Finds the transfer anchors
FindTransferAnchors(
reference,
query,
normalization.method = c("LogNormalize", "SCT"),
reference.assay = NULL,
query.assay = NULL,
reduction = "pcaproject",
project.query = FALSE,
features = NULL,
npcs = 30,
l2.norm = TRUE,
dims = 1:30,
k.anchor = 5,
k.filter = 200,
k.score = 30,
max.features = 200,
nn.method = "rann",
eps = 0,
approx.pca = TRUE,
verbose = TRUE
)Seurat object to use as the reference
Seurat object to use as the query
Name of normalization method used: LogNormalize or SCT
Assay to use from reference
Assay to use from query
Dimensional reduction to perform when finding anchors. Options are:
pcaproject: Project the PCA from the reference onto the query. We recommend using PCA when reference and query datasets are from scRNA-seq
cca: Run a CCA on the reference and query
Project the PCA from the query dataset onto the reference. Use only in rare cases where the query dataset has a much larger cell number, but the reference dataset has a unique assay for transfer.
Features to use for dimensional reduction
Number of PCs to compute on reference. If null, then use an existing PCA structure in the reference object
Perform L2 normalization on the cell embeddings after dimensional reduction
Which dimensions to use from the reduction to specify the neighbor search space
How many neighbors (k) to use when picking anchors
How many neighbors (k) to use when filtering anchors
How many neighbors (k) to use when scoring anchors
The maximum number of features to use when specifying the neighborhood search space in the anchor filtering
Method for nearest neighbor finding. Options include: rann, annoy
Error bound on the neighbor finding algorithm (from RANN)
Use truncated singular value decomposition to approximate PCA
Print progress bars and output
Returns an AnchorSet object