP.
k.weight: Number of neighbors used when weighting transfer anchors.
verbose: Whether to print the message. Default is TRUE.
seed: Random seed used for UMAP.

Value

A merged Seurat object containing RNA reference and ATAC query cells.

Examples

data(pbmcmultiome_sub)
pbmcmultiome_sub <- standard_scop(
  pbmcmultiome_sub,
  assay = c("RNA", "peaks"),
  linear_reduction_dims = 20
)
#> ℹ [2026-07-02 09:31:04] Start standard processing workflow...
#> ℹ [2026-07-02 09:31:04] Auto preprocess assays: "RNA" and "peaks"
#> ℹ [2026-07-02 09:31:04] Start standard processing workflow...
#> ℹ [2026-07-02 09:31:04] Checking a list of <Seurat>...
#> ! [2026-07-02 09:31:04] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-07-02 09:31:04] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-07-02 09:31:04] Perform `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
#> Warning: pseudoinverse used at -2.3979
#> Warning: neighborhood radius 0.30103
#> Warning: reciprocal condition number  1.2589e-15
#> ℹ [2026-07-02 09:31:04] Use the separate HVF from `srt_list`
#> ℹ [2026-07-02 09:31:04] Number of available HVF: 2000
#> ℹ [2026-07-02 09:31:05] Finished check
#> ℹ [2026-07-02 09:31:05] Perform `ScaleData()`
#> ℹ [2026-07-02 09:31:05] Perform pca linear dimension reduction
#> ℹ [2026-07-02 09:31:05] Use stored estimated dimensions 1:9 for RNApca
#> ℹ [2026-07-02 09:31:05] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-02 09:31:05] Reorder clusters...
#> ℹ [2026-07-02 09:31:05] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-02 09:31:06] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-02 09:31:11] Standard processing workflow completed
#> ℹ [2026-07-02 09:31:11] Start standard processing workflow...
#> ℹ [2026-07-02 09:31:11] Checking a list of <Seurat>...
#> ! [2026-07-02 09:31:12] Data 1/1 of the `srt_list` is "raw_counts"
#> ℹ [2026-07-02 09:31:12] Perform `RunTFIDF()` on 1/1 of `srt_list`...
#> ℹ [2026-07-02 09:31:12] Perform `FindTopFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-02 09:31:12] Use the separate HVF from `srt_list`
#> ℹ [2026-07-02 09:31:12] Number of available HVF: 11413
#> ℹ [2026-07-02 09:31:12] Finished check
#> ℹ [2026-07-02 09:31:12] `normalization_method` is TFIDF. Use lsi workflow
#> ℹ [2026-07-02 09:31:12] Perform svd linear dimension reduction
#> Running SVD
#> Scaling cell embeddings
#> ℹ [2026-07-02 09:31:13] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-02 09:31:13] Reorder clusters...
#> ℹ [2026-07-02 09:31:13] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-02 09:31:13] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-02 09:31:19] Standard processing workflow completed
coembed <- RunCoEmbedding(
  srt = pbmcmultiome_sub,
  reference = pbmcmultiome_sub,
  assay = "peaks",
  reference_assay = "RNA",
  gene_activity_assay = "RNA",
  reference_reduction = "RNApca",
  reference_dims = 1:10,
  dims = 2:10,
  umap_dims = 1:10
)
#> ℹ [2026-07-02 09:31:19] Use "ATAClsi" as the ATAC weight reduction
#> ℹ [2026-07-02 09:31:19] Finding RNA-to-ATAC transfer anchors...
#> Warning: Different cells and/or features from existing assay RNA
#> Warning: No layers found matching search pattern provided
#> ℹ [2026-07-02 09:31:23] Imputing RNA expression into ATAC cells...
#> Warning: No layers found matching search pattern provided
#> Warning: Some cell names are duplicated across objects provided. Renaming to enforce unique cell names.

CellDimPlot(
  coembed,
  group.by = c("modality", "CellType"),
  xlab = "CoEmbedUMAP_1",
  ylab = "CoEmbedUMAP_2"
)