FindTransferAnchors: Find transfer anchors
In atakanekiz/Seurat3.0: Tools for Single Cell Genomics

Finds the transfer anchors

FindTransferAnchors(reference, query, 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, eps = 0, approx.pca = TRUE,
  verbose = TRUE)

`reference`	Seurat object to use as the reference
`query`	Seurat object to use as the query
`reference.assay`	Assay to use from reference
`query.assay`	Assay to use from query
`reduction`	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.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`	Features to use for dimensional reduction
`npcs`	Number of PCs to compute on reference. If null, then use an existing PCA structure in the reference object
`l2.norm`	Perform L2 normalization on the cell embeddings after dimensional reduction
`dims`	Which dimensions to use from the reduction to specify the neighbor search space
`k.anchor`	How many neighbors (k) to use when picking anchors
`k.filter`	How many neighbors (k) to use when filtering anchors
`k.score`	How many neighbors (k) to use when scoring anchors
`max.features`	The maximum number of features to use when specifying the neighborhood search space in the anchor filtering
`eps`	Error bound on the neighbor finding algorithm (from RANN)
`approx.pca`	Use truncated singular value decomposition to approximate PCA
`verbose`	Print progress bars and output