This function is a wrapper to switch between alternative factor loading alignment methods that LIGER provides, which is a required step for producing the final integrated result. Two methods are provided (click on options for more details):
method = "quantileNorm"
: Previously published quantile normalization method. (default)method = "centroidAlign"
: Newly developed centroid alignment method.
Arguments
- object
A liger or Seurat object with valid factorization result available (i.e.
runIntegration
performed in advance).- method
Character, method to align factors. Default
"centroidAlign"
. Optionally"quantileNorm"
.- ...
Additional arguments passed to selected methods. For
"quantileNorm"
:quantiles
Number of quantiles to use for quantile normalization. Default
50
.reference
Character, numeric or logical selection of one dataset, out of all available datasets in
object
, to use as a "reference" for quantile normalization. DefaultNULL
tries to find an RNA dataset with the largest number of cells; if no RNA dataset available, use the globally largest dataset.minCells
Minimum number of cells to consider a cluster shared across datasets. Default
20
.nNeighbors
Number of nearest neighbors for within-dataset knn graph. Default
20
.useDims
Indices of factors to use for shared nearest factor determination. Default
NULL
uses all factors.center
Whether to center the data when scaling factors. Could be useful for less sparse modalities like methylation data. Default
FALSE
.maxSample
Maximum number of cells used for quantile normalization of each cluster and factor. Default
1000
.eps
The error bound of the nearest neighbor search. Lower values give more accurate nearest neighbor graphs but take much longer to compute. Default
0.9
.refineKNN
Whether to increase robustness of cluster assignments using KNN graph. Default
TRUE
.clusterName
Variable name that will store the clustering result in metadata of a liger object or a
Seurat
object. Default"quantileNorm_cluster"
.seed
Random seed to allow reproducible results. Default
1
.verbose
Logical. Whether to show information of the progress. Default
getOption("ligerVerbose")
orTRUE
if users have not set.
lambda
Ridge regression penalty applied to each dataset. Can be one number that applies to all datasets, or a numeric vector with length equal to the number of datasets. Default
1
.useDims
Indices of factors to use considered for the alignment. Default
NULL
uses all factors.scaleEmb
Logical, whether to scale the factor loading being considered as the embedding. Default
TRUE
.centerEmb
Logical, whether to center the factor loading being considered as the embedding before scaling it. Default
TRUE
.scaleCluster
Logical, whether to scale the factor loading being considered as the cluster assignment probability. Default
FALSE
.centerCluster
Logical, whether to center the factor loading being considered as the cluster assignment probability before scaling it. Default
FALSE
.shift
Logical, whether to shift the factor loading being considered as the cluster assignment probability after centered scaling. Default
FALSE
.diagnosis
Logical, whether to return cell metadata variables with diagnostic information. Default
FALSE
.