Find a set of anchors between a list of Seurat
objects. These anchors can later be used to integrate the objects using the IntegrateData
function.
FindIntegrationAnchors( object.list = NULL, assay = NULL, reference = NULL, anchor.features = 2000, scale = TRUE, normalization.method = c("LogNormalize", "SCT"), sct.clip.range = NULL, reduction = c("cca", "rpca", "rlsi"), 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, verbose = TRUE )
object.list  A list of 

assay  A vector of assay names specifying which assay to use when constructing anchors. If NULL, the current default assay for each object is used. 
reference  A vector specifying the object/s to be used as a reference during integration. If NULL (default), all pairwise anchors are found (no reference/s). If not NULL, the corresponding objects in 
anchor.features  Can be either:

scale  Whether or not to scale the features provided. Only set to FALSE if you have previously scaled the features you want to use for each object in the object.list 
normalization.method  Name of normalization method used: LogNormalize or SCT 
sct.clip.range  Numeric of length two specifying the min and max values the Pearson residual will be clipped to 
reduction  Dimensional reduction to perform when finding anchors. Can be one of:

l2.norm  Perform L2 normalization on the CCA cell embeddings after dimensional reduction 
dims  Which dimensions to use from the CCA 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 
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 RANN/Annoy) 
verbose  Print progress bars and output 
Returns an AnchorSet
object that can be used as input to IntegrateData
.
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
First, determine anchor.features if not explicitly specified using SelectIntegrationFeatures
. Then for all pairwise combinations of reference and query datasets:
Perform dimensional reduction on the dataset pair as specified via the reduction
parameter. If l2.norm
is set to TRUE
, perform L2 normalization of the embedding vectors.
Identify anchors  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 01.
Stuart T, Butler A, et al. Comprehensive Integration of SingleCell Data. Cell. 2019;177:18881902 doi: 10.1016/j.cell.2019.05.031
if (FALSE) { # to install the SeuratData package see https://github.com/satijalab/seuratdata library(SeuratData) data("panc8") # panc8 is a merged Seurat object containing 8 separate pancreas datasets # split the object by dataset pancreas.list < SplitObject(panc8, split.by = "tech") # perform standard preprocessing on each object for (i in 1:length(pancreas.list)) { pancreas.list[[i]] < NormalizeData(pancreas.list[[i]], verbose = FALSE) pancreas.list[[i]] < FindVariableFeatures( pancreas.list[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE ) } # find anchors anchors < FindIntegrationAnchors(object.list = pancreas.list) # integrate data integrated < IntegrateData(anchorset = anchors) }