Single-cell reference mapping with Symphony method

Usage

RunSymphonyMap(
  srt_query,
  srt_ref,
  query_assay = NULL,
  ref_assay = srt_ref[[ref_pca]]@assay.used,
  ref_pca = NULL,
  ref_harmony = NULL,
  ref_umap = NULL,
  ref_group = NULL,
  projection_method = c("model", "knn"),
  nn_method = NULL,
  k = 30,
  distance_metric = "cosine",
  vote_fun = "mean"
)

Arguments

srt_query: An object of class Seurat to be annotated with cell types.
srt_ref: A Seurat object or count matrix representing the reference object. If provided, the similarities will be calculated between cells from the query and reference objects. If not provided, the similarities will be calculated within the query object.
query_assay: The assay to use for the query object. If not provided, the default assay of the query object will be used.
ref_assay: The assay to use for the reference object. If not provided, the default assay of the reference object will be used.
ref_pca: The PCA reduction in the reference object to use for calculating the distance metric.
ref_harmony: The Harmony reduction in the reference object to use for calculating the distance metric.
ref_umap: A character string specifying the name of the UMAP reduction in the reference object. If not provided, the first UMAP reduction found in the reference object will be used.
ref_group: The grouping variable in the reference object. This variable will be used to group cells in the heatmap columns. If not provided, all cells will be treated as one group.
projection_method: A character string specifying the projection method to use. Options are "model" and "knn". If "model" is selected, the function will try to use a pre-trained UMAP model in the reference object for projection. If "knn" is selected, the function will directly find the nearest neighbors using the distance metric.
nn_method: A character string specifying the nearest neighbor search method to use. Options are "raw", "annoy", and "rann". If "raw" is selected, the function will use the brute-force method to find the nearest neighbors. If "annoy" is selected, the function will use the Annoy library for approximate nearest neighbor search. If "rann" is selected, the function will use the RANN library for approximate nearest neighbor search. If not provided, the function will choose the search method based on the size of the query and reference datasets.
k: A number of nearest neighbors to find for each cell in the query object.
distance_metric: The distance metric to use for calculating the pairwise distances between cells. Options include: "pearson", "spearman", "cosine", "correlation", "jaccard", "ejaccard", "dice", "edice", "hamman", "simple matching", and "faith". Additional distance metrics can also be used, such as "euclidean", "manhattan", "hamming", etc.
vote_fun: A character string specifying the function to be used for aggregating the nearest neighbors in the reference object. Options are "mean", "median", "sum", "min", "max", "sd", "var", etc. If not provided, the default is "mean".

Examples

data(panc8_sub)
panc8_sub <- standard_scop(panc8_sub)
#> ℹ [2026-03-20 09:49:21] Start standard scop workflow...
#> ℹ [2026-03-20 09:49:21] Checking a list of <Seurat>...
#> ! [2026-03-20 09:49:21] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-03-20 09:49:21] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-03-20 09:49:24] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-03-20 09:49:24] Use the separate HVF from `srt_list`
#> ℹ [2026-03-20 09:49:24] Number of available HVF: 2000
#> ℹ [2026-03-20 09:49:25] Finished check
#> ℹ [2026-03-20 09:49:25] Perform `Seurat::ScaleData()`
#> ℹ [2026-03-20 09:49:25] Perform pca linear dimension reduction
#> ℹ [2026-03-20 09:49:27] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-03-20 09:49:27] Reorder clusters...
#> ℹ [2026-03-20 09:49:27] Perform umap nonlinear dimension reduction
#> ℹ [2026-03-20 09:49:27] Perform umap nonlinear dimension reduction using Standardpca (1:50)
#> ℹ [2026-03-20 09:49:32] Perform umap nonlinear dimension reduction using Standardpca (1:50)
#> ✔ [2026-03-20 09:49:37] Run scop standard workflow completed
srt_ref <- panc8_sub[, panc8_sub$tech != "fluidigmc1"]
srt_query <- panc8_sub[, panc8_sub$tech == "fluidigmc1"]
srt_ref <- integration_scop(
  srt_ref,
  batch = "tech",
  integration_method = "Harmony"
)
#> ◌ [2026-03-20 09:49:38] Run Harmony integration...
#> ℹ [2026-03-20 09:49:38] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-03-20 09:49:39] Checking a list of <Seurat>...
#> ℹ [2026-03-20 09:49:39] Data 1/4 of the `srt_list` has been log-normalized
#> ℹ [2026-03-20 09:49:39] Perform `Seurat::FindVariableFeatures()` on 1/4 of `srt_list`...
#> ℹ [2026-03-20 09:49:40] Data 2/4 of the `srt_list` has been log-normalized
#> ℹ [2026-03-20 09:49:40] Perform `Seurat::FindVariableFeatures()` on 2/4 of `srt_list`...
#> ℹ [2026-03-20 09:49:40] Data 3/4 of the `srt_list` has been log-normalized
#> ℹ [2026-03-20 09:49:40] Perform `Seurat::FindVariableFeatures()` on 3/4 of `srt_list`...
#> ℹ [2026-03-20 09:49:41] Data 4/4 of the `srt_list` has been log-normalized
#> ℹ [2026-03-20 09:49:41] Perform `Seurat::FindVariableFeatures()` on 4/4 of `srt_list`...
#> ℹ [2026-03-20 09:49:41] Use the separate HVF from `srt_list`
#> ℹ [2026-03-20 09:49:41] Number of available HVF: 2000
#> ℹ [2026-03-20 09:49:42] Finished check
#> ℹ [2026-03-20 09:49:45] Perform `Seurat::ScaleData()`
#> ℹ [2026-03-20 09:49:46] Perform linear dimension reduction("pca")
#> ℹ [2026-03-20 09:49:47] Perform Harmony integration
#> ℹ [2026-03-20 09:49:47] Using "CSSpca" (1:10) as input
#> ℹ [2026-03-20 09:49:48] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-03-20 09:49:48] Reorder clusters...
#> ℹ [2026-03-20 09:49:48] Perform umap nonlinear dimension reduction using Harmony (1:10)
#> ℹ [2026-03-20 09:49:53] Perform umap nonlinear dimension reduction using Harmony (1:10)
#> ✔ [2026-03-20 09:50:00] Run Harmony integration done
CellDimPlot(srt_ref, group.by = c("celltype", "tech"))


# Projection
srt_query <- RunSymphonyMap(
  srt_query = srt_query,
  srt_ref = srt_ref,
  ref_pca = "Harmonypca",
  ref_harmony = "Harmony",
  ref_umap = "HarmonyUMAP2D"
)
#> ℹ [2026-03-20 09:50:29] Data type is log-normalized
#> ℹ [2026-03-20 09:50:29] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-03-20 09:50:29] Data type is log-normalized
#> ℹ [2026-03-20 09:50:29] Detected `srt_ref` data type: "log_normalized_counts"
#> ℹ [2026-03-20 09:50:29] Build reference
#> ℹ [2026-03-20 09:50:29] Saved embeddings
#> ℹ [2026-03-20 09:50:29] Saved soft cluster assignments
#> ℹ [2026-03-20 09:50:30] Saved variable gene information for 2000 genes
#> ℹ [2026-03-20 09:50:30] Saved PCA loadings
#> ℹ [2026-03-20 09:50:30] Saved metadata
#> ℹ [2026-03-20 09:50:30] Calculate final L2 normalized reference centroids (Y_cos)
#> ℹ [2026-03-20 09:50:30] Calculate reference compression terms (Nr and C)
#> ℹ [2026-03-20 09:50:30] Run mapQuery
#> ℹ [2026-03-20 09:50:30] Scaling and synchronizing query gene expression
#> ℹ [2026-03-20 09:50:30] Found 2000 reference variable genes in query object
#> ℹ [2026-03-20 09:50:30] Project query cells using reference gene loadings
#> ℹ [2026-03-20 09:50:30] Clustering query cells to reference centroids
#> ℹ [2026-03-20 09:50:30] Correcting query batch effects
#> ℹ [2026-03-20 09:50:30] Run UMAP projection
#> ℹ [2026-03-20 09:50:30] Use the reduction to calculate distance metric
#> ℹ [2026-03-20 09:50:30] Use raw method to find neighbors
#> ℹ [2026-03-20 09:50:30] Running UMAP projection
#> ℹ [2026-03-20 09:50:30] Run SymphonyMap finished
ProjectionPlot(
  srt_query = srt_query,
  srt_ref = srt_ref,
  query_group = "celltype",
  ref_group = "celltype"
)
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.