Find transfer anchors
Find a set of anchors between a reference and query object. These
anchors can later be used to transfer data from the reference to
query object using the TransferData object.
FindTransferAnchors( reference, query, normalization.method = "LogNormalize", recompute.residuals = TRUE, reference.assay = NULL, reference.neighbors = NULL, query.assay = NULL, reduction = "pcaproject", reference.reduction = NULL, 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 = "annoy", n.trees = 50, eps = 0, approx.pca = TRUE, mapping.score.k = NULL, verbose = TRUE )
reference |
|
query |
|
normalization.method |
Name of normalization method used: LogNormalize or SCT. |
recompute.residuals |
If using SCT as a normalization method, compute query Pearson residuals using the reference SCT model parameters. |
reference.assay |
Name of the Assay to use from reference |
reference.neighbors |
Name of the Neighbor to use from the reference. Optionally enables reuse of precomputed neighbors. |
query.assay |
Name of the Assay to use from query |
reduction |
Dimensional reduction to perform when finding anchors. Options are:
|
reference.reduction |
Name of dimensional reduction to use from the reference if running the pcaproject workflow. Optionally enables reuse of precomputed reference dimensional reduction. If NULL (default), use a PCA computed on the reference object. |
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. In this case, the default features will be set to the variable features of the query object that are alos present in the reference. |
features |
Features to use for dimensional reduction. If not specified, set as variable features of the reference object which are also present in the query. |
npcs |
Number of PCs to compute on reference if reference.reduction is not provided. |
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 finding anchors |
k.filter |
How many neighbors (k) to use when filtering anchors. Set to NA to turn off filtering. |
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 |
nn.method |
Method for nearest neighbor finding. Options include: rann, annoy |
n.trees |
More trees gives higher precision when using annoy approximate nearest neighbor search |
eps |
Error bound on the neighbor finding algorithm (from
|
approx.pca |
Use truncated singular value decomposition to approximate PCA |
mapping.score.k |
Compute and store nearest k query neighbors in the AnchorSet object that is returned. You can optionally set this if you plan on computing the mapping score and want to enable reuse of some downstream neighbor calculations to make the mapping score function more efficient. |
verbose |
Print progress bars and output |
The main steps of this procedure are outlined below. For a more detailed description of the methodology, please see Stuart, Butler, et al Cell 2019. doi: 10.1016/j.cell.2019.05.031; doi: 10.1101/460147
Perform dimensional reduction. Exactly what is done here depends on
the values set for the reduction and project.query
parameters. If reduction = "pcaproject", a PCA is performed on
either the reference (if project.query = FALSE) or the query (if
project.query = TRUE), using the features specified. The data
from the other dataset is then projected onto this learned PCA structure.
If reduction = "cca", then CCA is performed on the reference and
query for this dimensional reduction step. If l2.norm is set to
TRUE, perform L2 normalization of the embedding vectors.
Identify anchors between the reference and query - pairs of cells from each dataset that are contained within each other's neighborhoods (also known as mutual nearest neighbors).
Filter low confidence anchors to ensure anchors in the low dimension
space are in broad agreement with the high dimensional measurements. This
is done by looking at the neighbors of each query cell in the reference
dataset using max.features to define this space. If the reference
cell isn't found within the first k.filter neighbors, remove the
anchor.
Assign each remaining anchor a score. For each anchor cell, determine
the nearest k.score anchors within its own dataset and within its
pair's dataset. Based on these neighborhoods, construct an overall neighbor
graph and then compute the shared neighbor overlap between anchor and query
cells (analogous to an SNN graph). We use the 0.01 and 0.90 quantiles on
these scores to dampen outlier effects and rescale to range between 0-1.
Returns an AnchorSet object that can be used as input to
TransferData
Stuart T, Butler A, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177:1888-1902 doi: 10.1016/j.cell.2019.05.031;
## Not run:
# to install the SeuratData package see https://github.com/satijalab/seurat-data
library(SeuratData)
data("pbmc3k")
# for demonstration, split the object into reference and query
pbmc.reference <- pbmc3k[, 1:1350]
pbmc.query <- pbmc3k[, 1351:2700]
# perform standard preprocessing on each object
pbmc.reference <- NormalizeData(pbmc.reference)
pbmc.reference <- FindVariableFeatures(pbmc.reference)
pbmc.reference <- ScaleData(pbmc.reference)
pbmc.query <- NormalizeData(pbmc.query)
pbmc.query <- FindVariableFeatures(pbmc.query)
pbmc.query <- ScaleData(pbmc.query)
# find anchors
anchors <- FindTransferAnchors(reference = pbmc.reference, query = pbmc.query)
# transfer labels
predictions <- TransferData(
anchorset = anchors,
refdata = pbmc.reference$seurat_annotations
)
pbmc.query <- AddMetaData(object = pbmc.query, metadata = predictions)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.